From ec90cc856b10545bb752315c9b34707de554f889 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Tue, 26 Jul 2022 03:38:58 +0800
Subject: [PATCH 01/25] Create README.md

---
 QML4FRAUD/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 QML4FRAUD/README.md

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/QML4FRAUD/README.md
@@ -0,0 +1 @@
+

From f247c66a25c77cd54bfd071edd94a87dc042cfa9 Mon Sep 17 00:00:00 2001
From: Javier Mancilla <63371285+maximer-v@users.noreply.github.com>
Date: Mon, 25 Jul 2022 23:01:30 +0200
Subject: [PATCH 02/25] First version of PCA and LDA variational Pennylane

---
 QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb      |  1448 ++
 QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb      |  1104 +
 QML4FRAUD/fraud_detection_bank_dataset.csv | 20469 +++++++++++++++++++
 3 files changed, 23021 insertions(+)
 create mode 100644 QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb
 create mode 100644 QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb
 create mode 100644 QML4FRAUD/fraud_detection_bank_dataset.csv

diff --git a/QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb b/QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb
new file mode 100644
index 0000000..c75bdba
--- /dev/null
+++ b/QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb
@@ -0,0 +1,1448 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "from pennylane_qiskit import IBMQDevice\n",
+    "from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "\n",
+    "import time\n",
+    "start = time.time()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read out CSV and sets/samples creation\n",
+    "\n",
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
+    "df_sample = df.sample(2000)\n",
+    "train,test = train_test_split(df_sample, test_size=0.30, random_state=10)\n",
+    "train_set = train\n",
+    "test_set = test\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 10214 to 13822\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Review the information related to the dataframe\n",
+    "\n",
+    "df_sample.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.288000</td>\n",
+       "      <td>292.928000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>2.379000</td>\n",
+       "      <td>0.085500</td>\n",
+       "      <td>0.974500</td>\n",
+       "      <td>2.290000</td>\n",
+       "      <td>3.288000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.36600</td>\n",
+       "      <td>0.00150</td>\n",
+       "      <td>0.307000</td>\n",
+       "      <td>0.197000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.046500</td>\n",
+       "      <td>0.033000</td>\n",
+       "      <td>45.092500</td>\n",
+       "      <td>0.277500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.770584</td>\n",
+       "      <td>722.829477</td>\n",
+       "      <td>2.342166</td>\n",
+       "      <td>8.138641</td>\n",
+       "      <td>0.741933</td>\n",
+       "      <td>3.368757</td>\n",
+       "      <td>2.997564</td>\n",
+       "      <td>14.770584</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.48183</td>\n",
+       "      <td>0.03871</td>\n",
+       "      <td>0.461365</td>\n",
+       "      <td>0.397832</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.210618</td>\n",
+       "      <td>0.382079</td>\n",
+       "      <td>59.864654</td>\n",
+       "      <td>0.447877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.750000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>270.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>368.000000</td>\n",
+       "      <td>19687.000000</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>183.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>78.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>368.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>502.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      3.288000    292.928000     0.300000     2.379000     0.085500   \n",
+       "std      14.770584    722.829477     2.342166     8.138641     0.741933   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     37.750000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     93.000000     0.000000     1.000000     0.000000   \n",
+       "75%       1.000000    270.500000     0.000000     2.000000     0.000000   \n",
+       "max     368.000000  19687.000000    86.000000   183.000000    26.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.974500     2.290000     3.288000     0.0     0.0  ...   \n",
+       "std       3.368757     2.997564    14.770584     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     1.000000     0.0     0.0  ...   \n",
+       "max      78.000000    11.000000   368.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103     col_104     col_105      col_106      col_107  col_108  \\\n",
+       "count  2000.000000  2000.00000  2000.00000  2000.000000  2000.000000   2000.0   \n",
+       "mean      0.004000     0.36600     0.00150     0.307000     0.197000      0.0   \n",
+       "std       0.063135     0.48183     0.03871     0.461365     0.397832      0.0   \n",
+       "min       0.000000     0.00000     0.00000     0.000000     0.000000      0.0   \n",
+       "25%       0.000000     0.00000     0.00000     0.000000     0.000000      0.0   \n",
+       "50%       0.000000     0.00000     0.00000     0.000000     0.000000      0.0   \n",
+       "75%       0.000000     1.00000     0.00000     1.000000     0.000000      0.0   \n",
+       "max       1.000000     1.00000     1.00000     1.000000     1.000000      0.0   \n",
+       "\n",
+       "           col_109      col_110      col_111      targets  \n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean      0.046500     0.033000    45.092500     0.277500  \n",
+       "std       0.210618     0.382079    59.864654     0.447877  \n",
+       "min       0.000000     0.000000     0.000000     0.000000  \n",
+       "25%       0.000000     0.000000     5.000000     0.000000  \n",
+       "50%       0.000000     0.000000    20.000000     0.000000  \n",
+       "75%       0.000000     0.000000    62.000000     1.000000  \n",
+       "max       1.000000    10.000000   502.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Table of the description of the dataframe related to fixed parameters\n",
+    "\n",
+    "df_sample.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separation of labels\n",
+    "\n",
+    "x_train = train_set\n",
+    "y_train = train_set[['targets']]\n",
+    "\n",
+    "x_test = test_set\n",
+    "y_test = test_set[['targets']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_a = x_train.iloc[:,:55]\n",
+    "features_b = x_train.iloc[:,55:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_a_test = x_test.iloc[:,:55]\n",
+    "features_b_test = x_test.iloc[:,55:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lda = LDA(n_components=1)\n",
+    "features_lda_1 = lda.fit_transform(features_a, y_train)\n",
+    "features_lda_2 = lda.fit_transform(features_b, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_lda_1_test = lda.fit_transform(features_a_test, y_test)\n",
+    "features_lda_2_test = lda.fit_transform(features_b_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_lda_1 = pd.DataFrame(features_lda_1)\n",
+    "features_lda_2 = pd.DataFrame(features_lda_2)\n",
+    "features_lda_1_test = pd.DataFrame(features_lda_1_test)\n",
+    "features_lda_2_test = pd.DataFrame(features_lda_2_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_lda = features_lda_1.join(features_lda_2, lsuffix=\"_left\", rsuffix=\"_right\")\n",
+    "x_test_lda = features_lda_1_test.join(features_lda_2_test, lsuffix=\"_left\", rsuffix=\"_right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_left</th>\n",
+       "      <th>0_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.477842</td>\n",
+       "      <td>-0.286121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.876101</td>\n",
+       "      <td>-0.603912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.039679</td>\n",
+       "      <td>-0.840358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.789524</td>\n",
+       "      <td>3.625557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.827172</td>\n",
+       "      <td>-1.663958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>2.047377</td>\n",
+       "      <td>-0.173438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>-0.684520</td>\n",
+       "      <td>-1.561943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1397</th>\n",
+       "      <td>-0.367997</td>\n",
+       "      <td>0.290284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1398</th>\n",
+       "      <td>-0.945484</td>\n",
+       "      <td>-0.603912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1399</th>\n",
+       "      <td>0.687198</td>\n",
+       "      <td>-0.865416</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1400 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        0_left   0_right\n",
+       "0     0.477842 -0.286121\n",
+       "1    -0.876101 -0.603912\n",
+       "2    -1.039679 -0.840358\n",
+       "3     1.789524  3.625557\n",
+       "4    -0.827172 -1.663958\n",
+       "...        ...       ...\n",
+       "1395  2.047377 -0.173438\n",
+       "1396 -0.684520 -1.561943\n",
+       "1397 -0.367997  0.290284\n",
+       "1398 -0.945484 -0.603912\n",
+       "1399  0.687198 -0.865416\n",
+       "\n",
+       "[1400 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_left</th>\n",
+       "      <th>0_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.549145</td>\n",
+       "      <td>-1.277229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.386657</td>\n",
+       "      <td>0.107849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.833744</td>\n",
+       "      <td>0.011079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.091864</td>\n",
+       "      <td>-1.277316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.353161</td>\n",
+       "      <td>0.107849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>0.895299</td>\n",
+       "      <td>0.102784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-1.027923</td>\n",
+       "      <td>-0.286958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.057668</td>\n",
+       "      <td>-0.806670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>-1.679386</td>\n",
+       "      <td>-0.714452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>0.423589</td>\n",
+       "      <td>0.107849</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0_left   0_right\n",
+       "0   -0.549145 -1.277229\n",
+       "1   -1.386657  0.107849\n",
+       "2    0.833744  0.011079\n",
+       "3   -0.091864 -1.277316\n",
+       "4   -1.353161  0.107849\n",
+       "..        ...       ...\n",
+       "595  0.895299  0.102784\n",
+       "596 -1.027923 -0.286958\n",
+       "597 -1.057668 -0.806670\n",
+       "598 -1.679386 -0.714452\n",
+       "599  0.423589  0.107849\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_train_lda)\n",
+    "data = std_scale.transform(x_train_lda)\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_test_lda)\n",
+    "x_test_lda_n = std_scale.transform(x_test_lda)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.49520369, -0.94852857],\n",
+       "       [-1.25044871,  0.08009347],\n",
+       "       [ 0.75184743,  0.00822801],\n",
+       "       ...,\n",
+       "       [-0.9537753 , -0.59907011],\n",
+       "       [-1.51442363, -0.53058467],\n",
+       "       [ 0.38198046,  0.08009347]])"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_dim = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        72.714286\n",
+       "1.0        27.285714\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in train\n",
+    "\n",
+    "y_train.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        71.166667\n",
+       "1.0        28.833333\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in test\n",
+    "\n",
+    "y_test.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Angle Encoding\n",
+    "\n",
+    "num_qubits = n_dim\n",
+    "\n",
+    "dev = qml.device('default.qubit', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "#dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def circuit(parameters, data):\n",
+    "    for i in range(num_qubits):\n",
+    "        qml.Hadamard(wires = i)\n",
+    "    \n",
+    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "    \n",
+    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "    \n",
+    "    return qml.expval(qml.PauliZ(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
+      "  [ 0.0152303  -0.00234153 -0.00234137]]\n",
+      "\n",
+      " [[ 0.01579213  0.00767435 -0.00469474]\n",
+      "  [ 0.0054256  -0.00463418 -0.0046573 ]]\n",
+      "\n",
+      " [[ 0.00241962 -0.0191328  -0.01724918]\n",
+      "  [-0.00562288 -0.01012831  0.00314247]]\n",
+      "\n",
+      " [[-0.00908024 -0.01412304  0.01465649]\n",
+      "  [-0.00225776  0.00067528 -0.01424748]]\n",
+      "\n",
+      " [[-0.00544383  0.00110923 -0.01150994]\n",
+      "  [ 0.00375698 -0.00600639 -0.00291694]]] 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_layers = 5\n",
+    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
+    "bias_init = np.array(0.0, requires_grad=True)\n",
+    "\n",
+    "print(weights_init, bias_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-0.06445312, requires_grad=True)"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit(weights_init, data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def variational_classifier(weights, bias, x):\n",
+    "    return circuit(weights, x) + bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def square_loss(labels, predictions):\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        loss = loss + (l - p) ** 2\n",
+    "\n",
+    "    loss = loss / len(labels)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(labels, predictions):\n",
+    "\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        if abs(l - p) < 1e-5:\n",
+    "            loss = loss + 1\n",
+    "    loss = loss / len(labels)\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost(weights, bias, X, Y):\n",
+    "    predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "    return square_loss(Y, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X = [tensor(0.43425169, requires_grad=False), tensor(-0.21131303, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.79617925, requires_grad=False), tensor(-0.44601562, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.94483513, requires_grad=False), tensor(-0.62064063, requires_grad=False)], Y = -1\n",
+      "X = [tensor(1.62627636, requires_grad=False), tensor(2.67763153, requires_grad=False)], Y =  1\n",
+      "X = [tensor(-0.75171383, requires_grad=False), tensor(-1.22890557, requires_grad=False)], Y = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "X = np.array(data, requires_grad=False)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
+    "batch_size = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7766051 | Accuracy: 0.7250000 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.6020277 | Accuracy: 0.8000000 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.5430088 | Accuracy: 0.8200000 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.6133099 | Accuracy: 0.8242857 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.5682896 | Accuracy: 0.8328571 \n",
+      "New best\n",
+      "Iter:     6 | Cost: 0.5139809 | Accuracy: 0.8385714 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.4862393 | Accuracy: 0.8457143 \n",
+      "New best\n",
+      "Iter:     8 | Cost: 0.4849819 | Accuracy: 0.8485714 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.4730128 | Accuracy: 0.8492857 \n",
+      "New best\n",
+      "Iter:    10 | Cost: 0.4595886 | Accuracy: 0.8535714 \n",
+      "Iter:    11 | Cost: 0.4523207 | Accuracy: 0.8478571 \n",
+      "Iter:    12 | Cost: 0.4455806 | Accuracy: 0.8507143 \n",
+      "Iter:    13 | Cost: 0.4476530 | Accuracy: 0.8528571 \n",
+      "New best\n",
+      "Iter:    14 | Cost: 0.4501227 | Accuracy: 0.8550000 \n",
+      "New best\n",
+      "Iter:    15 | Cost: 0.4498906 | Accuracy: 0.8571429 \n",
+      "Iter:    16 | Cost: 0.4526023 | Accuracy: 0.8571429 \n",
+      "Iter:    17 | Cost: 0.4800448 | Accuracy: 0.8564286 \n",
+      "Iter:    18 | Cost: 0.4578922 | Accuracy: 0.8521429 \n",
+      "Iter:    19 | Cost: 0.4361061 | Accuracy: 0.8542857 \n",
+      "Iter:    20 | Cost: 0.4270024 | Accuracy: 0.8564286 \n",
+      "Iter:    21 | Cost: 0.4299489 | Accuracy: 0.8528571 \n",
+      "Iter:    22 | Cost: 0.4461888 | Accuracy: 0.8485714 \n",
+      "Iter:    23 | Cost: 0.4546085 | Accuracy: 0.8485714 \n",
+      "Iter:    24 | Cost: 0.4470947 | Accuracy: 0.8500000 \n",
+      "Iter:    25 | Cost: 0.4406860 | Accuracy: 0.8500000 \n",
+      "Iter:    26 | Cost: 0.4348689 | Accuracy: 0.8485714 \n",
+      "Iter:    27 | Cost: 0.4416649 | Accuracy: 0.8485714 \n",
+      "Iter:    28 | Cost: 0.4581730 | Accuracy: 0.8478571 \n",
+      "Iter:    29 | Cost: 0.4735164 | Accuracy: 0.8485714 \n",
+      "Iter:    30 | Cost: 0.4735897 | Accuracy: 0.8478571 \n",
+      "Iter:    31 | Cost: 0.4453789 | Accuracy: 0.8500000 \n",
+      "Iter:    32 | Cost: 0.4232235 | Accuracy: 0.8542857 \n",
+      "New best\n",
+      "Iter:    33 | Cost: 0.4522567 | Accuracy: 0.8628571 \n",
+      "Iter:    34 | Cost: 0.6122500 | Accuracy: 0.8385714 \n",
+      "Iter:    35 | Cost: 0.7930322 | Accuracy: 0.7078571 \n",
+      "Iter:    36 | Cost: 0.6229256 | Accuracy: 0.8214286 \n",
+      "New best\n",
+      "Iter:    37 | Cost: 0.5148708 | Accuracy: 0.8671429 \n",
+      "Iter:    38 | Cost: 0.4319905 | Accuracy: 0.8650000 \n",
+      "Iter:    39 | Cost: 0.4170537 | Accuracy: 0.8657143 \n",
+      "Iter:    40 | Cost: 0.4161079 | Accuracy: 0.8600000 \n",
+      "Iter:    41 | Cost: 0.4340515 | Accuracy: 0.8635714 \n",
+      "Iter:    42 | Cost: 0.4476147 | Accuracy: 0.8657143 \n",
+      "Iter:    43 | Cost: 0.4551047 | Accuracy: 0.8664286 \n",
+      "Iter:    44 | Cost: 0.4369361 | Accuracy: 0.8600000 \n",
+      "Iter:    45 | Cost: 0.4175058 | Accuracy: 0.8542857 \n",
+      "Iter:    46 | Cost: 0.4198623 | Accuracy: 0.8507143 \n",
+      "Iter:    47 | Cost: 0.4327565 | Accuracy: 0.8528571 \n",
+      "Iter:    48 | Cost: 0.4348296 | Accuracy: 0.8500000 \n",
+      "Iter:    49 | Cost: 0.4199491 | Accuracy: 0.8535714 \n",
+      "Iter:    50 | Cost: 0.4101626 | Accuracy: 0.8557143 \n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = weights_init\n",
+    "bias = bias_init\n",
+    "\n",
+    "wbest = 0\n",
+    "bbest = 0\n",
+    "abest = 0\n",
+    "\n",
+    "for it in range(50):\n",
+    "\n",
+    "    # weights update by one optimizer step\n",
+    "\n",
+    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "    X_batch = X[batch_index]\n",
+    "    Y_batch = Y[batch_index]\n",
+    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "    # Compute the accuracy\n",
+    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "    \n",
+    "    if accuracy(Y, predictions) > abest:\n",
+    "        wbest = weights\n",
+    "        bbest = bias\n",
+    "        abest = accuracy(Y, predictions)\n",
+    "        print('New best')\n",
+    "\n",
+    "    acc = accuracy(Y, predictions)\n",
+    "\n",
+    "    print(\n",
+    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "            it + 1, cost(weights, bias, X, Y), acc\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
+    "Xte = np.array(normalize(x_test_lda_n), requires_grad=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost: 0.5347601293965113, Accuracy: 83.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
+    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
+    "acc = accuracy(Yte, predictions)\n",
+    "\n",
+    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions  Test\n",
+       "0          -1.0  -1.0\n",
+       "1          -1.0  -1.0\n",
+       "2           1.0  -1.0\n",
+       "3          -1.0  -1.0\n",
+       "4          -1.0  -1.0\n",
+       "..          ...   ...\n",
+       "595         1.0  -1.0\n",
+       "596        -1.0  -1.0\n",
+       "597        -1.0  -1.0\n",
+       "598        -1.0  -1.0\n",
+       "599         1.0  -1.0\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.79      0.96      0.87       352\n",
+      "         1.0       0.92      0.65      0.76       248\n",
+      "\n",
+      "    accuracy                           0.83       600\n",
+      "   macro avg       0.86      0.80      0.82       600\n",
+      "weighted avg       0.85      0.83      0.82       600\n",
+      "\n",
+      "0.9248554913294798\n",
+      "0.6451612903225806\n",
+      "0.7600950118764846\n",
+      "0.8041147360703812\n",
+      "[[339  13]\n",
+      " [ 88 160]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the classification report and important metrics\n",
+    "\n",
+    "print(metrics.classification_report(predictions,Yte))\n",
+    "print(metrics.precision_score(predictions,Yte))\n",
+    "print(metrics.recall_score(predictions,Yte))\n",
+    "print(metrics.f1_score(predictions,Yte))\n",
+    "print(metrics.balanced_accuracy_score(predictions,Yte))\n",
+    "print(metrics.confusion_matrix(predictions,Yte))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Classical ML approach\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import model_selection\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import datasets\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = []\n",
+    "models.append(('LR', LogisticRegression()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('CART', DecisionTreeClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('SVM', SVC()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: 0.785000 (0.036485)\n",
+      "KNN: 0.817857 (0.017276)\n",
+      "CART: 1.000000 (0.000000)\n",
+      "NB: 0.406429 (0.032016)\n",
+      "SVM: 0.727143 (0.021381)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classic ML results\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'accuracy'\n",
+    "for name, model in models:\n",
+    "    kfold = model_selection.KFold(n_splits=10)\n",
+    "    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: 0.725856 (0.054155)\n",
+      "KNN: 0.748267 (0.027442)\n",
+      "CART: 1.000000 (0.000000)\n",
+      "NB: 0.574042 (0.019213)\n",
+      "SVM: 0.500000 (0.000000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classic ML results\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'balanced_accuracy'\n",
+    "for name, model in models:\n",
+    "    kfold = model_selection.KFold(n_splits=10)\n",
+    "    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: 0.596823 (0.120502)\n",
+      "KNN: 0.595380 (0.056029)\n",
+      "CART: 1.000000 (0.000000)\n",
+      "NB: 0.942471 (0.036170)\n",
+      "SVM: 0.000000 (0.000000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classic ML results\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'recall'\n",
+    "for name, model in models:\n",
+    "    kfold = model_selection.KFold(n_splits=10)\n",
+    "    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: 0.613604 (0.094372)\n",
+      "KNN: 0.695107 (0.076148)\n",
+      "CART: 1.000000 (0.000000)\n",
+      "NB: 0.308200 (0.027662)\n",
+      "SVM: 0.000000 (0.000000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classic ML results\n",
+    "\n",
+    "results = []\n",
+    "names = []\n",
+    "scoring = 'precision'\n",
+    "for name, model in models:\n",
+    "    kfold = model_selection.KFold(n_splits=10)\n",
+    "    cv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "09a2343d96e4209a5eb8971e1e9a0248c4f95d560a06ad86d050590524a525f8"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('qkt-tookit')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb b/QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb
new file mode 100644
index 0000000..d733849
--- /dev/null
+++ b/QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb
@@ -0,0 +1,1104 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "from pennylane_qiskit import IBMQDevice\n",
+    "from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "\n",
+    "import time\n",
+    "start = time.time()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read out CSV and sets/samples creation\n",
+    "\n",
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
+    "df_sample = df.sample(2000)\n",
+    "train,test = train_test_split(df_sample, test_size=0.30, random_state=10)\n",
+    "train_set = train\n",
+    "test_set = test\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 1989 to 15622\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Review the information related to the dataframe\n",
+    "\n",
+    "df_sample.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.363500</td>\n",
+       "      <td>319.388500</td>\n",
+       "      <td>0.447000</td>\n",
+       "      <td>2.431500</td>\n",
+       "      <td>0.098500</td>\n",
+       "      <td>1.058000</td>\n",
+       "      <td>2.415000</td>\n",
+       "      <td>3.363500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.007000</td>\n",
+       "      <td>0.354000</td>\n",
+       "      <td>0.003500</td>\n",
+       "      <td>0.328500</td>\n",
+       "      <td>0.207000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.054500</td>\n",
+       "      <td>0.033000</td>\n",
+       "      <td>45.240000</td>\n",
+       "      <td>0.257500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13.707491</td>\n",
+       "      <td>697.144656</td>\n",
+       "      <td>5.222818</td>\n",
+       "      <td>11.643285</td>\n",
+       "      <td>1.393115</td>\n",
+       "      <td>4.840697</td>\n",
+       "      <td>2.983325</td>\n",
+       "      <td>13.707491</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.089191</td>\n",
+       "      <td>0.478329</td>\n",
+       "      <td>0.059072</td>\n",
+       "      <td>0.469785</td>\n",
+       "      <td>0.405257</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.227058</td>\n",
+       "      <td>0.354928</td>\n",
+       "      <td>64.505317</td>\n",
+       "      <td>0.437366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>302.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>342.000000</td>\n",
+       "      <td>12179.000000</td>\n",
+       "      <td>194.000000</td>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>161.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>342.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      3.363500    319.388500     0.447000     2.431500     0.098500   \n",
+       "std      13.707491    697.144656     5.222818    11.643285     1.393115   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     39.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000    105.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    302.000000     0.000000     2.000000     0.000000   \n",
+       "max     342.000000  12179.000000   194.000000   360.000000    54.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      1.058000     2.415000     3.363500     0.0     0.0  ...   \n",
+       "std       4.840697     2.983325    13.707491     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     161.000000     8.000000   342.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.007000     0.354000     0.003500     0.328500     0.207000   \n",
+       "std       0.089191     0.478329     0.059072     0.469785     0.405257   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.054500     0.033000    45.240000     0.257500  \n",
+       "std        0.0     0.227058     0.354928    64.505317     0.437366  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.000000    11.000000   747.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Table of the description of the dataframe related to fixed parameters\n",
+    "\n",
+    "df_sample.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1400, 113)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 113)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set = train_set.astype(float)\n",
+    "test_set = test_set.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = train_set\n",
+    "y_train = train_set[['targets']]\n",
+    "x_test = test_set\n",
+    "y_test = test_set[['targets']]\n",
+    "\n",
+    "x_train.drop(['targets'],axis = 1,inplace = True)\n",
+    "x_test.drop(['targets'],axis = 1,inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        74.142857\n",
+       "1.0        25.857143\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Train classes distribution\n",
+    "y_train.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        74.5\n",
+       "1.0        25.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Testing classes distribution\n",
+    "y_test.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Shape: (1400, 112)\n",
+      "Test Shape : (600, 112)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Train and test shape\n",
+    "print(\"Train Shape: {}\\nTest Shape : {}\".format(x_train.shape, x_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Dimension definition\n",
+    "n_dim = 2\n",
+    "\n",
+    "#PCA application for dimensionality reduction\n",
+    "pca = PCA(n_components=n_dim, svd_solver='full')\n",
+    "pca.fit(x_train)\n",
+    "x_train_pca = pca.transform(x_train)\n",
+    "pca.fit(x_test)\n",
+    "x_test = pca.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.99997061  0.00766626]\n",
+      " [-0.99995283 -0.00971271]\n",
+      " [-0.99994675 -0.01032004]\n",
+      " ...\n",
+      " [-0.99744184  0.07148264]\n",
+      " [-0.99997117 -0.00759275]\n",
+      " [ 0.99997905 -0.0064727 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Normalization of train\n",
+    "data = normalize(x_train_pca)\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.999983   -0.00583141]\n",
+      " [-0.99998912 -0.00466432]\n",
+      " [ 0.99998742 -0.00501584]\n",
+      " ...\n",
+      " [-0.99999334 -0.00364874]\n",
+      " [-0.99999382 -0.00351611]\n",
+      " [-0.99999344 -0.00362247]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Normalization of test\n",
+    "x_test = normalize(x_test)\n",
+    "print(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Angle Encoding\n",
+    "\n",
+    "num_qubits = data.shape[1]\n",
+    "\n",
+    "dev = qml.device('default.qubit', wires = num_qubits, shots = 256)\n",
+    "#dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', provider=provider)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def circuit(parameters, data):\n",
+    "    # Apply Hadamards to all qubits in the circuit\n",
+    "    for i in range(num_qubits):\n",
+    "        qml.Hadamard(wires = i)\n",
+    "    \n",
+    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "    \n",
+    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "    \n",
+    "    return qml.expval(qml.PauliZ(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
+      "  [ 0.0152303  -0.00234153 -0.00234137]]\n",
+      "\n",
+      " [[ 0.01579213  0.00767435 -0.00469474]\n",
+      "  [ 0.0054256  -0.00463418 -0.0046573 ]]\n",
+      "\n",
+      " [[ 0.00241962 -0.0191328  -0.01724918]\n",
+      "  [-0.00562288 -0.01012831  0.00314247]]\n",
+      "\n",
+      " [[-0.00908024 -0.01412304  0.01465649]\n",
+      "  [-0.00225776  0.00067528 -0.01424748]]\n",
+      "\n",
+      " [[-0.00544383  0.00110923 -0.01150994]\n",
+      "  [ 0.00375698 -0.00600639 -0.00291694]]] 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_layers = 5\n",
+    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
+    "bias_init = np.array(0.0, requires_grad=True)\n",
+    "\n",
+    "print(weights_init, bias_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-0.03125, requires_grad=True)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit(weights_init, data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def variational_classifier(weights, bias, x):\n",
+    "    return circuit(weights, x) + bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def square_loss(labels, predictions):\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        loss = loss + (l - p) ** 2\n",
+    "\n",
+    "    loss = loss / len(labels)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(labels, predictions):\n",
+    "\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        if abs(l - p) < 1e-5:\n",
+    "            loss = loss + 1\n",
+    "    loss = loss / len(labels)\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost(weights, bias, X, Y):\n",
+    "    predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "    return square_loss(Y, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X = [tensor(-0.99997061, requires_grad=False), tensor(0.00766626, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.99995283, requires_grad=False), tensor(-0.00971271, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.99994675, requires_grad=False), tensor(-0.01032004, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.99467012, requires_grad=False), tensor(-0.10310845, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.99995653, requires_grad=False), tensor(-0.00932417, requires_grad=False)], Y = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "X = np.array(data, requires_grad=False)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
+    "#opt = AdagradOptimizer(stepsize=0.01, eps=1e-08)\n",
+    "#opt = GradientDescentOptimizer(stepsize=0.01)\n",
+    "#opt = RMSPropOptimizer(stepsize=0.01, decay=0.9, eps=1e-08)\n",
+    "#opt = NesterovMomentumOptimizer(stepsize=0.01, momentum=0.9)\n",
+    "batch_size = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.8142133 | Accuracy: 0.7371429 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.7492751 | Accuracy: 0.7414286 \n",
+      "Iter:     3 | Cost: 0.7481851 | Accuracy: 0.7414286 \n",
+      "Iter:     4 | Cost: 0.8636719 | Accuracy: 0.6300000 \n",
+      "Iter:     5 | Cost: 0.8025046 | Accuracy: 0.7292857 \n",
+      "Iter:     6 | Cost: 0.7605707 | Accuracy: 0.7414286 \n",
+      "Iter:     7 | Cost: 0.7525719 | Accuracy: 0.7414286 \n",
+      "Iter:     8 | Cost: 0.7460470 | Accuracy: 0.7414286 \n",
+      "Iter:     9 | Cost: 0.7504315 | Accuracy: 0.7414286 \n",
+      "Iter:    10 | Cost: 0.7668446 | Accuracy: 0.7414286 \n",
+      "Iter:    11 | Cost: 0.7903816 | Accuracy: 0.7414286 \n",
+      "Iter:    12 | Cost: 0.7583754 | Accuracy: 0.7414286 \n",
+      "Iter:    13 | Cost: 0.7435417 | Accuracy: 0.7414286 \n",
+      "Iter:    14 | Cost: 0.8243844 | Accuracy: 0.7314286 \n",
+      "Iter:    15 | Cost: 0.9313670 | Accuracy: 0.4850000 \n",
+      "Iter:    16 | Cost: 0.9178925 | Accuracy: 0.4914286 \n",
+      "Iter:    17 | Cost: 0.8572023 | Accuracy: 0.7028571 \n",
+      "Iter:    18 | Cost: 0.7702813 | Accuracy: 0.7414286 \n",
+      "Iter:    19 | Cost: 0.7740805 | Accuracy: 0.7414286 \n",
+      "Iter:    20 | Cost: 0.9743450 | Accuracy: 0.7414286 \n",
+      "Iter:    21 | Cost: 1.0429149 | Accuracy: 0.7414286 \n",
+      "Iter:    22 | Cost: 1.0326060 | Accuracy: 0.7414286 \n",
+      "Iter:    23 | Cost: 0.8991914 | Accuracy: 0.7414286 \n",
+      "Iter:    24 | Cost: 0.7709373 | Accuracy: 0.7414286 \n",
+      "Iter:    25 | Cost: 0.7350728 | Accuracy: 0.7414286 \n",
+      "Iter:    26 | Cost: 0.8537272 | Accuracy: 0.6492857 \n",
+      "Iter:    27 | Cost: 0.8607656 | Accuracy: 0.6292857 \n",
+      "Iter:    28 | Cost: 0.7637338 | Accuracy: 0.7414286 \n",
+      "Iter:    29 | Cost: 0.7567716 | Accuracy: 0.7414286 \n",
+      "Iter:    30 | Cost: 0.8052604 | Accuracy: 0.7414286 \n",
+      "Iter:    31 | Cost: 0.8293959 | Accuracy: 0.7414286 \n",
+      "Iter:    32 | Cost: 0.8172791 | Accuracy: 0.7414286 \n",
+      "Iter:    33 | Cost: 0.7971415 | Accuracy: 0.7414286 \n",
+      "Iter:    34 | Cost: 0.7733342 | Accuracy: 0.7414286 \n",
+      "Iter:    35 | Cost: 0.7615322 | Accuracy: 0.7414286 \n",
+      "Iter:    36 | Cost: 0.7589089 | Accuracy: 0.7414286 \n",
+      "Iter:    37 | Cost: 0.7902360 | Accuracy: 0.7414286 \n",
+      "Iter:    38 | Cost: 0.7981693 | Accuracy: 0.7414286 \n",
+      "Iter:    39 | Cost: 0.8453596 | Accuracy: 0.7414286 \n",
+      "Iter:    40 | Cost: 0.8964028 | Accuracy: 0.7414286 \n",
+      "Iter:    41 | Cost: 0.9092458 | Accuracy: 0.7414286 \n",
+      "Iter:    42 | Cost: 0.7900895 | Accuracy: 0.7414286 \n",
+      "Iter:    43 | Cost: 0.7414881 | Accuracy: 0.7414286 \n",
+      "Iter:    44 | Cost: 0.7478766 | Accuracy: 0.7414286 \n",
+      "Iter:    45 | Cost: 0.7826977 | Accuracy: 0.7414286 \n",
+      "Iter:    46 | Cost: 0.7973897 | Accuracy: 0.7350000 \n",
+      "Iter:    47 | Cost: 0.8330424 | Accuracy: 0.6828571 \n",
+      "Iter:    48 | Cost: 0.8554368 | Accuracy: 0.6378571 \n",
+      "Iter:    49 | Cost: 0.8663994 | Accuracy: 0.5985714 \n",
+      "Iter:    50 | Cost: 0.8770845 | Accuracy: 0.5657143 \n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = weights_init\n",
+    "bias = bias_init\n",
+    "\n",
+    "wbest = 0\n",
+    "bbest = 0\n",
+    "abest = 0\n",
+    "\n",
+    "for it in range(50):\n",
+    "\n",
+    "    # Update the weights by one optimizer step\n",
+    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "    X_batch = X[batch_index]\n",
+    "    Y_batch = Y[batch_index]\n",
+    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "    # Compute accuracy\n",
+    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "    \n",
+    "    if accuracy(Y, predictions) > abest:\n",
+    "        wbest = weights\n",
+    "        bbest = bias\n",
+    "        abest = accuracy(Y, predictions)\n",
+    "        print('New best')\n",
+    "\n",
+    "    acc = accuracy(Y, predictions)\n",
+    "\n",
+    "    print(\n",
+    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "            it + 1, cost(weights, bias, X, Y), acc\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
+    "Xte = np.array(normalize(x_test), requires_grad=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost: 0.7525858753099877, Accuracy: 74.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
+    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
+    "acc = accuracy(Yte, predictions)\n",
+    "\n",
+    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions  Test\n",
+       "0          -1.0  -1.0\n",
+       "1          -1.0  -1.0\n",
+       "2          -1.0   1.0\n",
+       "3          -1.0  -1.0\n",
+       "4          -1.0  -1.0\n",
+       "..          ...   ...\n",
+       "595        -1.0   1.0\n",
+       "596        -1.0  -1.0\n",
+       "597        -1.0  -1.0\n",
+       "598        -1.0   1.0\n",
+       "599        -1.0  -1.0\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution time: 32m-11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "totaltime = end - start\n",
+    "\n",
+    "mins = int(np.round(totaltime % 60))\n",
+    "secs = int(np.round((totaltime % 60 - mins) * 60))\n",
+    "\n",
+    "print(f'Execution time: {mins}m{secs}s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       1.00      0.74      0.85       600\n",
+      "         1.0       0.00      0.00      0.00         0\n",
+      "\n",
+      "    accuracy                           0.74       600\n",
+      "   macro avg       0.50      0.37      0.43       600\n",
+      "weighted avg       1.00      0.74      0.85       600\n",
+      "\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.745\n",
+      "[[447 153]\n",
+      " [  0   0]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "c:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "c:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "c:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "c:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1987: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the classification report and important metrics\n",
+    "\n",
+    "print(metrics.classification_report(predictions,Yte))\n",
+    "print(metrics.precision_score(predictions,Yte))\n",
+    "print(metrics.recall_score(predictions,Yte))\n",
+    "print(metrics.f1_score(predictions,Yte))\n",
+    "print(metrics.balanced_accuracy_score(predictions,Yte))\n",
+    "print(metrics.confusion_matrix(predictions,Yte))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "263930470851f494f0ed2879c35b57985588df20f9e529b86e97dd5eb9ddc466"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/QML4FRAUD/fraud_detection_bank_dataset.csv b/QML4FRAUD/fraud_detection_bank_dataset.csv
new file mode 100644
index 0000000..a076dc7
--- /dev/null
+++ b/QML4FRAUD/fraud_detection_bank_dataset.csv
@@ -0,0 +1,20469 @@
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From 30462d47d6f9f7840a6be821ae46b7b8de8e6645 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Tue, 26 Jul 2022 17:46:46 +0800
Subject: [PATCH 03/25] Add - Architecture notebook, half-finished

---
 .../Architecture-checkpoint.ipynb             | 574 ++++++++++++++++++
 QML4FRAUD/Architecture.ipynb                  | 574 ++++++++++++++++++
 2 files changed, 1148 insertions(+)
 create mode 100644 QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
 create mode 100644 QML4FRAUD/Architecture.ipynb

diff --git a/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb b/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
new file mode 100644
index 0000000..3f95b7d
--- /dev/null
+++ b/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9190faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "#from pennylane_qiskit import IBMQDevice\n",
+    "#from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c2c45f75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PrepareData:\n",
+    "    def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):\n",
+    "        self.data = data\n",
+    "        self.preprocess_done = None\n",
+    "        \n",
+    "        if sample_size == 0:\n",
+    "            self.data_sample = data\n",
+    "        else:\n",
+    "            self.data_sample = data.sample(sample_size)\n",
+    "            \n",
+    "        self.train_set, self.test_set = train_test_split(self.data_sample, test_size=test_split, random_state=seed)\n",
+    "        \n",
+    "        self.y_train = self.train_set[[target]]\n",
+    "        self.y_test = self.test_set[[target]]\n",
+    "        \n",
+    "        self.x_train = self.train_set.drop(target, axis=1)\n",
+    "        self.x_test = self.test_set.drop(target, axis=1)\n",
+    "        \n",
+    "    def view_info(self):\n",
+    "        print(self.data_sample.info())\n",
+    "        if self.preprocess_done == None:\n",
+    "            print(\"No preprocessing done yet.\")\n",
+    "        else:\n",
+    "            print(\"Preprocessing done via: \", self.preprocess_done)\n",
+    "        return self.data_sample.describe()\n",
+    "    \n",
+    "    def perform_LDA(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"LDA\"\n",
+    "        \n",
+    "        length = len(self.x_train.columns)\n",
+    "        split_feature = int(length/n_dim)\n",
+    "        features_train = []\n",
+    "        features_test = []\n",
+    "        \n",
+    "        for i in range(n_dim):\n",
+    "            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
+    "            features_train.append(new_set_train)\n",
+    "            \n",
+    "            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
+    "            features_test.append(new_set_test)\n",
+    "        \n",
+    "        lda = LDA(n_components= (n_dim - 1))\n",
+    "        features_lda_train = []\n",
+    "        features_lda_test = []\n",
+    "        \n",
+    "        for i in range(n_dim):\n",
+    "            features_lda_train_new = lda.fit_transform(features_train[i], self.y_train)\n",
+    "            features_lda_train.append(pd.DataFrame(features_lda_train_new))\n",
+    "            \n",
+    "            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)\n",
+    "            features_lda_test.append(pd.DataFrame(features_lda_test_new))\n",
+    "            \n",
+    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        X = np.array(data, requires_grad=False)\n",
+    "        \n",
+    "        \n",
+    "            \n",
+    "    def perform_PCA(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"PCA\"\n",
+    "        \n",
+    "        y_train.value_counts(normalize=True)*100\n",
+    "        y_test.value_counts(normalize=True)*100\n",
+    "        \n",
+    "        pca = PCA(n_components=n_dim, svd_solver='full')\n",
+    "        pca.fit(x_train)\n",
+    "        x_train_pca = pca.transform(x_train)\n",
+    "        pca.fit(x_test)\n",
+    "        x_test = pca.transform(x_test)\n",
+    "        \n",
+    "        data = normalize(x_train_pca)\n",
+    "        x_test = normalize(x_test)\n",
+    "        \n",
+    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        X = np.array(data, requires_grad=False)\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4393f82c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f48c0e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = Data(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dddfb6b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 9695 to 4714\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.092500</td>\n",
+       "      <td>282.785500</td>\n",
+       "      <td>0.315000</td>\n",
+       "      <td>2.130000</td>\n",
+       "      <td>0.073500</td>\n",
+       "      <td>1.112500</td>\n",
+       "      <td>2.355000</td>\n",
+       "      <td>3.092500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003500</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.00250</td>\n",
+       "      <td>0.308000</td>\n",
+       "      <td>0.197500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.049000</td>\n",
+       "      <td>0.024000</td>\n",
+       "      <td>44.923000</td>\n",
+       "      <td>0.270500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.300566</td>\n",
+       "      <td>612.116292</td>\n",
+       "      <td>1.874709</td>\n",
+       "      <td>5.852797</td>\n",
+       "      <td>0.857009</td>\n",
+       "      <td>6.910334</td>\n",
+       "      <td>2.972607</td>\n",
+       "      <td>14.300566</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.059072</td>\n",
+       "      <td>0.487872</td>\n",
+       "      <td>0.04995</td>\n",
+       "      <td>0.461782</td>\n",
+       "      <td>0.398212</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.215922</td>\n",
+       "      <td>0.354241</td>\n",
+       "      <td>60.920298</td>\n",
+       "      <td>0.444329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>285.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>12712.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      3.092500    282.785500     0.315000     2.130000     0.073500   \n",
+       "std      14.300566    612.116292     1.874709     5.852797     0.857009   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     39.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000    100.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    285.500000     0.000000     2.000000     0.000000   \n",
+       "max     420.000000  12712.000000    48.000000   105.000000    35.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      1.112500     2.355000     3.092500     0.0     0.0  ...   \n",
+       "std       6.910334     2.972607    14.300566     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     230.000000     8.000000   420.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104     col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.00000  2000.000000  2000.000000   \n",
+       "mean      0.003500     0.390000     0.00250     0.308000     0.197500   \n",
+       "std       0.059072     0.487872     0.04995     0.461782     0.398212   \n",
+       "min       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.00000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.00000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.049000     0.024000    44.923000     0.270500  \n",
+       "std        0.0     0.215922     0.354241    60.920298     0.444329  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    21.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.000000     9.000000   747.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb47bd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class QBC:\n",
+    "    def __init__(self, Data, n_dim, n_layers, \n",
+    "                 optimizer = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08),\n",
+    "                 loss_function = None, batch_size = 10, backend = \"default.qubit\", shots = 0):\n",
+    "        \n",
+    "        if loss_function == None:\n",
+    "            self.loss_function = square_loss\n",
+    "        else: \n",
+    "            self.loss_function = loss_function\n",
+    "        self.opt = optimizer\n",
+    "        self.batch_size = batch_size\n",
+    "        \n",
+    "        self.n_dim = n_dim\n",
+    "        \n",
+    "        dev = qml.device(backend, wires = self.n_dim, shots=shots)\n",
+    "        #dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "        #dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "        #dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "        @qml.qnode(dev)\n",
+    "        def circuit(parameters, data):\n",
+    "            for i in range(num_qubits):\n",
+    "                qml.Hadamard(wires = i)\n",
+    "\n",
+    "            AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "\n",
+    "            qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "\n",
+    "            return qml.expval(qml.PauliZ(0))\n",
+    "        \n",
+    "        self.n_layers = n_layers\n",
+    "        self.weights = 0.01 * np.random.randn(self.n_layers, self.n_dim, 3, requires_grad=True)\n",
+    "        self.bias = np.array(0.0, requires_grad=True)\n",
+    "        \n",
+    "    def variational_classifier(weights, bias, x):\n",
+    "        return circuit(weights, x) + bias\n",
+    "    \n",
+    "    def square_loss(labels, predictions):\n",
+    "        loss = 0\n",
+    "        for l, p in zip(labels, predictions):\n",
+    "            loss = loss + (l - p) ** 2\n",
+    "\n",
+    "        loss = loss / len(labels)\n",
+    "        return loss\n",
+    "    \n",
+    "    def accuracy(labels, predictions):\n",
+    "\n",
+    "        loss = 0\n",
+    "        for l, p in zip(labels, predictions):\n",
+    "            if abs(l - p) < 1e-5:\n",
+    "                loss = loss + 1\n",
+    "        loss = loss / len(labels)\n",
+    "\n",
+    "        return loss\n",
+    "    \n",
+    "    def cost(weights, bias, X, Y):\n",
+    "        predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "        return self.loss_function(Y, predictions)\n",
+    "\n",
+    "    def train(self, n_epochs):\n",
+    "        wbest = 0\n",
+    "        bbest = 0\n",
+    "        abest = 0\n",
+    "\n",
+    "        for it in range(n_epochs):\n",
+    "\n",
+    "            # weights update by one optimizer step\n",
+    "\n",
+    "            batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "            X_batch = X[batch_index]\n",
+    "            Y_batch = Y[batch_index]\n",
+    "            weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "            # Compute the accuracy\n",
+    "            predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "\n",
+    "            if accuracy(Y, predictions) > abest:\n",
+    "                wbest = weights\n",
+    "                bbest = bias\n",
+    "                abest = accuracy(Y, predictions)\n",
+    "                print('New best')\n",
+    "\n",
+    "            acc = accuracy(Y, predictions)\n",
+    "\n",
+    "            print(\n",
+    "                \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "                    it + 1, cost(weights, bias, X, Y), acc\n",
+    "                )\n",
+    "            )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QML4FRAUD/Architecture.ipynb b/QML4FRAUD/Architecture.ipynb
new file mode 100644
index 0000000..3f95b7d
--- /dev/null
+++ b/QML4FRAUD/Architecture.ipynb
@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9190faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "#from pennylane_qiskit import IBMQDevice\n",
+    "#from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c2c45f75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PrepareData:\n",
+    "    def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):\n",
+    "        self.data = data\n",
+    "        self.preprocess_done = None\n",
+    "        \n",
+    "        if sample_size == 0:\n",
+    "            self.data_sample = data\n",
+    "        else:\n",
+    "            self.data_sample = data.sample(sample_size)\n",
+    "            \n",
+    "        self.train_set, self.test_set = train_test_split(self.data_sample, test_size=test_split, random_state=seed)\n",
+    "        \n",
+    "        self.y_train = self.train_set[[target]]\n",
+    "        self.y_test = self.test_set[[target]]\n",
+    "        \n",
+    "        self.x_train = self.train_set.drop(target, axis=1)\n",
+    "        self.x_test = self.test_set.drop(target, axis=1)\n",
+    "        \n",
+    "    def view_info(self):\n",
+    "        print(self.data_sample.info())\n",
+    "        if self.preprocess_done == None:\n",
+    "            print(\"No preprocessing done yet.\")\n",
+    "        else:\n",
+    "            print(\"Preprocessing done via: \", self.preprocess_done)\n",
+    "        return self.data_sample.describe()\n",
+    "    \n",
+    "    def perform_LDA(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"LDA\"\n",
+    "        \n",
+    "        length = len(self.x_train.columns)\n",
+    "        split_feature = int(length/n_dim)\n",
+    "        features_train = []\n",
+    "        features_test = []\n",
+    "        \n",
+    "        for i in range(n_dim):\n",
+    "            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
+    "            features_train.append(new_set_train)\n",
+    "            \n",
+    "            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
+    "            features_test.append(new_set_test)\n",
+    "        \n",
+    "        lda = LDA(n_components= (n_dim - 1))\n",
+    "        features_lda_train = []\n",
+    "        features_lda_test = []\n",
+    "        \n",
+    "        for i in range(n_dim):\n",
+    "            features_lda_train_new = lda.fit_transform(features_train[i], self.y_train)\n",
+    "            features_lda_train.append(pd.DataFrame(features_lda_train_new))\n",
+    "            \n",
+    "            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)\n",
+    "            features_lda_test.append(pd.DataFrame(features_lda_test_new))\n",
+    "            \n",
+    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        X = np.array(data, requires_grad=False)\n",
+    "        \n",
+    "        \n",
+    "            \n",
+    "    def perform_PCA(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"PCA\"\n",
+    "        \n",
+    "        y_train.value_counts(normalize=True)*100\n",
+    "        y_test.value_counts(normalize=True)*100\n",
+    "        \n",
+    "        pca = PCA(n_components=n_dim, svd_solver='full')\n",
+    "        pca.fit(x_train)\n",
+    "        x_train_pca = pca.transform(x_train)\n",
+    "        pca.fit(x_test)\n",
+    "        x_test = pca.transform(x_test)\n",
+    "        \n",
+    "        data = normalize(x_train_pca)\n",
+    "        x_test = normalize(x_test)\n",
+    "        \n",
+    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        X = np.array(data, requires_grad=False)\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4393f82c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7f48c0e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = Data(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dddfb6b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 9695 to 4714\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.092500</td>\n",
+       "      <td>282.785500</td>\n",
+       "      <td>0.315000</td>\n",
+       "      <td>2.130000</td>\n",
+       "      <td>0.073500</td>\n",
+       "      <td>1.112500</td>\n",
+       "      <td>2.355000</td>\n",
+       "      <td>3.092500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003500</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.00250</td>\n",
+       "      <td>0.308000</td>\n",
+       "      <td>0.197500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.049000</td>\n",
+       "      <td>0.024000</td>\n",
+       "      <td>44.923000</td>\n",
+       "      <td>0.270500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.300566</td>\n",
+       "      <td>612.116292</td>\n",
+       "      <td>1.874709</td>\n",
+       "      <td>5.852797</td>\n",
+       "      <td>0.857009</td>\n",
+       "      <td>6.910334</td>\n",
+       "      <td>2.972607</td>\n",
+       "      <td>14.300566</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.059072</td>\n",
+       "      <td>0.487872</td>\n",
+       "      <td>0.04995</td>\n",
+       "      <td>0.461782</td>\n",
+       "      <td>0.398212</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.215922</td>\n",
+       "      <td>0.354241</td>\n",
+       "      <td>60.920298</td>\n",
+       "      <td>0.444329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>285.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>12712.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      3.092500    282.785500     0.315000     2.130000     0.073500   \n",
+       "std      14.300566    612.116292     1.874709     5.852797     0.857009   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     39.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000    100.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    285.500000     0.000000     2.000000     0.000000   \n",
+       "max     420.000000  12712.000000    48.000000   105.000000    35.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      1.112500     2.355000     3.092500     0.0     0.0  ...   \n",
+       "std       6.910334     2.972607    14.300566     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     230.000000     8.000000   420.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104     col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.00000  2000.000000  2000.000000   \n",
+       "mean      0.003500     0.390000     0.00250     0.308000     0.197500   \n",
+       "std       0.059072     0.487872     0.04995     0.461782     0.398212   \n",
+       "min       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.00000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.00000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.049000     0.024000    44.923000     0.270500  \n",
+       "std        0.0     0.215922     0.354241    60.920298     0.444329  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    21.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.000000     9.000000   747.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb47bd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class QBC:\n",
+    "    def __init__(self, Data, n_dim, n_layers, \n",
+    "                 optimizer = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08),\n",
+    "                 loss_function = None, batch_size = 10, backend = \"default.qubit\", shots = 0):\n",
+    "        \n",
+    "        if loss_function == None:\n",
+    "            self.loss_function = square_loss\n",
+    "        else: \n",
+    "            self.loss_function = loss_function\n",
+    "        self.opt = optimizer\n",
+    "        self.batch_size = batch_size\n",
+    "        \n",
+    "        self.n_dim = n_dim\n",
+    "        \n",
+    "        dev = qml.device(backend, wires = self.n_dim, shots=shots)\n",
+    "        #dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "        #dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "        #dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "        @qml.qnode(dev)\n",
+    "        def circuit(parameters, data):\n",
+    "            for i in range(num_qubits):\n",
+    "                qml.Hadamard(wires = i)\n",
+    "\n",
+    "            AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "\n",
+    "            qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "\n",
+    "            return qml.expval(qml.PauliZ(0))\n",
+    "        \n",
+    "        self.n_layers = n_layers\n",
+    "        self.weights = 0.01 * np.random.randn(self.n_layers, self.n_dim, 3, requires_grad=True)\n",
+    "        self.bias = np.array(0.0, requires_grad=True)\n",
+    "        \n",
+    "    def variational_classifier(weights, bias, x):\n",
+    "        return circuit(weights, x) + bias\n",
+    "    \n",
+    "    def square_loss(labels, predictions):\n",
+    "        loss = 0\n",
+    "        for l, p in zip(labels, predictions):\n",
+    "            loss = loss + (l - p) ** 2\n",
+    "\n",
+    "        loss = loss / len(labels)\n",
+    "        return loss\n",
+    "    \n",
+    "    def accuracy(labels, predictions):\n",
+    "\n",
+    "        loss = 0\n",
+    "        for l, p in zip(labels, predictions):\n",
+    "            if abs(l - p) < 1e-5:\n",
+    "                loss = loss + 1\n",
+    "        loss = loss / len(labels)\n",
+    "\n",
+    "        return loss\n",
+    "    \n",
+    "    def cost(weights, bias, X, Y):\n",
+    "        predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "        return self.loss_function(Y, predictions)\n",
+    "\n",
+    "    def train(self, n_epochs):\n",
+    "        wbest = 0\n",
+    "        bbest = 0\n",
+    "        abest = 0\n",
+    "\n",
+    "        for it in range(n_epochs):\n",
+    "\n",
+    "            # weights update by one optimizer step\n",
+    "\n",
+    "            batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "            X_batch = X[batch_index]\n",
+    "            Y_batch = Y[batch_index]\n",
+    "            weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "            # Compute the accuracy\n",
+    "            predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "\n",
+    "            if accuracy(Y, predictions) > abest:\n",
+    "                wbest = weights\n",
+    "                bbest = bias\n",
+    "                abest = accuracy(Y, predictions)\n",
+    "                print('New best')\n",
+    "\n",
+    "            acc = accuracy(Y, predictions)\n",
+    "\n",
+    "            print(\n",
+    "                \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "                    it + 1, cost(weights, bias, X, Y), acc\n",
+    "                )\n",
+    "            )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9c8f53ba2de0bf52b5a682ff38258bec78fa72bf Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Tue, 26 Jul 2022 13:22:04 -0400
Subject: [PATCH 04/25] Clean-up and feature grouping

---
 QML4FRAUD/EDA.ipynb           |  1756 +++
 QML4FRAUD/dataset_nonzero.csv | 20469 ++++++++++++++++++++++++++++++++
 2 files changed, 22225 insertions(+)
 create mode 100644 QML4FRAUD/EDA.ipynb
 create mode 100644 QML4FRAUD/dataset_nonzero.csv

diff --git a/QML4FRAUD/EDA.ipynb b/QML4FRAUD/EDA.ipynb
new file mode 100644
index 0000000..2e65d90
--- /dev/null
+++ b/QML4FRAUD/EDA.ipynb
@@ -0,0 +1,1756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Exploration and Feature Grouping\n",
+    "\n",
+    "In this notebook we do some quick cleaning of the dataset and perform LDA with features grouped according to correlation.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import libraries needed\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "from pennylane_qiskit import IBMQDevice\n",
+    "from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "# visualization\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import time\n",
+    "start = time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quick Clean-up and Feature Grouping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.0</td>\n",
+       "      <td>20468.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>294.793043</td>\n",
+       "      <td>0.420021</td>\n",
+       "      <td>2.329343</td>\n",
+       "      <td>0.083594</td>\n",
+       "      <td>0.939857</td>\n",
+       "      <td>2.386066</td>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004935</td>\n",
+       "      <td>0.358120</td>\n",
+       "      <td>0.002443</td>\n",
+       "      <td>0.308384</td>\n",
+       "      <td>0.190737</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.049345</td>\n",
+       "      <td>0.024282</td>\n",
+       "      <td>43.486125</td>\n",
+       "      <td>0.265683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>717.541984</td>\n",
+       "      <td>7.367275</td>\n",
+       "      <td>10.068512</td>\n",
+       "      <td>0.840537</td>\n",
+       "      <td>4.222896</td>\n",
+       "      <td>3.018140</td>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.073478</td>\n",
+       "      <td>0.479459</td>\n",
+       "      <td>0.049366</td>\n",
+       "      <td>0.461837</td>\n",
+       "      <td>0.392892</td>\n",
+       "      <td>0.006990</td>\n",
+       "      <td>0.216593</td>\n",
+       "      <td>0.305079</td>\n",
+       "      <td>59.217560</td>\n",
+       "      <td>0.441707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>283.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>37808.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>772.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              col_0         col_1         col_2         col_3         col_4  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       3.226256    294.793043      0.420021      2.329343      0.083594   \n",
+       "std       20.564308    717.541984      7.367275     10.068512      0.840537   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000     38.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000     97.000000      0.000000      1.000000      0.000000   \n",
+       "75%        2.000000    283.000000      0.000000      2.000000      0.000000   \n",
+       "max     2301.000000  37808.000000    904.000000    772.000000     54.000000   \n",
+       "\n",
+       "              col_5         col_6         col_7    col_8    col_9  ...  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.0  20468.0  ...   \n",
+       "mean       0.939857      2.386066      3.226256      0.0      0.0  ...   \n",
+       "std        4.222896      3.018140     20.564308      0.0      0.0  ...   \n",
+       "min        0.000000     -1.000000      0.000000      0.0      0.0  ...   \n",
+       "25%        0.000000      0.000000      0.000000      0.0      0.0  ...   \n",
+       "50%        0.000000      2.000000      0.000000      0.0      0.0  ...   \n",
+       "75%        1.000000      6.000000      2.000000      0.0      0.0  ...   \n",
+       "max      230.000000     11.000000   2301.000000      0.0      0.0  ...   \n",
+       "\n",
+       "            col_103       col_104       col_105       col_106       col_107  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.004935      0.358120      0.002443      0.308384      0.190737   \n",
+       "std        0.073478      0.479459      0.049366      0.461837      0.392892   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%        0.000000      1.000000      0.000000      1.000000      0.000000   \n",
+       "max        2.000000      1.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "            col_108       col_109       col_110       col_111       targets  \n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000  \n",
+       "mean       0.000049      0.049345      0.024282     43.486125      0.265683  \n",
+       "std        0.006990      0.216593      0.305079     59.217560      0.441707  \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000  \n",
+       "25%        0.000000      0.000000      0.000000      5.000000      0.000000  \n",
+       "50%        0.000000      0.000000      0.000000     19.000000      0.000000  \n",
+       "75%        0.000000      0.000000      0.000000     61.250000      1.000000  \n",
+       "max        1.000000      1.000000     17.000000    747.000000      1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read out CSV and sets/samples creation\n",
+    "\n",
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some columns seem to be all zeros. Let's drop them as they provide no differentiation between observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>294.793043</td>\n",
+       "      <td>0.420021</td>\n",
+       "      <td>2.329343</td>\n",
+       "      <td>0.083594</td>\n",
+       "      <td>0.939857</td>\n",
+       "      <td>2.386066</td>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>0.038646</td>\n",
+       "      <td>0.363152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004935</td>\n",
+       "      <td>0.358120</td>\n",
+       "      <td>0.002443</td>\n",
+       "      <td>0.308384</td>\n",
+       "      <td>0.190737</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.049345</td>\n",
+       "      <td>0.024282</td>\n",
+       "      <td>43.486125</td>\n",
+       "      <td>0.265683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>717.541984</td>\n",
+       "      <td>7.367275</td>\n",
+       "      <td>10.068512</td>\n",
+       "      <td>0.840537</td>\n",
+       "      <td>4.222896</td>\n",
+       "      <td>3.018140</td>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>0.192754</td>\n",
+       "      <td>0.480920</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.073478</td>\n",
+       "      <td>0.479459</td>\n",
+       "      <td>0.049366</td>\n",
+       "      <td>0.461837</td>\n",
+       "      <td>0.392892</td>\n",
+       "      <td>0.006990</td>\n",
+       "      <td>0.216593</td>\n",
+       "      <td>0.305079</td>\n",
+       "      <td>59.217560</td>\n",
+       "      <td>0.441707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>283.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>37808.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>772.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 98 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              col_0         col_1         col_2         col_3         col_4  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       3.226256    294.793043      0.420021      2.329343      0.083594   \n",
+       "std       20.564308    717.541984      7.367275     10.068512      0.840537   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000     38.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000     97.000000      0.000000      1.000000      0.000000   \n",
+       "75%        2.000000    283.000000      0.000000      2.000000      0.000000   \n",
+       "max     2301.000000  37808.000000    904.000000    772.000000     54.000000   \n",
+       "\n",
+       "              col_5         col_6         col_7        col_13        col_14  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.939857      2.386066      3.226256      0.038646      0.363152   \n",
+       "std        4.222896      3.018140     20.564308      0.192754      0.480920   \n",
+       "min        0.000000     -1.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000      2.000000      0.000000      0.000000      0.000000   \n",
+       "75%        1.000000      6.000000      2.000000      0.000000      1.000000   \n",
+       "max      230.000000     11.000000   2301.000000      1.000000      1.000000   \n",
+       "\n",
+       "       ...       col_103       col_104       col_105       col_106  \\\n",
+       "count  ...  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean   ...      0.004935      0.358120      0.002443      0.308384   \n",
+       "std    ...      0.073478      0.479459      0.049366      0.461837   \n",
+       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%    ...      0.000000      1.000000      0.000000      1.000000   \n",
+       "max    ...      2.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "            col_107       col_108       col_109       col_110       col_111  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.190737      0.000049      0.049345      0.024282     43.486125   \n",
+       "std        0.392892      0.006990      0.216593      0.305079     59.217560   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      5.000000   \n",
+       "50%        0.000000      0.000000      0.000000      0.000000     19.000000   \n",
+       "75%        0.000000      0.000000      0.000000      0.000000     61.250000   \n",
+       "max        1.000000      1.000000      1.000000     17.000000    747.000000   \n",
+       "\n",
+       "            targets  \n",
+       "count  20468.000000  \n",
+       "mean       0.265683  \n",
+       "std        0.441707  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        1.000000  \n",
+       "max        1.000000  \n",
+       "\n",
+       "[8 rows x 98 columns]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean = df.loc[:, df.any()]\n",
+    "df_clean.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the cleaned data\n",
+    "\n",
+    "df_clean.to_csv(\"dataset_nonzero.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to split the features into groups that are similar within but distinct between groups. Let's take a look at the correlation matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr = df_clean.corr()\n",
+    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The correlaton relation falls into two relatively clear patches. Since we are aiming to reduce the features into 2 latent features, the correlation matrix provides pretty good guidance on how to split. Let's take a closer look in the region where the two groups seem to be split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr = df_clean.loc[:,'col_49':'col_81'].corr()\n",
+    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll group features up to `col_69` into one group and the rest into the other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_sample = df_clean.sample(2000)\n",
+    "train,test = train_test_split(df_sample, test_size=0.30, random_state=10)\n",
+    "train_set = train\n",
+    "test_set = test\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separation of labels\n",
+    "\n",
+    "x_train = train_set\n",
+    "y_train = train_set[['targets']]\n",
+    "\n",
+    "x_test = test_set\n",
+    "y_test = test_set[['targets']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# grouping features\n",
+    " \n",
+    "features_a = x_train.loc[:,:'col_69']\n",
+    "features_b = x_train.loc[:,'col_69':]\n",
+    "\n",
+    "features_a_test = x_test.loc[:,:'col_67']\n",
+    "features_b_test = x_test.loc[:,'col_67':]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LDA Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transform the features\n",
+    "\n",
+    "lda = LDA(n_components=1)\n",
+    "features_lda_1 = lda.fit_transform(features_a, y_train.values.ravel())\n",
+    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "\n",
+    "features_lda_1_test = lda.fit_transform(features_a_test, y_test.values.ravel())\n",
+    "features_lda_2_test = lda.fit_transform(features_b_test, y_test.values.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_lda_1 = pd.DataFrame(features_lda_1)\n",
+    "features_lda_2 = pd.DataFrame(features_lda_2)\n",
+    "\n",
+    "features_lda_1_test = pd.DataFrame(features_lda_1_test)\n",
+    "features_lda_2_test = pd.DataFrame(features_lda_2_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.133929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.529977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.922923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.487324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.368786</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0\n",
+       "0  0.133929\n",
+       "1  0.529977\n",
+       "2 -0.922923\n",
+       "3  0.487324\n",
+       "4  0.368786"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_lda_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# combine the 2 features\n",
+    "\n",
+    "x_train_lda = features_lda_1.join(features_lda_2, lsuffix=\"_left\", rsuffix=\"_right\")\n",
+    "x_test_lda = features_lda_1_test.join(features_lda_2_test, lsuffix=\"_left\", rsuffix=\"_right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
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+       "      <th>0</th>\n",
+       "      <td>0.133929</td>\n",
+       "      <td>-1.091509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.529977</td>\n",
+       "      <td>-0.960667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.922923</td>\n",
+       "      <td>-0.639466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.487324</td>\n",
+       "      <td>0.204965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.368786</td>\n",
+       "      <td>-0.639466</td>\n",
+       "    </tr>\n",
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+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>1.091340</td>\n",
+       "      <td>0.604241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>-1.423830</td>\n",
+       "      <td>-0.052359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1397</th>\n",
+       "      <td>-0.065439</td>\n",
+       "      <td>-0.052359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1398</th>\n",
+       "      <td>-1.012592</td>\n",
+       "      <td>-0.591393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1399</th>\n",
+       "      <td>0.136169</td>\n",
+       "      <td>-0.639466</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1400 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        0_left   0_right\n",
+       "0     0.133929 -1.091509\n",
+       "1     0.529977 -0.960667\n",
+       "2    -0.922923 -0.639466\n",
+       "3     0.487324  0.204965\n",
+       "4     0.368786 -0.639466\n",
+       "...        ...       ...\n",
+       "1395  1.091340  0.604241\n",
+       "1396 -1.423830 -0.052359\n",
+       "1397 -0.065439 -0.052359\n",
+       "1398 -1.012592 -0.591393\n",
+       "1399  0.136169 -0.639466\n",
+       "\n",
+       "[1400 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_left</th>\n",
+       "      <th>0_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.262410</td>\n",
+       "      <td>-0.844093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.016687</td>\n",
+       "      <td>-0.403047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.978751</td>\n",
+       "      <td>-1.711895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.111510</td>\n",
+       "      <td>-0.000622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.286865</td>\n",
+       "      <td>-0.819957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>0.675788</td>\n",
+       "      <td>3.803108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-0.522451</td>\n",
+       "      <td>-0.281238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-2.422944</td>\n",
+       "      <td>0.172625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>0.764962</td>\n",
+       "      <td>-0.097968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>0.873537</td>\n",
+       "      <td>2.639325</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0_left   0_right\n",
+       "0    0.262410 -0.844093\n",
+       "1    0.016687 -0.403047\n",
+       "2   -0.978751 -1.711895\n",
+       "3   -0.111510 -0.000622\n",
+       "4    0.286865 -0.819957\n",
+       "..        ...       ...\n",
+       "595  0.675788  3.803108\n",
+       "596 -0.522451 -0.281238\n",
+       "597 -2.422944  0.172625\n",
+       "598  0.764962 -0.097968\n",
+       "599  0.873537  2.639325\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_train_lda)\n",
+    "data = std_scale.transform(x_train_lda)\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_test_lda)\n",
+    "x_test_lda_n = std_scale.transform(x_test_lda)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.2270424 , -0.60888212],\n",
+       "       [ 0.01443808, -0.29073569],\n",
+       "       [-0.84683642, -1.23486658],\n",
+       "       ...,\n",
+       "       [-2.09638321,  0.12452206],\n",
+       "       [ 0.66186192, -0.07066894],\n",
+       "       [ 0.75580324,  1.90386338]])"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_dim = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        73.857143\n",
+       "1.0        26.142857\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in train\n",
+    "\n",
+    "y_train.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        72.5\n",
+       "1.0        27.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in test\n",
+    "\n",
+    "y_test.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## QML\n",
+    "\n",
+    "We then perform quantum machine learning using the two LDA features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Angle Encoding\n",
+    "\n",
+    "num_qubits = n_dim\n",
+    "\n",
+    "dev = qml.device('default.qubit', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "#dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def circuit(parameters, data):\n",
+    "    for i in range(num_qubits):\n",
+    "        qml.Hadamard(wires = i)\n",
+    "    \n",
+    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "    \n",
+    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "    \n",
+    "    return qml.expval(qml.PauliZ(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0.00644174 -0.00463604 -0.00809824]\n",
+      "  [-0.01525027 -0.00605393  0.00755927]]\n",
+      "\n",
+      " [[ 0.00692921  0.02457865  0.00375194]\n",
+      "  [-0.01205123 -0.00389854 -0.01491166]]\n",
+      "\n",
+      " [[-0.00675347  0.00046245 -0.01278641]\n",
+      "  [-0.01214017  0.00756313 -0.00371434]]\n",
+      "\n",
+      " [[-0.00388285  0.00926124 -0.00735844]\n",
+      "  [ 0.01285322 -0.02080725 -0.00739566]]\n",
+      "\n",
+      " [[-0.00029247  0.0012585   0.00145581]\n",
+      "  [ 0.00211849 -0.00099338  0.0103849 ]]] 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_layers = 5\n",
+    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
+    "bias_init = np.array(0.0, requires_grad=True)\n",
+    "\n",
+    "print(weights_init, bias_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-0.078125, requires_grad=True)"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit(weights_init, data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def variational_classifier(weights, bias, x):\n",
+    "    return circuit(weights, x) + bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def square_loss(labels, predictions):\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        loss = loss + (l - p) ** 2\n",
+    "\n",
+    "    loss = loss / len(labels)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(labels, predictions):\n",
+    "\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        if abs(l - p) < 1e-5:\n",
+    "            loss = loss + 1\n",
+    "    loss = loss / len(labels)\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost(weights, bias, X, Y):\n",
+    "    predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "    return square_loss(Y, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X = [tensor(0.11799041, requires_grad=False), tensor(-0.82749202, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.46690762, requires_grad=False), tensor(-0.72829837, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.81309064, requires_grad=False), tensor(-0.48478984, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.42933072, requires_grad=False), tensor(0.15538733, requires_grad=False)], Y =  1\n",
+      "X = [tensor(0.32489913, requires_grad=False), tensor(-0.48478984, requires_grad=False)], Y = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "X = np.array(data, requires_grad=False)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
+    "batch_size = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7163056 | Accuracy: 0.7614286 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.5559769 | Accuracy: 0.8200000 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.4932610 | Accuracy: 0.8314286 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.4961598 | Accuracy: 0.8400000 \n",
+      "Iter:     5 | Cost: 0.5057743 | Accuracy: 0.8400000 \n",
+      "Iter:     6 | Cost: 0.5069953 | Accuracy: 0.8400000 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.4873486 | Accuracy: 0.8492857 \n",
+      "New best\n",
+      "Iter:     8 | Cost: 0.4672314 | Accuracy: 0.8600000 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.4420190 | Accuracy: 0.8635714 \n",
+      "Iter:    10 | Cost: 0.4362290 | Accuracy: 0.8635714 \n",
+      "Iter:    11 | Cost: 0.4296042 | Accuracy: 0.8585714 \n",
+      "Iter:    12 | Cost: 0.4506719 | Accuracy: 0.8557143 \n",
+      "Iter:    13 | Cost: 0.4721339 | Accuracy: 0.8500000 \n",
+      "Iter:    14 | Cost: 0.4821543 | Accuracy: 0.8507143 \n",
+      "Iter:    15 | Cost: 0.4873148 | Accuracy: 0.8507143 \n",
+      "Iter:    16 | Cost: 0.4802705 | Accuracy: 0.8514286 \n",
+      "Iter:    17 | Cost: 0.4694841 | Accuracy: 0.8535714 \n",
+      "Iter:    18 | Cost: 0.4581866 | Accuracy: 0.8528571 \n",
+      "Iter:    19 | Cost: 0.4508369 | Accuracy: 0.8557143 \n",
+      "Iter:    20 | Cost: 0.4410900 | Accuracy: 0.8578571 \n",
+      "Iter:    21 | Cost: 0.4346116 | Accuracy: 0.8607143 \n",
+      "Iter:    22 | Cost: 0.4395575 | Accuracy: 0.8607143 \n",
+      "Iter:    23 | Cost: 0.4369053 | Accuracy: 0.8614286 \n",
+      "Iter:    24 | Cost: 0.4391714 | Accuracy: 0.8592857 \n",
+      "Iter:    25 | Cost: 0.4430851 | Accuracy: 0.8571429 \n",
+      "Iter:    26 | Cost: 0.4370470 | Accuracy: 0.8585714 \n",
+      "Iter:    27 | Cost: 0.4480947 | Accuracy: 0.8578571 \n",
+      "Iter:    28 | Cost: 0.4317516 | Accuracy: 0.8600000 \n",
+      "New best\n",
+      "Iter:    29 | Cost: 0.4689808 | Accuracy: 0.8650000 \n",
+      "Iter:    30 | Cost: 0.4780110 | Accuracy: 0.8621429 \n",
+      "Iter:    31 | Cost: 0.5179296 | Accuracy: 0.8535714 \n",
+      "Iter:    32 | Cost: 0.5853279 | Accuracy: 0.8314286 \n",
+      "Iter:    33 | Cost: 0.5275210 | Accuracy: 0.8542857 \n",
+      "Iter:    34 | Cost: 0.4413556 | Accuracy: 0.8621429 \n",
+      "Iter:    35 | Cost: 0.4123321 | Accuracy: 0.8614286 \n",
+      "Iter:    36 | Cost: 0.4264267 | Accuracy: 0.8600000 \n",
+      "Iter:    37 | Cost: 0.4552265 | Accuracy: 0.8592857 \n",
+      "Iter:    38 | Cost: 0.4896763 | Accuracy: 0.8578571 \n",
+      "Iter:    39 | Cost: 0.5017294 | Accuracy: 0.8564286 \n",
+      "Iter:    40 | Cost: 0.4911548 | Accuracy: 0.8528571 \n",
+      "Iter:    41 | Cost: 0.4806956 | Accuracy: 0.8507143 \n",
+      "Iter:    42 | Cost: 0.4881041 | Accuracy: 0.8435714 \n",
+      "Iter:    43 | Cost: 0.4955334 | Accuracy: 0.8407143 \n",
+      "Iter:    44 | Cost: 0.5159703 | Accuracy: 0.8407143 \n",
+      "Iter:    45 | Cost: 0.5603780 | Accuracy: 0.8421429 \n",
+      "Iter:    46 | Cost: 0.6229637 | Accuracy: 0.8450000 \n",
+      "Iter:    47 | Cost: 0.6417202 | Accuracy: 0.8435714 \n",
+      "Iter:    48 | Cost: 0.5837545 | Accuracy: 0.8492857 \n",
+      "Iter:    49 | Cost: 0.4822762 | Accuracy: 0.8550000 \n",
+      "Iter:    50 | Cost: 0.4385916 | Accuracy: 0.8592857 \n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = weights_init\n",
+    "bias = bias_init\n",
+    "\n",
+    "wbest = 0\n",
+    "bbest = 0\n",
+    "abest = 0\n",
+    "\n",
+    "for it in range(50):\n",
+    "\n",
+    "    # weights update by one optimizer step\n",
+    "\n",
+    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "    X_batch = X[batch_index]\n",
+    "    Y_batch = Y[batch_index]\n",
+    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "    # Compute the accuracy\n",
+    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "    \n",
+    "    if accuracy(Y, predictions) > abest:\n",
+    "        wbest = weights\n",
+    "        bbest = bias\n",
+    "        abest = accuracy(Y, predictions)\n",
+    "        print('New best')\n",
+    "\n",
+    "    acc = accuracy(Y, predictions)\n",
+    "\n",
+    "    print(\n",
+    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "            it + 1, cost(weights, bias, X, Y), acc\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
+    "Xte = np.array(normalize(x_test_lda_n), requires_grad=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost: 0.4677206980912003, Accuracy: 85.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
+    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
+    "acc = accuracy(Yte, predictions)\n",
+    "\n",
+    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions  Test\n",
+       "0          -1.0  -1.0\n",
+       "1          -1.0  -1.0\n",
+       "2          -1.0  -1.0\n",
+       "3          -1.0  -1.0\n",
+       "4          -1.0  -1.0\n",
+       "..          ...   ...\n",
+       "595         1.0   1.0\n",
+       "596        -1.0  -1.0\n",
+       "597        -1.0  -1.0\n",
+       "598        -1.0   1.0\n",
+       "599         1.0   1.0\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.94      0.87      0.90       468\n",
+      "         1.0       0.63      0.79      0.70       132\n",
+      "\n",
+      "    accuracy                           0.85       600\n",
+      "   macro avg       0.78      0.83      0.80       600\n",
+      "weighted avg       0.87      0.85      0.86       600\n",
+      "\n",
+      "0.6303030303030303\n",
+      "0.7878787878787878\n",
+      "0.7003367003367004\n",
+      "0.8287684537684538\n",
+      "[[407  61]\n",
+      " [ 28 104]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the classification report and important metrics\n",
+    "\n",
+    "print(metrics.classification_report(predictions,Yte))\n",
+    "print(metrics.precision_score(predictions,Yte))\n",
+    "print(metrics.recall_score(predictions,Yte))\n",
+    "print(metrics.f1_score(predictions,Yte))\n",
+    "print(metrics.balanced_accuracy_score(predictions,Yte))\n",
+    "print(metrics.confusion_matrix(predictions,Yte))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "QC",
+   "language": "python",
+   "name": "qc"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5d83d7e0c60eaba1e78b3a460636a8f0ffa6eaee266f7bf3ff4e970676c5e4a4"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/QML4FRAUD/dataset_nonzero.csv b/QML4FRAUD/dataset_nonzero.csv
new file mode 100644
index 0000000..76b339e
--- /dev/null
+++ b/QML4FRAUD/dataset_nonzero.csv
@@ -0,0 +1,20469 @@
+,col_0,col_1,col_2,col_3,col_4,col_5,col_6,col_7,col_13,col_14,col_15,col_16,col_17,col_22,col_23,col_24,col_25,col_26,col_27,col_28,col_29,col_30,col_31,col_32,col_33,col_34,col_36,col_37,col_38,col_39,col_40,col_41,col_42,col_43,col_44,col_45,col_46,col_47,col_48,col_49,col_50,col_54,col_55,col_56,col_57,col_58,col_59,col_60,col_61,col_62,col_63,col_64,col_65,col_66,col_67,col_68,col_69,col_72,col_73,col_74,col_75,col_76,col_77,col_78,col_79,col_80,col_81,col_82,col_83,col_84,col_85,col_86,col_87,col_88,col_89,col_90,col_91,col_92,col_93,col_94,col_95,col_96,col_97,col_98,col_99,col_100,col_101,col_102,col_103,col_104,col_105,col_106,col_107,col_108,col_109,col_110,col_111,targets
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+20462,0.0,53.0,0.0,0.0,0.0,2.0,4.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,11.0,2.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,68.0,0.0
+20463,0.0,88.0,0.0,0.0,0.0,2.0,-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,85.0,0.0
+20464,0.0,134.0,0.0,2.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,40035.0,0.0,2.0,2.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1337.6,5.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0
+20465,4.0,393.0,1.0,1.0,0.0,0.0,-1.0,4.0,0.0,0.0,0.0,1.0,0.0,4.0,62.0,1.0,1.0,13.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1163.32954545,88.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,45.0,0.0
+20466,0.0,10.0,0.0,1.0,0.0,0.0,-1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0
+20467,4.0,399.0,0.0,3.0,0.0,1.0,7.0,4.0,0.0,1.0,0.0,1.0,0.0,4.0,50.0,3.0,0.0,11.0,0.0,1.0,1.0,18.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,72.0,0.0

From a235f16b8c28dbda0a22400e08da89dc89ce7b8d Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Tue, 26 Jul 2022 13:26:06 -0400
Subject: [PATCH 05/25] Slight modification to EDA.ipynb

---
 QML4FRAUD/EDA.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/QML4FRAUD/EDA.ipynb b/QML4FRAUD/EDA.ipynb
index 2e65d90..3e0ea7e 100644
--- a/QML4FRAUD/EDA.ipynb
+++ b/QML4FRAUD/EDA.ipynb
@@ -749,7 +749,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll group features up to `col_69` into one group and the rest into the other."
+    "We'll simply group features up to `col_69` into one group and the rest into the other. There may be groupings better than this. Those would require further analysis."
    ]
   },
   {

From a3e7ec3d1ac18bda5fcd5e6f30359d316aa73d76 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 02:07:19 +0800
Subject: [PATCH 06/25] add architecture

---
 .../Architecture-checkpoint.ipynb             |  343 ++--
 ...Solution via architecture-checkpoint.ipynb | 1421 +++++++++++++++++
 QML4FRAUD/Architecture.ipynb                  |  593 +++++--
 .../Factory/__pycache__/data.cpython-39.pyc   |  Bin 0 -> 4504 bytes
 .../Factory/__pycache__/model.cpython-39.pyc  |  Bin 0 -> 4117 bytes
 QML4FRAUD/Factory/data.py                     |  149 ++
 QML4FRAUD/Factory/model.py                    |  119 ++
 QML4FRAUD/Solution via architecture.ipynb     | 1421 +++++++++++++++++
 8 files changed, 3788 insertions(+), 258 deletions(-)
 create mode 100644 QML4FRAUD/.ipynb_checkpoints/Solution via architecture-checkpoint.ipynb
 create mode 100644 QML4FRAUD/Factory/__pycache__/data.cpython-39.pyc
 create mode 100644 QML4FRAUD/Factory/__pycache__/model.cpython-39.pyc
 create mode 100644 QML4FRAUD/Factory/data.py
 create mode 100644 QML4FRAUD/Factory/model.py
 create mode 100644 QML4FRAUD/Solution via architecture.ipynb

diff --git a/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb b/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
index 3f95b7d..0583434 100644
--- a/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
+++ b/QML4FRAUD/.ipynb_checkpoints/Architecture-checkpoint.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d9190faa",
+   "id": "41542144",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,8 +31,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "c2c45f75",
+   "execution_count": 54,
+   "id": "0240b50b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,15 +62,29 @@
     "            print(\"Preprocessing done via: \", self.preprocess_done)\n",
     "        return self.data_sample.describe()\n",
     "    \n",
+    "    def view_preprocessed(self):\n",
+    "        if self.preprocess_done == None:\n",
+    "            print(\"Please do some preprocessing first.\")\n",
+    "        else:\n",
+    "            print(\"Training Set and Labels: \")\n",
+    "            print(self.train_X_preprocessed)\n",
+    "            print(self.train_Y_preprocessed)\n",
+    "\n",
+    "            print(\"Test Set and Labels: \")\n",
+    "            print(self.test_X_preprocessed)\n",
+    "            print(self.test_Y_preprocessed)\n",
+    "    \n",
     "    def perform_LDA(self, n_dim = 2):\n",
     "        \n",
     "        self.preprocess_done = \"LDA\"\n",
+    "        print(\"Performing LDA...\")\n",
     "        \n",
     "        length = len(self.x_train.columns)\n",
     "        split_feature = int(length/n_dim)\n",
     "        features_train = []\n",
     "        features_test = []\n",
     "        \n",
+    "        # Split Features (for Yaqi to change)\n",
     "        for i in range(n_dim):\n",
     "            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
     "            features_train.append(new_set_train)\n",
@@ -78,6 +92,7 @@
     "            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
     "            features_test.append(new_set_test)\n",
     "        \n",
+    "        # Run the LDA\n",
     "        lda = LDA(n_components= (n_dim - 1))\n",
     "        features_lda_train = []\n",
     "        features_lda_test = []\n",
@@ -88,38 +103,72 @@
     "            \n",
     "            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)\n",
     "            features_lda_test.append(pd.DataFrame(features_lda_test_new))\n",
-    "            \n",
-    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
-    "        X = np.array(data, requires_grad=False)\n",
     "        \n",
+    "        x_train_data = features_lda_train[0]\n",
+    "        x_test_data = features_lda_test[0]\n",
+    "        \n",
+    "        # Join the results together\n",
+    "        for i in range(1, n_dim):\n",
+    "            l_suffix = \"_\" + str(i)\n",
+    "            r_suffix = \"_\" + str(i+1)\n",
+    "            x_train_data = x_train_data.join(features_lda_train[i], lsuffix=l_suffix, rsuffix=r_suffix)\n",
+    "            x_test_data = x_test_data.join(features_lda_test[i], lsuffix=l_suffix, rsuffix=r_suffix)\n",
     "        \n",
+    "        # Normalize\n",
+    "        std_scale_train = StandardScaler().fit(x_train_data)\n",
+    "        x_train_data = std_scale_train.transform(x_train_data)\n",
+    "        \n",
+    "        std_scale_test = StandardScaler().fit(x_test_data)\n",
+    "        x_test_data = std_scale_test.transform(x_test_data)\n",
     "            \n",
+    "        # shift label from {0, 1} to {-1, 1}\n",
+    "        self.train_X_preprocessed = np.array(x_train_data, requires_grad=False)\n",
+    "        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)\n",
+    "        \n",
+    "        self.test_X_preprocessed = np.array(x_test_data, requires_grad=False)\n",
+    "        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)\n",
+    "        \n",
     "    def perform_PCA(self, n_dim = 2):\n",
     "        \n",
     "        self.preprocess_done = \"PCA\"\n",
+    "        print(\"Performing PCA...\")\n",
     "        \n",
-    "        y_train.value_counts(normalize=True)*100\n",
-    "        y_test.value_counts(normalize=True)*100\n",
+    "        self.y_train.value_counts(normalize=True)*100\n",
+    "        self.y_test.value_counts(normalize=True)*100\n",
     "        \n",
     "        pca = PCA(n_components=n_dim, svd_solver='full')\n",
-    "        pca.fit(x_train)\n",
-    "        x_train_pca = pca.transform(x_train)\n",
-    "        pca.fit(x_test)\n",
-    "        x_test = pca.transform(x_test)\n",
+    "        pca.fit(self.x_train)\n",
+    "        x_train_pca = pca.transform(self.x_train)\n",
+    "        pca.fit(self.x_test)\n",
+    "        x_test = pca.transform(self.x_test)\n",
+    "        \n",
+    "        train_X_preprocessed = normalize(self.x_train_pca)\n",
+    "        test_X_preprocessed = normalize(self.x_test)\n",
+    "        \n",
+    "        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        self.train_X_preprocessed = np.array(train_X_preprocessed, requires_grad=False)\n",
     "        \n",
-    "        data = normalize(x_train_pca)\n",
-    "        x_test = normalize(x_test)\n",
+    "        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        self.test_X_preprocessed = np.array(test_X_preprocessed, requires_grad=False)\n",
     "        \n",
-    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
-    "        X = np.array(data, requires_grad=False)\n",
+    "    def perform_normalize(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"Normalize\"\n",
+    "        print(\"Performing Normalize...\")\n",
+    "        \n",
+    "        self.y_train.value_counts(normalize=True)*100\n",
+    "        self.y_test.value_counts(normalize=True)*100\n",
+    "        \n",
+    "        self.x_train.value_counts(normalize=True)*100\n",
+    "        self.x_test.value_counts(normalize=True)*100\n",
     "        \n",
     "        "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "4393f82c",
+   "execution_count": 55,
+   "id": "6162ae6b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,18 +179,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "7f48c0e4",
+   "execution_count": 56,
+   "id": "38e42694",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = Data(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+    "data = PrepareData(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "dddfb6b3",
+   "execution_count": 57,
+   "id": "2e769ad0",
    "metadata": {},
    "outputs": [
     {
@@ -149,11 +198,12 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 9695 to 4714\n",
+      "Int64Index: 2000 entries, 9997 to 10438\n",
       "Columns: 113 entries, col_0 to targets\n",
       "dtypes: float64(113)\n",
       "memory usage: 1.7 MB\n",
-      "None\n"
+      "None\n",
+      "No preprocessing done yet.\n"
      ]
     },
     {
@@ -204,7 +254,7 @@
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
@@ -220,63 +270,63 @@
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.0</td>\n",
-       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>3.092500</td>\n",
-       "      <td>282.785500</td>\n",
-       "      <td>0.315000</td>\n",
-       "      <td>2.130000</td>\n",
-       "      <td>0.073500</td>\n",
-       "      <td>1.112500</td>\n",
-       "      <td>2.355000</td>\n",
-       "      <td>3.092500</td>\n",
+       "      <td>3.089000</td>\n",
+       "      <td>284.03150</td>\n",
+       "      <td>0.277500</td>\n",
+       "      <td>2.346000</td>\n",
+       "      <td>0.059000</td>\n",
+       "      <td>0.885500</td>\n",
+       "      <td>2.543000</td>\n",
+       "      <td>3.089000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003500</td>\n",
-       "      <td>0.390000</td>\n",
+       "      <td>0.005500</td>\n",
+       "      <td>0.346000</td>\n",
        "      <td>0.00250</td>\n",
-       "      <td>0.308000</td>\n",
-       "      <td>0.197500</td>\n",
+       "      <td>0.306000</td>\n",
+       "      <td>0.181500</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.049000</td>\n",
-       "      <td>0.024000</td>\n",
-       "      <td>44.923000</td>\n",
-       "      <td>0.270500</td>\n",
+       "      <td>0.04550</td>\n",
+       "      <td>0.020500</td>\n",
+       "      <td>42.191500</td>\n",
+       "      <td>0.253500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>14.300566</td>\n",
-       "      <td>612.116292</td>\n",
-       "      <td>1.874709</td>\n",
-       "      <td>5.852797</td>\n",
-       "      <td>0.857009</td>\n",
-       "      <td>6.910334</td>\n",
-       "      <td>2.972607</td>\n",
-       "      <td>14.300566</td>\n",
+       "      <td>10.594584</td>\n",
+       "      <td>525.37318</td>\n",
+       "      <td>1.345882</td>\n",
+       "      <td>8.883453</td>\n",
+       "      <td>0.406942</td>\n",
+       "      <td>3.193977</td>\n",
+       "      <td>3.036406</td>\n",
+       "      <td>10.594584</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.059072</td>\n",
-       "      <td>0.487872</td>\n",
+       "      <td>0.073976</td>\n",
+       "      <td>0.475812</td>\n",
        "      <td>0.04995</td>\n",
-       "      <td>0.461782</td>\n",
-       "      <td>0.398212</td>\n",
+       "      <td>0.460945</td>\n",
+       "      <td>0.385528</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.215922</td>\n",
-       "      <td>0.354241</td>\n",
-       "      <td>60.920298</td>\n",
-       "      <td>0.444329</td>\n",
+       "      <td>0.20845</td>\n",
+       "      <td>0.219326</td>\n",
+       "      <td>60.283662</td>\n",
+       "      <td>0.435123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -292,7 +342,7 @@
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -300,7 +350,7 @@
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>39.000000</td>\n",
+       "      <td>38.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -316,7 +366,7 @@
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>5.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -324,7 +374,7 @@
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.000000</td>\n",
+       "      <td>98.50000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -340,15 +390,15 @@
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>21.000000</td>\n",
+       "      <td>14.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>2.000000</td>\n",
-       "      <td>285.500000</td>\n",
+       "      <td>307.25000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -364,21 +414,21 @@
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>62.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>420.000000</td>\n",
-       "      <td>12712.000000</td>\n",
-       "      <td>48.000000</td>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>35.000000</td>\n",
-       "      <td>230.000000</td>\n",
+       "      <td>182.000000</td>\n",
+       "      <td>7457.00000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>179.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>79.000000</td>\n",
        "      <td>8.000000</td>\n",
-       "      <td>420.000000</td>\n",
+       "      <td>182.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
@@ -388,9 +438,9 @@
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>747.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>567.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -399,50 +449,50 @@
        "</div>"
       ],
       "text/plain": [
-       "             col_0         col_1        col_2        col_3        col_4  \\\n",
-       "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
-       "mean      3.092500    282.785500     0.315000     2.130000     0.073500   \n",
-       "std      14.300566    612.116292     1.874709     5.852797     0.857009   \n",
-       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
-       "25%       0.000000     39.000000     0.000000     0.000000     0.000000   \n",
-       "50%       0.000000    100.000000     0.000000     1.000000     0.000000   \n",
-       "75%       2.000000    285.500000     0.000000     2.000000     0.000000   \n",
-       "max     420.000000  12712.000000    48.000000   105.000000    35.000000   \n",
+       "             col_0       col_1        col_2        col_3        col_4  \\\n",
+       "count  2000.000000  2000.00000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      3.089000   284.03150     0.277500     2.346000     0.059000   \n",
+       "std      10.594584   525.37318     1.345882     8.883453     0.406942   \n",
+       "min       0.000000     0.00000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000    38.00000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000    98.50000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000   307.25000     0.000000     2.000000     0.000000   \n",
+       "max     182.000000  7457.00000    25.000000   179.000000    11.000000   \n",
        "\n",
        "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
        "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
-       "mean      1.112500     2.355000     3.092500     0.0     0.0  ...   \n",
-       "std       6.910334     2.972607    14.300566     0.0     0.0  ...   \n",
+       "mean      0.885500     2.543000     3.089000     0.0     0.0  ...   \n",
+       "std       3.193977     3.036406    10.594584     0.0     0.0  ...   \n",
        "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
        "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
        "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
        "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
-       "max     230.000000     8.000000   420.000000     0.0     0.0  ...   \n",
+       "max      79.000000     8.000000   182.000000     0.0     0.0  ...   \n",
        "\n",
        "           col_103      col_104     col_105      col_106      col_107  \\\n",
        "count  2000.000000  2000.000000  2000.00000  2000.000000  2000.000000   \n",
-       "mean      0.003500     0.390000     0.00250     0.308000     0.197500   \n",
-       "std       0.059072     0.487872     0.04995     0.461782     0.398212   \n",
+       "mean      0.005500     0.346000     0.00250     0.306000     0.181500   \n",
+       "std       0.073976     0.475812     0.04995     0.460945     0.385528   \n",
        "min       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
        "25%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
        "50%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
        "75%       0.000000     1.000000     0.00000     1.000000     0.000000   \n",
        "max       1.000000     1.000000     1.00000     1.000000     1.000000   \n",
        "\n",
-       "       col_108      col_109      col_110      col_111      targets  \n",
-       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
-       "mean       0.0     0.049000     0.024000    44.923000     0.270500  \n",
-       "std        0.0     0.215922     0.354241    60.920298     0.444329  \n",
-       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
-       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
-       "50%        0.0     0.000000     0.000000    21.000000     0.000000  \n",
-       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
-       "max        0.0     1.000000     9.000000   747.000000     1.000000  \n",
+       "       col_108     col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.00000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.04550     0.020500    42.191500     0.253500  \n",
+       "std        0.0     0.20845     0.219326    60.283662     0.435123  \n",
+       "min        0.0     0.00000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.00000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.00000     0.000000    14.000000     0.000000  \n",
+       "75%        0.0     0.00000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.00000     5.000000   567.000000     1.000000  \n",
        "\n",
        "[8 rows x 113 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -451,10 +501,97 @@
     "data.view_info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "97307aad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing LDA...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "35b2cb3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'preprocessed' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/1375028561.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview_preprocessed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/3634425272.py\u001b[0m in \u001b[0;36mview_preprocessed\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mview_preprocessed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mpreprocessed\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Please do some preprocessing first.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'preprocessed' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "data.view_preprocessed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33ca589b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7073e47c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "804fc23c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81ab7ee2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9bb47bd2",
+   "id": "c00fde6e",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/QML4FRAUD/.ipynb_checkpoints/Solution via architecture-checkpoint.ipynb b/QML4FRAUD/.ipynb_checkpoints/Solution via architecture-checkpoint.ipynb
new file mode 100644
index 0000000..a0d549e
--- /dev/null
+++ b/QML4FRAUD/.ipynb_checkpoints/Solution via architecture-checkpoint.ipynb	
@@ -0,0 +1,1421 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f66fdd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\_distutils_hack\\__init__.py:30: UserWarning: Setuptools is replacing distutils.\n",
+      "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pennylane import numpy as np\n",
+    "from Factory.data import PrepareData\n",
+    "from Factory.model import QBC\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0344a9",
+   "metadata": {},
+   "source": [
+    "# Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3722cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b9722cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = PrepareData(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a48959d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "No preprocessing done yet.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
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+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00c69d2",
+   "metadata": {},
+   "source": [
+    "# Use LDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "383f00dc",
+   "metadata": {},
+   "source": [
+    "Preprocess LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd8fb0b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing LDA...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d64e1dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "Preprocessing done via:  LDA\n"
+     ]
+    },
+    {
+     "data": {
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+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9020cf",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "488ed7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_LDA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f59a837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7146527 | Accuracy: 0.7557143 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.5467249 | Accuracy: 0.8171429 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.4751983 | Accuracy: 0.8307143 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.4734549 | Accuracy: 0.8385714 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.4472145 | Accuracy: 0.8471429 \n",
+      "New best\n",
+      "Iter:     6 | Cost: 0.4475311 | Accuracy: 0.8478571 \n",
+      "Iter:     7 | Cost: 0.6056652 | Accuracy: 0.8064286 \n",
+      "Iter:     8 | Cost: 0.5346172 | Accuracy: 0.8457143 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.4346356 | Accuracy: 0.8571429 \n",
+      "Iter:    10 | Cost: 0.4457327 | Accuracy: 0.8528571 \n",
+      "Iter:    11 | Cost: 0.5464202 | Accuracy: 0.8464286 \n",
+      "Iter:    12 | Cost: 0.5721968 | Accuracy: 0.8464286 \n",
+      "Iter:    13 | Cost: 0.5576279 | Accuracy: 0.8442857 \n",
+      "Iter:    14 | Cost: 0.5400980 | Accuracy: 0.8435714 \n",
+      "Iter:    15 | Cost: 0.5297521 | Accuracy: 0.8457143 \n",
+      "Iter:    16 | Cost: 0.5273266 | Accuracy: 0.8471429 \n",
+      "Iter:    17 | Cost: 0.4711755 | Accuracy: 0.8442857 \n",
+      "Iter:    18 | Cost: 0.4586101 | Accuracy: 0.8492857 \n",
+      "Iter:    19 | Cost: 0.4710555 | Accuracy: 0.8471429 \n",
+      "Iter:    20 | Cost: 0.4496019 | Accuracy: 0.8564286 \n",
+      "New best\n",
+      "Iter:    21 | Cost: 0.4334069 | Accuracy: 0.8628571 \n",
+      "New best\n",
+      "Iter:    22 | Cost: 0.4350645 | Accuracy: 0.8635714 \n",
+      "New best\n",
+      "Iter:    23 | Cost: 0.4307490 | Accuracy: 0.8678571 \n",
+      "New best\n",
+      "Iter:    24 | Cost: 0.4142857 | Accuracy: 0.8728571 \n",
+      "New best\n",
+      "Iter:    25 | Cost: 0.4104879 | Accuracy: 0.8757143 \n",
+      "Iter:    26 | Cost: 0.4025811 | Accuracy: 0.8750000 \n",
+      "New best\n",
+      "Iter:    27 | Cost: 0.3971891 | Accuracy: 0.8778571 \n",
+      "Iter:    28 | Cost: 0.3814916 | Accuracy: 0.8757143 \n",
+      "Iter:    29 | Cost: 0.3685935 | Accuracy: 0.8757143 \n",
+      "Iter:    30 | Cost: 0.3622557 | Accuracy: 0.8757143 \n",
+      "Iter:    31 | Cost: 0.3592110 | Accuracy: 0.8742857 \n",
+      "Iter:    32 | Cost: 0.3595640 | Accuracy: 0.8778571 \n",
+      "New best\n",
+      "Iter:    33 | Cost: 0.3677971 | Accuracy: 0.8785714 \n",
+      "Iter:    34 | Cost: 0.4011392 | Accuracy: 0.8771429 \n",
+      "Iter:    35 | Cost: 0.4123842 | Accuracy: 0.8742857 \n",
+      "New best\n",
+      "Iter:    36 | Cost: 0.4398058 | Accuracy: 0.8807143 \n",
+      "Iter:    37 | Cost: 0.4797217 | Accuracy: 0.8700000 \n",
+      "Iter:    38 | Cost: 0.5145149 | Accuracy: 0.8550000 \n",
+      "Iter:    39 | Cost: 0.5033828 | Accuracy: 0.8600000 \n",
+      "Iter:    40 | Cost: 0.4766182 | Accuracy: 0.8792857 \n",
+      "New best\n",
+      "Iter:    41 | Cost: 0.4339206 | Accuracy: 0.8814286 \n",
+      "Iter:    42 | Cost: 0.4180167 | Accuracy: 0.8757143 \n",
+      "Iter:    43 | Cost: 0.3960513 | Accuracy: 0.8721429 \n",
+      "Iter:    44 | Cost: 0.3896418 | Accuracy: 0.8714286 \n",
+      "Iter:    45 | Cost: 0.3829375 | Accuracy: 0.8714286 \n",
+      "Iter:    46 | Cost: 0.3750100 | Accuracy: 0.8728571 \n",
+      "Iter:    47 | Cost: 0.3678940 | Accuracy: 0.8742857 \n",
+      "Iter:    48 | Cost: 0.3640737 | Accuracy: 0.8764286 \n",
+      "Iter:    49 | Cost: 0.3642824 | Accuracy: 0.8785714 \n",
+      "Iter:    50 | Cost: 0.3671699 | Accuracy: 0.8814286 \n"
+     ]
+    }
+   ],
+   "source": [
+    "model_LDA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384518e",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "572a4735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, X_test, Y_test = data.get_preprocessed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3b26df72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_LDA = model_LDA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ce01db5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.97      0.89      0.93       497\n",
+      "         1.0       0.63      0.87      0.73       103\n",
+      "\n",
+      "    accuracy                           0.89       600\n",
+      "   macro avg       0.80      0.88      0.83       600\n",
+      "weighted avg       0.91      0.89      0.90       600\n",
+      "\n",
+      "0.6293706293706294\n",
+      "0.8737864077669902\n",
+      "0.7317073170731707\n",
+      "0.8835732843663925\n",
+      "[[444  53]\n",
+      " [ 13  90]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
+    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
+    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
+    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ea9f5b",
+   "metadata": {},
+   "source": [
+    "# Use PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e6c02d",
+   "metadata": {},
+   "source": [
+    "Preprocess PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9f2da505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing PCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_PCA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d722a49c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "Preprocessing done via:  PCA\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df4d46d",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "df69026d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PCA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "401edaee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.9922963 | Accuracy: 0.6050000 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.9133256 | Accuracy: 0.7357143 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.8372795 | Accuracy: 0.7428571 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.8082843 | Accuracy: 0.7464286 \n",
+      "Iter:     5 | Cost: 0.8039261 | Accuracy: 0.7464286 \n",
+      "Iter:     6 | Cost: 0.8042474 | Accuracy: 0.7464286 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.8297579 | Accuracy: 0.7471429 \n",
+      "Iter:     8 | Cost: 0.8814247 | Accuracy: 0.7471429 \n",
+      "Iter:     9 | Cost: 0.9090855 | Accuracy: 0.7471429 \n",
+      "Iter:    10 | Cost: 0.9006644 | Accuracy: 0.7471429 \n",
+      "Iter:    11 | Cost: 0.8605321 | Accuracy: 0.7471429 \n",
+      "Iter:    12 | Cost: 0.8280091 | Accuracy: 0.7471429 \n",
+      "Iter:    13 | Cost: 0.7825952 | Accuracy: 0.7471429 \n",
+      "Iter:    14 | Cost: 0.7447115 | Accuracy: 0.7471429 \n",
+      "Iter:    15 | Cost: 0.7485581 | Accuracy: 0.7471429 \n",
+      "Iter:    16 | Cost: 0.8402239 | Accuracy: 0.7278571 \n",
+      "Iter:    17 | Cost: 0.8835448 | Accuracy: 0.6978571 \n",
+      "Iter:    18 | Cost: 0.9145426 | Accuracy: 0.3971429 \n",
+      "Iter:    19 | Cost: 0.8670825 | Accuracy: 0.7150000 \n",
+      "Iter:    20 | Cost: 0.7791539 | Accuracy: 0.7428571 \n",
+      "Iter:    21 | Cost: 0.7432145 | Accuracy: 0.7471429 \n",
+      "Iter:    22 | Cost: 0.7460729 | Accuracy: 0.7471429 \n",
+      "Iter:    23 | Cost: 0.7734032 | Accuracy: 0.7471429 \n",
+      "Iter:    24 | Cost: 0.7911368 | Accuracy: 0.7471429 \n",
+      "Iter:    25 | Cost: 0.8098764 | Accuracy: 0.7471429 \n",
+      "Iter:    26 | Cost: 0.8396121 | Accuracy: 0.7471429 \n",
+      "Iter:    27 | Cost: 0.8571642 | Accuracy: 0.7471429 \n",
+      "Iter:    28 | Cost: 0.8267991 | Accuracy: 0.7471429 \n",
+      "Iter:    29 | Cost: 0.7796929 | Accuracy: 0.7464286 \n",
+      "Iter:    30 | Cost: 0.7718953 | Accuracy: 0.7464286 \n",
+      "Iter:    31 | Cost: 0.7747632 | Accuracy: 0.7464286 \n",
+      "Iter:    32 | Cost: 0.7990050 | Accuracy: 0.7464286 \n",
+      "Iter:    33 | Cost: 0.8508553 | Accuracy: 0.7357143 \n",
+      "Iter:    34 | Cost: 0.8222431 | Accuracy: 0.7435714 \n",
+      "Iter:    35 | Cost: 0.7661793 | Accuracy: 0.7471429 \n",
+      "Iter:    36 | Cost: 0.7379706 | Accuracy: 0.7471429 \n",
+      "Iter:    37 | Cost: 0.7647438 | Accuracy: 0.7471429 \n",
+      "Iter:    38 | Cost: 0.8199030 | Accuracy: 0.7471429 \n",
+      "Iter:    39 | Cost: 0.8230046 | Accuracy: 0.7471429 \n",
+      "Iter:    40 | Cost: 0.8318804 | Accuracy: 0.7471429 \n",
+      "Iter:    41 | Cost: 0.8325269 | Accuracy: 0.7471429 \n",
+      "Iter:    42 | Cost: 0.8229071 | Accuracy: 0.7471429 \n",
+      "Iter:    43 | Cost: 0.7878330 | Accuracy: 0.7471429 \n",
+      "Iter:    44 | Cost: 0.7527745 | Accuracy: 0.7471429 \n",
+      "Iter:    45 | Cost: 0.7394627 | Accuracy: 0.7471429 \n",
+      "Iter:    46 | Cost: 0.7763476 | Accuracy: 0.7471429 \n",
+      "Iter:    47 | Cost: 0.8037952 | Accuracy: 0.7464286 \n",
+      "Iter:    48 | Cost: 0.7987263 | Accuracy: 0.7464286 \n",
+      "Iter:    49 | Cost: 0.7850808 | Accuracy: 0.7464286 \n",
+      "Iter:    50 | Cost: 0.7664157 | Accuracy: 0.7471429 \n"
+     ]
+    }
+   ],
+   "source": [
+    "model_PCA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb757835",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "700192cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_PCA = model_PCA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0a3f0a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.91      0.84      0.88       497\n",
+      "         1.0       0.45      0.62      0.52       103\n",
+      "\n",
+      "    accuracy                           0.80       600\n",
+      "   macro avg       0.68      0.73      0.70       600\n",
+      "weighted avg       0.83      0.80      0.82       600\n",
+      "\n",
+      "0.44755244755244755\n",
+      "0.6213592233009708\n",
+      "0.5203252032520325\n",
+      "0.7312027504834834\n",
+      "[[418  79]\n",
+      " [ 39  64]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(metrics.classification_report(predictions_PCA,Y_test))\n",
+    "print(metrics.precision_score(predictions_PCA,Y_test))\n",
+    "print(metrics.recall_score(predictions_PCA,Y_test))\n",
+    "print(metrics.f1_score(predictions_PCA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_PCA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_PCA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58662136",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QML4FRAUD/Architecture.ipynb b/QML4FRAUD/Architecture.ipynb
index 3f95b7d..f6d5124 100644
--- a/QML4FRAUD/Architecture.ipynb
+++ b/QML4FRAUD/Architecture.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d9190faa",
+   "id": "6f465949",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,14 +25,13 @@
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
     "\n",
-    "\n",
     "import time"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "c2c45f75",
+   "execution_count": 74,
+   "id": "9fa6d32d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,15 +61,33 @@
     "            print(\"Preprocessing done via: \", self.preprocess_done)\n",
     "        return self.data_sample.describe()\n",
     "    \n",
+    "    def get_preprocessed(self, to_show = False):\n",
+    "        if self.preprocess_done == None:\n",
+    "            print(\"Please do some preprocessing first.\")\n",
+    "        else:\n",
+    "            \n",
+    "            if to_show:\n",
+    "                print(\"Training Set and Labels: \")\n",
+    "                print(self.train_X_preprocessed)\n",
+    "                print(self.train_Y_preprocessed)\n",
+    "\n",
+    "                print(\"Test Set and Labels: \")\n",
+    "                print(self.test_X_preprocessed)\n",
+    "                print(self.test_Y_preprocessed)\n",
+    "            \n",
+    "            return self.train_X_preprocessed, self.train_Y_preprocessed, self.test_X_preprocessed, self.test_Y_preprocessed\n",
+    "    \n",
     "    def perform_LDA(self, n_dim = 2):\n",
     "        \n",
     "        self.preprocess_done = \"LDA\"\n",
+    "        print(\"Performing LDA...\")\n",
     "        \n",
     "        length = len(self.x_train.columns)\n",
     "        split_feature = int(length/n_dim)\n",
     "        features_train = []\n",
     "        features_test = []\n",
     "        \n",
+    "        # Split Features (for Yaqi to change)\n",
     "        for i in range(n_dim):\n",
     "            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
     "            features_train.append(new_set_train)\n",
@@ -78,6 +95,7 @@
     "            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]\n",
     "            features_test.append(new_set_test)\n",
     "        \n",
+    "        # Run the LDA\n",
     "        lda = LDA(n_components= (n_dim - 1))\n",
     "        features_lda_train = []\n",
     "        features_lda_test = []\n",
@@ -88,38 +106,72 @@
     "            \n",
     "            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)\n",
     "            features_lda_test.append(pd.DataFrame(features_lda_test_new))\n",
-    "            \n",
-    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
-    "        X = np.array(data, requires_grad=False)\n",
     "        \n",
+    "        x_train_data = features_lda_train[0]\n",
+    "        x_test_data = features_lda_test[0]\n",
+    "        \n",
+    "        # Join the results together\n",
+    "        for i in range(1, n_dim):\n",
+    "            l_suffix = \"_\" + str(i)\n",
+    "            r_suffix = \"_\" + str(i+1)\n",
+    "            x_train_data = x_train_data.join(features_lda_train[i], lsuffix=l_suffix, rsuffix=r_suffix)\n",
+    "            x_test_data = x_test_data.join(features_lda_test[i], lsuffix=l_suffix, rsuffix=r_suffix)\n",
     "        \n",
+    "        # Normalize\n",
+    "        std_scale_train = StandardScaler().fit(x_train_data)\n",
+    "        x_train_data = std_scale_train.transform(x_train_data)\n",
+    "        \n",
+    "        std_scale_test = StandardScaler().fit(x_test_data)\n",
+    "        x_test_data = std_scale_test.transform(x_test_data)\n",
     "            \n",
+    "        # shift label from {0, 1} to {-1, 1}\n",
+    "        self.train_X_preprocessed = np.array(x_train_data, requires_grad=False)\n",
+    "        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)\n",
+    "        \n",
+    "        self.test_X_preprocessed = np.array(x_test_data, requires_grad=False)\n",
+    "        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)\n",
+    "        \n",
     "    def perform_PCA(self, n_dim = 2):\n",
     "        \n",
     "        self.preprocess_done = \"PCA\"\n",
+    "        print(\"Performing PCA...\")\n",
     "        \n",
-    "        y_train.value_counts(normalize=True)*100\n",
-    "        y_test.value_counts(normalize=True)*100\n",
+    "        self.y_train.value_counts(normalize=True)*100\n",
+    "        self.y_test.value_counts(normalize=True)*100\n",
     "        \n",
     "        pca = PCA(n_components=n_dim, svd_solver='full')\n",
-    "        pca.fit(x_train)\n",
-    "        x_train_pca = pca.transform(x_train)\n",
-    "        pca.fit(x_test)\n",
-    "        x_test = pca.transform(x_test)\n",
+    "        pca.fit(self.x_train)\n",
+    "        x_train_pca = pca.transform(self.x_train)\n",
+    "        pca.fit(self.x_test)\n",
+    "        x_test_pca = pca.transform(self.x_test)\n",
+    "        \n",
+    "        train_X_preprocessed = normalize(x_train_pca)\n",
+    "        test_X_preprocessed = normalize(x_test_pca)\n",
+    "        \n",
+    "        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        self.train_X_preprocessed = np.array(train_X_preprocessed, requires_grad=False)\n",
+    "        \n",
+    "        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "        self.test_X_preprocessed = np.array(test_X_preprocessed, requires_grad=False)\n",
+    "        \n",
+    "    def perform_normalize(self, n_dim = 2):\n",
+    "        \n",
+    "        self.preprocess_done = \"Normalize\"\n",
+    "        print(\"Performing Normalize...\")\n",
     "        \n",
-    "        data = normalize(x_train_pca)\n",
-    "        x_test = normalize(x_test)\n",
+    "        self.y_train.value_counts(normalize=True)*100\n",
+    "        self.y_test.value_counts(normalize=True)*100\n",
     "        \n",
-    "        Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
-    "        X = np.array(data, requires_grad=False)\n",
+    "        self.x_train.value_counts(normalize=True)*100\n",
+    "        self.x_test.value_counts(normalize=True)*100\n",
     "        \n",
     "        "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "4393f82c",
+   "execution_count": 75,
+   "id": "12676440",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,18 +182,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "7f48c0e4",
+   "execution_count": 76,
+   "id": "fb2bfa38",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = Data(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+    "data = PrepareData(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "dddfb6b3",
+   "execution_count": 77,
+   "id": "34f2e357",
    "metadata": {},
    "outputs": [
     {
@@ -149,11 +201,12 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 9695 to 4714\n",
+      "Int64Index: 2000 entries, 10272 to 19363\n",
       "Columns: 113 entries, col_0 to targets\n",
       "dtypes: float64(113)\n",
       "memory usage: 1.7 MB\n",
-      "None\n"
+      "None\n",
+      "No preprocessing done yet.\n"
      ]
     },
     {
@@ -216,62 +269,62 @@
        "      <td>...</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
        "      <td>2000.000000</td>\n",
        "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
        "      <td>2000.000000</td>\n",
+       "      <td>2000.0000</td>\n",
        "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>3.092500</td>\n",
-       "      <td>282.785500</td>\n",
-       "      <td>0.315000</td>\n",
-       "      <td>2.130000</td>\n",
-       "      <td>0.073500</td>\n",
-       "      <td>1.112500</td>\n",
-       "      <td>2.355000</td>\n",
-       "      <td>3.092500</td>\n",
+       "      <td>3.013000</td>\n",
+       "      <td>309.904500</td>\n",
+       "      <td>0.886500</td>\n",
+       "      <td>2.378000</td>\n",
+       "      <td>0.095500</td>\n",
+       "      <td>1.066000</td>\n",
+       "      <td>2.440000</td>\n",
+       "      <td>3.013000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.003500</td>\n",
-       "      <td>0.390000</td>\n",
-       "      <td>0.00250</td>\n",
-       "      <td>0.308000</td>\n",
+       "      <td>0.011000</td>\n",
+       "      <td>0.387500</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.318000</td>\n",
        "      <td>0.197500</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.049000</td>\n",
-       "      <td>0.024000</td>\n",
-       "      <td>44.923000</td>\n",
-       "      <td>0.270500</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.0250</td>\n",
+       "      <td>47.011500</td>\n",
+       "      <td>0.26400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>14.300566</td>\n",
-       "      <td>612.116292</td>\n",
-       "      <td>1.874709</td>\n",
-       "      <td>5.852797</td>\n",
-       "      <td>0.857009</td>\n",
-       "      <td>6.910334</td>\n",
-       "      <td>2.972607</td>\n",
-       "      <td>14.300566</td>\n",
+       "      <td>14.092556</td>\n",
+       "      <td>893.336008</td>\n",
+       "      <td>20.937306</td>\n",
+       "      <td>8.773574</td>\n",
+       "      <td>1.135793</td>\n",
+       "      <td>6.593283</td>\n",
+       "      <td>3.051894</td>\n",
+       "      <td>14.092556</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.059072</td>\n",
-       "      <td>0.487872</td>\n",
-       "      <td>0.04995</td>\n",
-       "      <td>0.461782</td>\n",
+       "      <td>0.113514</td>\n",
+       "      <td>0.487301</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.465816</td>\n",
        "      <td>0.398212</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.215922</td>\n",
-       "      <td>0.354241</td>\n",
-       "      <td>60.920298</td>\n",
-       "      <td>0.444329</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.3007</td>\n",
+       "      <td>64.113467</td>\n",
+       "      <td>0.44091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -288,19 +341,19 @@
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>39.000000</td>\n",
+       "      <td>37.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -312,19 +365,19 @@
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>5.000000</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>100.000000</td>\n",
+       "      <td>97.500000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -336,19 +389,19 @@
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>21.000000</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>2.000000</td>\n",
-       "      <td>285.500000</td>\n",
+       "      <td>286.250000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -360,38 +413,38 @@
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>62.000000</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>420.000000</td>\n",
-       "      <td>12712.000000</td>\n",
-       "      <td>48.000000</td>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>35.000000</td>\n",
+       "      <td>490.000000</td>\n",
+       "      <td>25927.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>235.000000</td>\n",
+       "      <td>40.000000</td>\n",
        "      <td>230.000000</td>\n",
        "      <td>8.000000</td>\n",
-       "      <td>420.000000</td>\n",
+       "      <td>490.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.00000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>747.000000</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>11.0000</td>\n",
+       "      <td>728.000000</td>\n",
+       "      <td>1.00000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -401,48 +454,48 @@
       "text/plain": [
        "             col_0         col_1        col_2        col_3        col_4  \\\n",
        "count  2000.000000   2000.000000  2000.000000  2000.000000  2000.000000   \n",
-       "mean      3.092500    282.785500     0.315000     2.130000     0.073500   \n",
-       "std      14.300566    612.116292     1.874709     5.852797     0.857009   \n",
+       "mean      3.013000    309.904500     0.886500     2.378000     0.095500   \n",
+       "std      14.092556    893.336008    20.937306     8.773574     1.135793   \n",
        "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
-       "25%       0.000000     39.000000     0.000000     0.000000     0.000000   \n",
-       "50%       0.000000    100.000000     0.000000     1.000000     0.000000   \n",
-       "75%       2.000000    285.500000     0.000000     2.000000     0.000000   \n",
-       "max     420.000000  12712.000000    48.000000   105.000000    35.000000   \n",
+       "25%       0.000000     37.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     97.500000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    286.250000     0.000000     2.000000     0.000000   \n",
+       "max     490.000000  25927.000000   904.000000   235.000000    40.000000   \n",
        "\n",
        "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
        "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
-       "mean      1.112500     2.355000     3.092500     0.0     0.0  ...   \n",
-       "std       6.910334     2.972607    14.300566     0.0     0.0  ...   \n",
+       "mean      1.066000     2.440000     3.013000     0.0     0.0  ...   \n",
+       "std       6.593283     3.051894    14.092556     0.0     0.0  ...   \n",
        "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
        "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
        "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
        "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
-       "max     230.000000     8.000000   420.000000     0.0     0.0  ...   \n",
+       "max     230.000000     8.000000   490.000000     0.0     0.0  ...   \n",
        "\n",
-       "           col_103      col_104     col_105      col_106      col_107  \\\n",
-       "count  2000.000000  2000.000000  2000.00000  2000.000000  2000.000000   \n",
-       "mean      0.003500     0.390000     0.00250     0.308000     0.197500   \n",
-       "std       0.059072     0.487872     0.04995     0.461782     0.398212   \n",
-       "min       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
-       "25%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
-       "50%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
-       "75%       0.000000     1.000000     0.00000     1.000000     0.000000   \n",
-       "max       1.000000     1.000000     1.00000     1.000000     1.000000   \n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.011000     0.387500     0.001000     0.318000     0.197500   \n",
+       "std       0.113514     0.487301     0.031615     0.465816     0.398212   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
        "\n",
-       "       col_108      col_109      col_110      col_111      targets  \n",
-       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
-       "mean       0.0     0.049000     0.024000    44.923000     0.270500  \n",
-       "std        0.0     0.215922     0.354241    60.920298     0.444329  \n",
-       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
-       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
-       "50%        0.0     0.000000     0.000000    21.000000     0.000000  \n",
-       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
-       "max        0.0     1.000000     9.000000   747.000000     1.000000  \n",
+       "       col_108      col_109    col_110      col_111     targets  \n",
+       "count   2000.0  2000.000000  2000.0000  2000.000000  2000.00000  \n",
+       "mean       0.0     0.050500     0.0250    47.011500     0.26400  \n",
+       "std        0.0     0.219029     0.3007    64.113467     0.44091  \n",
+       "min        0.0     0.000000     0.0000     0.000000     0.00000  \n",
+       "25%        0.0     0.000000     0.0000     6.000000     0.00000  \n",
+       "50%        0.0     0.000000     0.0000    22.000000     0.00000  \n",
+       "75%        0.0     0.000000     0.0000    62.000000     1.00000  \n",
+       "max        0.0     1.000000    11.0000   728.000000     1.00000  \n",
        "\n",
        "[8 rows x 113 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -451,24 +504,197 @@
     "data.view_info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "b1f6a7bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing PCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "c856f748",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set and Labels: \n",
+      "[[-0.99360793  0.11288612]\n",
+      " [-0.99992919 -0.01190007]\n",
+      " [-0.99991751 -0.01284414]\n",
+      " ...\n",
+      " [-0.99991742 -0.01285143]\n",
+      " [-0.49123299  0.87102821]\n",
+      " [-0.99991403 -0.01311251]]\n",
+      "[-1. -1. -1. ...  1. -1. -1.]\n",
+      "Test Set and Labels: \n",
+      "[[-0.99992372 -0.01235157]\n",
+      " [-0.99988422 -0.01521681]\n",
+      " [-0.99992729 -0.01205916]\n",
+      " ...\n",
+      " [ 0.99999839 -0.00179606]\n",
+      " [-0.9999273  -0.01205789]\n",
+      " [-0.99992564 -0.01219491]]\n",
+      "[-1. -1.  1. -1.  1. -1.  1.  1. -1.  1.  1. -1. -1. -1. -1.  1.  1. -1.\n",
+      " -1. -1. -1.  1. -1. -1. -1.  1. -1.  1.  1. -1. -1. -1. -1.  1. -1. -1.\n",
+      " -1. -1. -1. -1. -1. -1.  1.  1. -1.  1. -1. -1.  1. -1. -1. -1. -1.  1.\n",
+      " -1. -1. -1. -1.  1.  1. -1. -1. -1. -1. -1. -1.  1. -1. -1.  1. -1. -1.\n",
+      "  1.  1.  1.  1.  1. -1. -1. -1.  1. -1.  1. -1.  1. -1. -1. -1.  1. -1.\n",
+      "  1. -1.  1.  1. -1. -1. -1. -1. -1.  1. -1. -1.  1. -1. -1. -1. -1.  1.\n",
+      " -1. -1. -1.  1. -1. -1. -1. -1.  1.  1. -1. -1.  1.  1.  1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.  1.  1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1.  1.\n",
+      " -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.  1.  1.  1.\n",
+      " -1. -1.  1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1.  1. -1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1. -1. -1. -1. -1.  1.  1.  1.  1.  1. -1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.  1.  1.  1.  1. -1. -1. -1.\n",
+      " -1. -1. -1. -1.  1.  1. -1. -1.  1.  1. -1.  1. -1. -1. -1. -1. -1. -1.\n",
+      "  1. -1.  1.  1. -1. -1.  1.  1. -1.  1.  1.  1. -1. -1. -1.  1. -1. -1.\n",
+      "  1. -1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1.\n",
+      " -1.  1.  1. -1.  1. -1. -1. -1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1.\n",
+      "  1. -1.  1. -1. -1. -1. -1. -1. -1.  1.  1. -1. -1.  1. -1. -1. -1. -1.\n",
+      "  1. -1. -1. -1.  1. -1. -1.  1. -1.  1. -1. -1. -1.  1. -1.  1. -1.  1.\n",
+      "  1.  1. -1.  1. -1. -1. -1. -1. -1. -1. -1. -1. -1.  1. -1.  1. -1. -1.\n",
+      "  1.  1. -1. -1. -1.  1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.  1.\n",
+      " -1.  1. -1. -1.  1. -1. -1. -1.  1. -1. -1. -1.  1.  1. -1. -1. -1. -1.\n",
+      "  1. -1.  1. -1. -1. -1. -1. -1. -1. -1.  1.  1. -1. -1. -1. -1. -1. -1.\n",
+      " -1. -1.  1. -1.  1. -1. -1. -1. -1. -1. -1.  1. -1. -1. -1.  1. -1.  1.\n",
+      " -1.  1. -1. -1.  1.  1. -1. -1. -1.  1.  1.  1.  1. -1.  1. -1.  1. -1.\n",
+      " -1. -1. -1. -1. -1. -1.  1.  1. -1. -1.  1. -1. -1.  1.  1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1.  1. -1. -1. -1. -1.  1. -1.  1.  1. -1.  1. -1. -1.\n",
+      " -1.  1.  1. -1. -1.  1. -1. -1. -1. -1. -1. -1.  1.  1. -1. -1. -1.  1.\n",
+      " -1. -1. -1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1. -1.  1. -1. -1.\n",
+      " -1. -1. -1.  1.  1. -1. -1. -1. -1.  1. -1.  1. -1. -1. -1.  1. -1. -1.\n",
+      " -1.  1.  1. -1. -1.  1. -1. -1.  1.  1. -1. -1. -1.  1. -1. -1. -1. -1.\n",
+      "  1. -1.  1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1. -1. -1. -1. -1.\n",
+      " -1. -1. -1. -1. -1. -1. -1.  1. -1. -1. -1. -1. -1. -1. -1.  1.  1.  1.\n",
+      " -1.  1. -1. -1. -1. -1.]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[-0.99360793,  0.11288612],\n",
+       "         [-0.99992919, -0.01190007],\n",
+       "         [-0.99991751, -0.01284414],\n",
+       "         ...,\n",
+       "         [-0.99991742, -0.01285143],\n",
+       "         [-0.49123299,  0.87102821],\n",
+       "         [-0.99991403, -0.01311251]], requires_grad=False),\n",
+       " tensor([-1., -1., -1., ...,  1., -1., -1.], requires_grad=False),\n",
+       " tensor([[-0.99992372, -0.01235157],\n",
+       "         [-0.99988422, -0.01521681],\n",
+       "         [-0.99992729, -0.01205916],\n",
+       "         ...,\n",
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+       "         [-0.9999273 , -0.01205789],\n",
+       "         [-0.99992564, -0.01219491]], requires_grad=False),\n",
+       " tensor([-1., -1.,  1., -1.,  1., -1.,  1.,  1., -1.,  1.,  1., -1., -1.,\n",
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+       "         -1., -1.], requires_grad=False))"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.get_preprocessed(to_show = True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9bb47bd2",
+   "id": "1ef23506",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "5ce4e582",
    "metadata": {},
    "outputs": [],
    "source": [
     "class QBC:\n",
-    "    def __init__(self, Data, n_dim, n_layers, \n",
+    "    def __init__(self, data, n_dim, n_layers, \n",
     "                 optimizer = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08),\n",
-    "                 loss_function = None, batch_size = 10, backend = \"default.qubit\", shots = 0):\n",
+    "                 interface_type = \"autograd\",\n",
+    "                 loss_function = None,  backend = \"default.qubit\", shots = None):\n",
     "        \n",
     "        if loss_function == None:\n",
+    "            def square_loss(labels, predictions):\n",
+    "                loss = 0\n",
+    "                for l, p in zip(labels, predictions):\n",
+    "                    loss = loss + (l - p) ** 2\n",
+    "\n",
+    "                loss = loss / len(labels)\n",
+    "                return loss\n",
     "            self.loss_function = square_loss\n",
     "        else: \n",
     "            self.loss_function = loss_function\n",
     "        self.opt = optimizer\n",
-    "        self.batch_size = batch_size\n",
+    "        self.data = data\n",
     "        \n",
     "        self.n_dim = n_dim\n",
     "        \n",
@@ -479,48 +705,43 @@
     "\n",
     "        @qml.qnode(dev)\n",
     "        def circuit(parameters, data):\n",
-    "            for i in range(num_qubits):\n",
+    "            for i in range(n_dim):\n",
     "                qml.Hadamard(wires = i)\n",
     "\n",
-    "            AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "            AngleEmbedding(features = data, wires = range(self.n_dim), rotation = 'Y')\n",
     "\n",
-    "            qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "            qml.StronglyEntanglingLayers(weights = parameters, wires = range(self.n_dim))\n",
     "\n",
     "            return qml.expval(qml.PauliZ(0))\n",
     "        \n",
+    "        self.qlayer = qml.QNode(circuit, dev, interface=interface_type, diff_method='best')\n",
+    "        \n",
     "        self.n_layers = n_layers\n",
     "        self.weights = 0.01 * np.random.randn(self.n_layers, self.n_dim, 3, requires_grad=True)\n",
     "        self.bias = np.array(0.0, requires_grad=True)\n",
-    "        \n",
-    "    def variational_classifier(weights, bias, x):\n",
-    "        return circuit(weights, x) + bias\n",
-    "    \n",
-    "    def square_loss(labels, predictions):\n",
-    "        loss = 0\n",
-    "        for l, p in zip(labels, predictions):\n",
-    "            loss = loss + (l - p) ** 2\n",
     "\n",
-    "        loss = loss / len(labels)\n",
-    "        return loss\n",
-    "    \n",
-    "    def accuracy(labels, predictions):\n",
-    "\n",
-    "        loss = 0\n",
-    "        for l, p in zip(labels, predictions):\n",
-    "            if abs(l - p) < 1e-5:\n",
-    "                loss = loss + 1\n",
-    "        loss = loss / len(labels)\n",
-    "\n",
-    "        return loss\n",
-    "    \n",
-    "    def cost(weights, bias, X, Y):\n",
-    "        predictions = [variational_classifier(weights, bias, x) for x in X]\n",
-    "        return self.loss_function(Y, predictions)\n",
-    "\n",
-    "    def train(self, n_epochs):\n",
+    "    def variational_classifier(self, weights, bias, x):\n",
+    "        return self.qlayer(weights, x) + bias\n",
+    "        \n",
+    "    def train(self, batch_size = 10, n_epochs = 50):\n",
     "        wbest = 0\n",
     "        bbest = 0\n",
     "        abest = 0\n",
+    "        X, Y, _, _ = self.data.get_preprocessed()\n",
+    "        \n",
+    "        def cost(weights, bias, X, Y):\n",
+    "            predictions = [self.variational_classifier(weights, bias, x) for x in X]\n",
+    "            return self.loss_function(Y, predictions)\n",
+    "\n",
+    "        def accuracy(labels, predictions):\n",
+    "\n",
+    "            loss = 0\n",
+    "            for l, p in zip(labels, predictions):\n",
+    "                if abs(l - p) < 1e-5:\n",
+    "                    loss = loss + 1\n",
+    "            loss = loss / len(labels)\n",
+    "\n",
+    "            return loss\n",
     "\n",
     "        for it in range(n_epochs):\n",
     "\n",
@@ -529,14 +750,14 @@
     "            batch_index = np.random.randint(0, len(X), (batch_size,))\n",
     "            X_batch = X[batch_index]\n",
     "            Y_batch = Y[batch_index]\n",
-    "            weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "            self.weights, self.bias, _, _ = self.opt.step(cost, self.weights, self.bias, X_batch, Y_batch)\n",
     "\n",
     "            # Compute the accuracy\n",
-    "            predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "            predictions = [np.sign(self.variational_classifier(self.weights, self.bias, x)) for x in X]\n",
     "\n",
     "            if accuracy(Y, predictions) > abest:\n",
-    "                wbest = weights\n",
-    "                bbest = bias\n",
+    "                wbest = self.weights\n",
+    "                bbest = self.bias\n",
     "                abest = accuracy(Y, predictions)\n",
     "                print('New best')\n",
     "\n",
@@ -544,10 +765,72 @@
     "\n",
     "            print(\n",
     "                \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
-    "                    it + 1, cost(weights, bias, X, Y), acc\n",
+    "                    it + 1, cost(self.weights, self.bias, X, Y), acc\n",
     "                )\n",
-    "            )"
+    "            )\n",
+    "            \n",
+    "        self.weights = wbest\n",
+    "        self.bias = bbest\n",
+    "        \n",
+    "    def predict(self, test_data):\n",
+    "        predictions = [np.sign(self.variational_classifier(self.weights, self.bias, x)) for x in test_data]\n",
+    "        return predictions"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "a63a4b55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "37e6e796",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.8362042 | Accuracy: 0.7278571 \n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/1676823408.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/3881831539.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, batch_size, n_epochs)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m             \u001b[1;31m# Compute the accuracy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m             \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariational_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mabest\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/3881831539.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m             \u001b[1;31m# Compute the accuracy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m             \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariational_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mabest\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_39060/3881831539.py\u001b[0m in \u001b[0;36mvariational_classifier\u001b[1;34m(self, weights, bias, x)\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mvariational_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_epochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\pennylane\\qnode.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    576\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tape_cached\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0musing_custom_cache\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhash\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    577\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 578\u001b[1;33m         res = qml.execute(\n\u001b[0m\u001b[0;32m    579\u001b[0m             \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtape\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    580\u001b[0m             \u001b[0mdevice\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\pennylane\\interfaces\\batch\\__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(tapes, device, gradient_fn, interface, mode, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[0mcache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLRUCache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaxsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcachesize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgetsizeof\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mqml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m     \u001b[0mbatch_execute\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset_shots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverride_shots\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_execute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexpand_fn\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"device\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m         \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recreate_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\functools.py\u001b[0m in \u001b[0;36mupdate_wrapper\u001b[1;34m(wrapper, wrapped, assigned, updated)\u001b[0m\n\u001b[0;32m     54\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m         \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "model.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f442a9b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/QML4FRAUD/Factory/__pycache__/data.cpython-39.pyc b/QML4FRAUD/Factory/__pycache__/data.cpython-39.pyc
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diff --git a/QML4FRAUD/Factory/data.py b/QML4FRAUD/Factory/data.py
new file mode 100644
index 0000000..a5dcde5
--- /dev/null
+++ b/QML4FRAUD/Factory/data.py
@@ -0,0 +1,149 @@
+import pandas as pd
+import numpy as np
+from pennylane import numpy as np
+from sklearn.preprocessing import normalize
+from sklearn.preprocessing import StandardScaler
+
+import pennylane as qml
+#from pennylane_qiskit import IBMQDevice
+#from pennylane_qiskit import BasicAerDevice
+from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding
+from pennylane.optimize import AdamOptimizer
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+
+import time
+
+class PrepareData:
+    def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):
+        self.data = data
+        self.preprocess_done = None
+        
+        if sample_size == 0:
+            self.data_sample = data
+        else:
+            self.data_sample = data.sample(sample_size)
+            
+        self.train_set, self.test_set = train_test_split(self.data_sample, test_size=test_split, random_state=seed)
+        
+        self.y_train = self.train_set[[target]]
+        self.y_test = self.test_set[[target]]
+        
+        self.x_train = self.train_set.drop(target, axis=1)
+        self.x_test = self.test_set.drop(target, axis=1)
+        
+    def view_info(self):
+        print(self.data_sample.info())
+        if self.preprocess_done == None:
+            print("No preprocessing done yet.")
+        else:
+            print("Preprocessing done via: ", self.preprocess_done)
+        return self.data_sample.describe()
+    
+    def get_preprocessed(self, to_show = False):
+        if self.preprocess_done == None:
+            print("Please do some preprocessing first.")
+        else:
+            
+            if to_show:
+                print("Training Set and Labels: ")
+                print(self.train_X_preprocessed)
+                print(self.train_Y_preprocessed)
+
+                print("Test Set and Labels: ")
+                print(self.test_X_preprocessed)
+                print(self.test_Y_preprocessed)
+            
+            return self.train_X_preprocessed, self.train_Y_preprocessed, self.test_X_preprocessed, self.test_Y_preprocessed
+    
+    def perform_LDA(self, n_dim = 2):
+        
+        self.preprocess_done = "LDA"
+        print("Performing LDA...")
+        
+        length = len(self.x_train.columns)
+        split_feature = int(length/n_dim)
+        features_train = []
+        features_test = []
+        
+        # Split Features (for Yaqi to change)
+        for i in range(n_dim):
+            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]
+            features_train.append(new_set_train)
+            
+            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]
+            features_test.append(new_set_test)
+        
+        # Run the LDA
+        lda = LDA(n_components= (n_dim - 1))
+        features_lda_train = []
+        features_lda_test = []
+        
+        for i in range(n_dim):
+            features_lda_train_new = lda.fit_transform(features_train[i], self.y_train)
+            features_lda_train.append(pd.DataFrame(features_lda_train_new))
+            
+            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)
+            features_lda_test.append(pd.DataFrame(features_lda_test_new))
+        
+        x_train_data = features_lda_train[0]
+        x_test_data = features_lda_test[0]
+        
+        # Join the results together
+        for i in range(1, n_dim):
+            l_suffix = "_" + str(i)
+            r_suffix = "_" + str(i+1)
+            x_train_data = x_train_data.join(features_lda_train[i], lsuffix=l_suffix, rsuffix=r_suffix)
+            x_test_data = x_test_data.join(features_lda_test[i], lsuffix=l_suffix, rsuffix=r_suffix)
+        
+        # Normalize
+        std_scale_train = StandardScaler().fit(x_train_data)
+        x_train_data = std_scale_train.transform(x_train_data)
+        
+        std_scale_test = StandardScaler().fit(x_test_data)
+        x_test_data = std_scale_test.transform(x_test_data)
+            
+        # shift label from {0, 1} to {-1, 1}
+        self.train_X_preprocessed = np.array(x_train_data, requires_grad=False)
+        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)
+        
+        self.test_X_preprocessed = np.array(x_test_data, requires_grad=False)
+        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)
+        
+    def perform_PCA(self, n_dim = 2):
+        
+        self.preprocess_done = "PCA"
+        print("Performing PCA...")
+        
+        self.y_train.value_counts(normalize=True)*100
+        self.y_test.value_counts(normalize=True)*100
+        
+        pca = PCA(n_components=n_dim, svd_solver='full')
+        pca.fit(self.x_train)
+        x_train_pca = pca.transform(self.x_train)
+        pca.fit(self.x_test)
+        x_test_pca = pca.transform(self.x_test)
+        
+        train_X_preprocessed = normalize(x_train_pca)
+        test_X_preprocessed = normalize(x_test_pca)
+        
+        self.train_Y_preprocessed = np.array(self.y_train.values[:,0] * 2 - np.ones(len(self.y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}
+        self.train_X_preprocessed = np.array(train_X_preprocessed, requires_grad=False)
+        
+        self.test_Y_preprocessed = np.array(self.y_test.values[:,0] * 2 - np.ones(len(self.y_test.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}
+        self.test_X_preprocessed = np.array(test_X_preprocessed, requires_grad=False)
+        
+    def perform_normalize(self, n_dim = 2):
+        
+        self.preprocess_done = "Normalize"
+        print("Performing Normalize...")
+        
+        self.y_train.value_counts(normalize=True)*100
+        self.y_test.value_counts(normalize=True)*100
+        
+        self.x_train.value_counts(normalize=True)*100
+        self.x_test.value_counts(normalize=True)*100
\ No newline at end of file
diff --git a/QML4FRAUD/Factory/model.py b/QML4FRAUD/Factory/model.py
new file mode 100644
index 0000000..134737b
--- /dev/null
+++ b/QML4FRAUD/Factory/model.py
@@ -0,0 +1,119 @@
+import pandas as pd
+import numpy as np
+from pennylane import numpy as np
+from sklearn.preprocessing import normalize
+from sklearn.preprocessing import StandardScaler
+
+import pennylane as qml
+#from pennylane_qiskit import IBMQDevice
+#from pennylane_qiskit import BasicAerDevice
+from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding
+from pennylane.optimize import AdamOptimizer
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+
+import time
+
+class QBC:
+    def __init__(self, data, n_dim, n_layers, 
+                 optimizer = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08),
+                 interface_type = "autograd",
+                 loss_function = None,  backend = "default.qubit", shots = None):
+        
+        if loss_function == None:
+            def square_loss(labels, predictions):
+                loss = 0
+                for l, p in zip(labels, predictions):
+                    loss = loss + (l - p) ** 2
+
+                loss = loss / len(labels)
+                return loss
+            self.loss_function = square_loss
+        else: 
+            self.loss_function = loss_function
+        self.opt = optimizer
+        self.data = data
+        
+        self.n_dim = n_dim
+        
+        dev = qml.device(backend, wires = self.n_dim, shots=shots)
+        #dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)
+        #dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token="6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356", shots=256)
+        #dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)
+
+        @qml.qnode(dev)
+        def circuit(parameters, data):
+            for i in range(n_dim):
+                qml.Hadamard(wires = i)
+
+            AngleEmbedding(features = data, wires = range(self.n_dim), rotation = 'Y')
+
+            qml.StronglyEntanglingLayers(weights = parameters, wires = range(self.n_dim))
+
+            return qml.expval(qml.PauliZ(0))
+        
+        self.qlayer = qml.QNode(circuit, dev, interface=interface_type, diff_method='best')
+        
+        self.n_layers = n_layers
+        self.weights = 0.01 * np.random.randn(self.n_layers, self.n_dim, 3, requires_grad=True)
+        self.bias = np.array(0.0, requires_grad=True)
+
+    def variational_classifier(self, weights, bias, x):
+        return self.qlayer(weights, x) + bias
+        
+    def train(self, batch_size = 10, n_epochs = 50):
+        wbest = 0
+        bbest = 0
+        abest = 0
+        X, Y, _, _ = self.data.get_preprocessed()
+        
+        def cost(weights, bias, X, Y):
+            predictions = [self.variational_classifier(weights, bias, x) for x in X]
+            return self.loss_function(Y, predictions)
+
+        def accuracy(labels, predictions):
+
+            loss = 0
+            for l, p in zip(labels, predictions):
+                if abs(l - p) < 1e-5:
+                    loss = loss + 1
+            loss = loss / len(labels)
+
+            return loss
+
+        for it in range(n_epochs):
+
+            # weights update by one optimizer step
+
+            batch_index = np.random.randint(0, len(X), (batch_size,))
+            X_batch = X[batch_index]
+            Y_batch = Y[batch_index]
+            self.weights, self.bias, _, _ = self.opt.step(cost, self.weights, self.bias, X_batch, Y_batch)
+
+            # Compute the accuracy
+            predictions = [np.sign(self.variational_classifier(self.weights, self.bias, x)) for x in X]
+
+            if accuracy(Y, predictions) > abest:
+                wbest = self.weights
+                bbest = self.bias
+                abest = accuracy(Y, predictions)
+                print('New best')
+
+            acc = accuracy(Y, predictions)
+
+            print(
+                "Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} ".format(
+                    it + 1, cost(self.weights, self.bias, X, Y), acc
+                )
+            )
+            
+        self.weights = wbest
+        self.bias = bbest
+        
+    def predict(self, test_data):
+        predictions = [np.sign(self.variational_classifier(self.weights, self.bias, x)) for x in test_data]
+        return predictions
\ No newline at end of file
diff --git a/QML4FRAUD/Solution via architecture.ipynb b/QML4FRAUD/Solution via architecture.ipynb
new file mode 100644
index 0000000..a0d549e
--- /dev/null
+++ b/QML4FRAUD/Solution via architecture.ipynb	
@@ -0,0 +1,1421 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f66fdd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\_distutils_hack\\__init__.py:30: UserWarning: Setuptools is replacing distutils.\n",
+      "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pennylane import numpy as np\n",
+    "from Factory.data import PrepareData\n",
+    "from Factory.model import QBC\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0344a9",
+   "metadata": {},
+   "source": [
+    "# Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3722cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b9722cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = PrepareData(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a48959d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "No preprocessing done yet.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00c69d2",
+   "metadata": {},
+   "source": [
+    "# Use LDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "383f00dc",
+   "metadata": {},
+   "source": [
+    "Preprocess LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd8fb0b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing LDA...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d64e1dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "Preprocessing done via:  LDA\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9020cf",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "488ed7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_LDA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f59a837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7146527 | Accuracy: 0.7557143 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.5467249 | Accuracy: 0.8171429 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.4751983 | Accuracy: 0.8307143 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.4734549 | Accuracy: 0.8385714 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.4472145 | Accuracy: 0.8471429 \n",
+      "New best\n",
+      "Iter:     6 | Cost: 0.4475311 | Accuracy: 0.8478571 \n",
+      "Iter:     7 | Cost: 0.6056652 | Accuracy: 0.8064286 \n",
+      "Iter:     8 | Cost: 0.5346172 | Accuracy: 0.8457143 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.4346356 | Accuracy: 0.8571429 \n",
+      "Iter:    10 | Cost: 0.4457327 | Accuracy: 0.8528571 \n",
+      "Iter:    11 | Cost: 0.5464202 | Accuracy: 0.8464286 \n",
+      "Iter:    12 | Cost: 0.5721968 | Accuracy: 0.8464286 \n",
+      "Iter:    13 | Cost: 0.5576279 | Accuracy: 0.8442857 \n",
+      "Iter:    14 | Cost: 0.5400980 | Accuracy: 0.8435714 \n",
+      "Iter:    15 | Cost: 0.5297521 | Accuracy: 0.8457143 \n",
+      "Iter:    16 | Cost: 0.5273266 | Accuracy: 0.8471429 \n",
+      "Iter:    17 | Cost: 0.4711755 | Accuracy: 0.8442857 \n",
+      "Iter:    18 | Cost: 0.4586101 | Accuracy: 0.8492857 \n",
+      "Iter:    19 | Cost: 0.4710555 | Accuracy: 0.8471429 \n",
+      "Iter:    20 | Cost: 0.4496019 | Accuracy: 0.8564286 \n",
+      "New best\n",
+      "Iter:    21 | Cost: 0.4334069 | Accuracy: 0.8628571 \n",
+      "New best\n",
+      "Iter:    22 | Cost: 0.4350645 | Accuracy: 0.8635714 \n",
+      "New best\n",
+      "Iter:    23 | Cost: 0.4307490 | Accuracy: 0.8678571 \n",
+      "New best\n",
+      "Iter:    24 | Cost: 0.4142857 | Accuracy: 0.8728571 \n",
+      "New best\n",
+      "Iter:    25 | Cost: 0.4104879 | Accuracy: 0.8757143 \n",
+      "Iter:    26 | Cost: 0.4025811 | Accuracy: 0.8750000 \n",
+      "New best\n",
+      "Iter:    27 | Cost: 0.3971891 | Accuracy: 0.8778571 \n",
+      "Iter:    28 | Cost: 0.3814916 | Accuracy: 0.8757143 \n",
+      "Iter:    29 | Cost: 0.3685935 | Accuracy: 0.8757143 \n",
+      "Iter:    30 | Cost: 0.3622557 | Accuracy: 0.8757143 \n",
+      "Iter:    31 | Cost: 0.3592110 | Accuracy: 0.8742857 \n",
+      "Iter:    32 | Cost: 0.3595640 | Accuracy: 0.8778571 \n",
+      "New best\n",
+      "Iter:    33 | Cost: 0.3677971 | Accuracy: 0.8785714 \n",
+      "Iter:    34 | Cost: 0.4011392 | Accuracy: 0.8771429 \n",
+      "Iter:    35 | Cost: 0.4123842 | Accuracy: 0.8742857 \n",
+      "New best\n",
+      "Iter:    36 | Cost: 0.4398058 | Accuracy: 0.8807143 \n",
+      "Iter:    37 | Cost: 0.4797217 | Accuracy: 0.8700000 \n",
+      "Iter:    38 | Cost: 0.5145149 | Accuracy: 0.8550000 \n",
+      "Iter:    39 | Cost: 0.5033828 | Accuracy: 0.8600000 \n",
+      "Iter:    40 | Cost: 0.4766182 | Accuracy: 0.8792857 \n",
+      "New best\n",
+      "Iter:    41 | Cost: 0.4339206 | Accuracy: 0.8814286 \n",
+      "Iter:    42 | Cost: 0.4180167 | Accuracy: 0.8757143 \n",
+      "Iter:    43 | Cost: 0.3960513 | Accuracy: 0.8721429 \n",
+      "Iter:    44 | Cost: 0.3896418 | Accuracy: 0.8714286 \n",
+      "Iter:    45 | Cost: 0.3829375 | Accuracy: 0.8714286 \n",
+      "Iter:    46 | Cost: 0.3750100 | Accuracy: 0.8728571 \n",
+      "Iter:    47 | Cost: 0.3678940 | Accuracy: 0.8742857 \n",
+      "Iter:    48 | Cost: 0.3640737 | Accuracy: 0.8764286 \n",
+      "Iter:    49 | Cost: 0.3642824 | Accuracy: 0.8785714 \n",
+      "Iter:    50 | Cost: 0.3671699 | Accuracy: 0.8814286 \n"
+     ]
+    }
+   ],
+   "source": [
+    "model_LDA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384518e",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "572a4735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, X_test, Y_test = data.get_preprocessed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3b26df72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_LDA = model_LDA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ce01db5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.97      0.89      0.93       497\n",
+      "         1.0       0.63      0.87      0.73       103\n",
+      "\n",
+      "    accuracy                           0.89       600\n",
+      "   macro avg       0.80      0.88      0.83       600\n",
+      "weighted avg       0.91      0.89      0.90       600\n",
+      "\n",
+      "0.6293706293706294\n",
+      "0.8737864077669902\n",
+      "0.7317073170731707\n",
+      "0.8835732843663925\n",
+      "[[444  53]\n",
+      " [ 13  90]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
+    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
+    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
+    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ea9f5b",
+   "metadata": {},
+   "source": [
+    "# Use PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e6c02d",
+   "metadata": {},
+   "source": [
+    "Preprocess PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9f2da505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing PCA...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_PCA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d722a49c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2000 entries, 17991 to 19934\n",
+      "Columns: 113 entries, col_0 to targets\n",
+      "dtypes: float64(113)\n",
+      "memory usage: 1.7 MB\n",
+      "None\n",
+      "Preprocessing done via:  PCA\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.00000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>287.945500</td>\n",
+       "      <td>0.325500</td>\n",
+       "      <td>2.10300</td>\n",
+       "      <td>0.067000</td>\n",
+       "      <td>0.930500</td>\n",
+       "      <td>2.516500</td>\n",
+       "      <td>3.126500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>0.370000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.190000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.050500</td>\n",
+       "      <td>0.018500</td>\n",
+       "      <td>43.798000</td>\n",
+       "      <td>0.248500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>605.090262</td>\n",
+       "      <td>2.742318</td>\n",
+       "      <td>7.29685</td>\n",
+       "      <td>0.410604</td>\n",
+       "      <td>3.780584</td>\n",
+       "      <td>3.052767</td>\n",
+       "      <td>11.301963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063135</td>\n",
+       "      <td>0.482925</td>\n",
+       "      <td>0.031615</td>\n",
+       "      <td>0.464433</td>\n",
+       "      <td>0.392399</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.219029</td>\n",
+       "      <td>0.241219</td>\n",
+       "      <td>56.534726</td>\n",
+       "      <td>0.432251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>101.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>280.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>9319.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>183.00000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
+       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
+       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
+       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
+       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "\n",
+       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
+       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
+       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
+       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
+       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
+       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
+       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
+       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
+       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
+       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df4d46d",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "df69026d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PCA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "401edaee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.9922963 | Accuracy: 0.6050000 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.9133256 | Accuracy: 0.7357143 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.8372795 | Accuracy: 0.7428571 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.8082843 | Accuracy: 0.7464286 \n",
+      "Iter:     5 | Cost: 0.8039261 | Accuracy: 0.7464286 \n",
+      "Iter:     6 | Cost: 0.8042474 | Accuracy: 0.7464286 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.8297579 | Accuracy: 0.7471429 \n",
+      "Iter:     8 | Cost: 0.8814247 | Accuracy: 0.7471429 \n",
+      "Iter:     9 | Cost: 0.9090855 | Accuracy: 0.7471429 \n",
+      "Iter:    10 | Cost: 0.9006644 | Accuracy: 0.7471429 \n",
+      "Iter:    11 | Cost: 0.8605321 | Accuracy: 0.7471429 \n",
+      "Iter:    12 | Cost: 0.8280091 | Accuracy: 0.7471429 \n",
+      "Iter:    13 | Cost: 0.7825952 | Accuracy: 0.7471429 \n",
+      "Iter:    14 | Cost: 0.7447115 | Accuracy: 0.7471429 \n",
+      "Iter:    15 | Cost: 0.7485581 | Accuracy: 0.7471429 \n",
+      "Iter:    16 | Cost: 0.8402239 | Accuracy: 0.7278571 \n",
+      "Iter:    17 | Cost: 0.8835448 | Accuracy: 0.6978571 \n",
+      "Iter:    18 | Cost: 0.9145426 | Accuracy: 0.3971429 \n",
+      "Iter:    19 | Cost: 0.8670825 | Accuracy: 0.7150000 \n",
+      "Iter:    20 | Cost: 0.7791539 | Accuracy: 0.7428571 \n",
+      "Iter:    21 | Cost: 0.7432145 | Accuracy: 0.7471429 \n",
+      "Iter:    22 | Cost: 0.7460729 | Accuracy: 0.7471429 \n",
+      "Iter:    23 | Cost: 0.7734032 | Accuracy: 0.7471429 \n",
+      "Iter:    24 | Cost: 0.7911368 | Accuracy: 0.7471429 \n",
+      "Iter:    25 | Cost: 0.8098764 | Accuracy: 0.7471429 \n",
+      "Iter:    26 | Cost: 0.8396121 | Accuracy: 0.7471429 \n",
+      "Iter:    27 | Cost: 0.8571642 | Accuracy: 0.7471429 \n",
+      "Iter:    28 | Cost: 0.8267991 | Accuracy: 0.7471429 \n",
+      "Iter:    29 | Cost: 0.7796929 | Accuracy: 0.7464286 \n",
+      "Iter:    30 | Cost: 0.7718953 | Accuracy: 0.7464286 \n",
+      "Iter:    31 | Cost: 0.7747632 | Accuracy: 0.7464286 \n",
+      "Iter:    32 | Cost: 0.7990050 | Accuracy: 0.7464286 \n",
+      "Iter:    33 | Cost: 0.8508553 | Accuracy: 0.7357143 \n",
+      "Iter:    34 | Cost: 0.8222431 | Accuracy: 0.7435714 \n",
+      "Iter:    35 | Cost: 0.7661793 | Accuracy: 0.7471429 \n",
+      "Iter:    36 | Cost: 0.7379706 | Accuracy: 0.7471429 \n",
+      "Iter:    37 | Cost: 0.7647438 | Accuracy: 0.7471429 \n",
+      "Iter:    38 | Cost: 0.8199030 | Accuracy: 0.7471429 \n",
+      "Iter:    39 | Cost: 0.8230046 | Accuracy: 0.7471429 \n",
+      "Iter:    40 | Cost: 0.8318804 | Accuracy: 0.7471429 \n",
+      "Iter:    41 | Cost: 0.8325269 | Accuracy: 0.7471429 \n",
+      "Iter:    42 | Cost: 0.8229071 | Accuracy: 0.7471429 \n",
+      "Iter:    43 | Cost: 0.7878330 | Accuracy: 0.7471429 \n",
+      "Iter:    44 | Cost: 0.7527745 | Accuracy: 0.7471429 \n",
+      "Iter:    45 | Cost: 0.7394627 | Accuracy: 0.7471429 \n",
+      "Iter:    46 | Cost: 0.7763476 | Accuracy: 0.7471429 \n",
+      "Iter:    47 | Cost: 0.8037952 | Accuracy: 0.7464286 \n",
+      "Iter:    48 | Cost: 0.7987263 | Accuracy: 0.7464286 \n",
+      "Iter:    49 | Cost: 0.7850808 | Accuracy: 0.7464286 \n",
+      "Iter:    50 | Cost: 0.7664157 | Accuracy: 0.7471429 \n"
+     ]
+    }
+   ],
+   "source": [
+    "model_PCA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb757835",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "700192cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_PCA = model_PCA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0a3f0a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.91      0.84      0.88       497\n",
+      "         1.0       0.45      0.62      0.52       103\n",
+      "\n",
+      "    accuracy                           0.80       600\n",
+      "   macro avg       0.68      0.73      0.70       600\n",
+      "weighted avg       0.83      0.80      0.82       600\n",
+      "\n",
+      "0.44755244755244755\n",
+      "0.6213592233009708\n",
+      "0.5203252032520325\n",
+      "0.7312027504834834\n",
+      "[[418  79]\n",
+      " [ 39  64]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(metrics.classification_report(predictions_PCA,Y_test))\n",
+    "print(metrics.precision_score(predictions_PCA,Y_test))\n",
+    "print(metrics.recall_score(predictions_PCA,Y_test))\n",
+    "print(metrics.f1_score(predictions_PCA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_PCA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_PCA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58662136",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9582a509b808205ce4dd99284e2280c7e0afa179 Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Wed, 27 Jul 2022 00:36:35 -0400
Subject: [PATCH 07/25] Updated EDA

---
 QML4FRAUD/.DS_Store                           |   Bin 0 -> 6148 bytes
 QML4FRAUD/EDA.ipynb                           |  2208 +-
 ...ataset_nonzero.csv => dataset_cleaned.csv} | 40938 ++++++++--------
 3 files changed, 22388 insertions(+), 20758 deletions(-)
 create mode 100644 QML4FRAUD/.DS_Store
 rename QML4FRAUD/{dataset_nonzero.csv => dataset_cleaned.csv} (52%)

diff --git a/QML4FRAUD/.DS_Store b/QML4FRAUD/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
GIT binary patch
literal 6148
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zem<@ulZcFPQ@L2!n>{z**<q8>++&mCkOWA81W14cNZ<zv;LbK1Poaz?KmsK2CSc!(
z0ynLxE!0092;Krf2c+FF_Fe*7ECH>lEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ
zLs35+`xjp>T0<F0fCPF1$Cyrb|F7^5{eNG?83~ZUUlGt@xh*qZDeu<Z%US-OSsOPv
j)R!Z4KLME7ReXlK;d!wEw5GODWMKRea10D2@KpjYNUI8I

literal 0
HcmV?d00001

diff --git a/QML4FRAUD/EDA.ipynb b/QML4FRAUD/EDA.ipynb
index 3e0ea7e..2e75d21 100644
--- a/QML4FRAUD/EDA.ipynb
+++ b/QML4FRAUD/EDA.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,12 +48,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Quick Clean-up and Feature Grouping"
+    "## Clean-up and Quick Run"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +342,7 @@
        "[8 rows x 113 columns]"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -664,7 +664,7 @@
        "[8 rows x 98 columns]"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -674,87 +674,43 @@
     "df_clean.describe()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save the cleaned data\n",
-    "\n",
-    "df_clean.to_csv(\"dataset_nonzero.csv\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We want to split the features into groups that are similar within but distinct between groups. Let's take a look at the correlation matrix."
+    "Note: `col_7` is an exact copy of `col_0`. We will drop it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1008x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(14,8))\n",
-    "sns.set_theme(style=\"white\")\n",
-    "corr = df_clean.corr()\n",
-    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The correlaton relation falls into two relatively clear patches. Since we are aiming to reduce the features into 2 latent features, the correlation matrix provides pretty good guidance on how to split. Let's take a closer look in the region where the two groups seem to be split."
+    "df_clean = df_clean.drop('col_7', axis = 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1008x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.figure(figsize=(14,8))\n",
-    "sns.set_theme(style=\"white\")\n",
-    "corr = df_clean.loc[:,'col_49':'col_81'].corr()\n",
-    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
+    "# save the cleaned data\n",
+    "\n",
+    "df_clean.to_csv(\"dataset_nonzero.csv\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll simply group features up to `col_69` into one group and the rest into the other. There may be groupings better than this. Those would require further analysis."
+    "Now let's go through the whole process by simply spliting the features evenly into two groups."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -767,44 +723,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Separation of labels\n",
     "\n",
-    "x_train = train_set\n",
     "y_train = train_set[['targets']]\n",
+    "x_train = train_set.drop('targets', axis=1)\n",
     "\n",
-    "x_test = test_set\n",
-    "y_test = test_set[['targets']]"
+    "\n",
+    "y_test = test_set[['targets']]\n",
+    "x_test = test_set.drop('targets', axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1400 entries, 17785 to 17942\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   col_0   1400 non-null   float64\n",
+      " 1   col_1   1400 non-null   float64\n",
+      " 2   col_2   1400 non-null   float64\n",
+      " 3   col_3   1400 non-null   float64\n",
+      " 4   col_4   1400 non-null   float64\n",
+      " 5   col_5   1400 non-null   float64\n",
+      " 6   col_6   1400 non-null   float64\n",
+      " 7   col_13  1400 non-null   float64\n",
+      " 8   col_14  1400 non-null   float64\n",
+      " 9   col_15  1400 non-null   float64\n",
+      " 10  col_16  1400 non-null   float64\n",
+      " 11  col_17  1400 non-null   float64\n",
+      " 12  col_22  1400 non-null   float64\n",
+      " 13  col_23  1400 non-null   float64\n",
+      " 14  col_24  1400 non-null   float64\n",
+      " 15  col_25  1400 non-null   float64\n",
+      " 16  col_26  1400 non-null   float64\n",
+      " 17  col_27  1400 non-null   float64\n",
+      " 18  col_28  1400 non-null   float64\n",
+      " 19  col_29  1400 non-null   float64\n",
+      " 20  col_30  1400 non-null   float64\n",
+      " 21  col_31  1400 non-null   float64\n",
+      " 22  col_32  1400 non-null   float64\n",
+      " 23  col_33  1400 non-null   float64\n",
+      " 24  col_34  1400 non-null   float64\n",
+      " 25  col_36  1400 non-null   float64\n",
+      " 26  col_37  1400 non-null   float64\n",
+      " 27  col_38  1400 non-null   float64\n",
+      " 28  col_39  1400 non-null   float64\n",
+      " 29  col_40  1400 non-null   float64\n",
+      " 30  col_41  1400 non-null   float64\n",
+      " 31  col_42  1400 non-null   float64\n",
+      " 32  col_43  1400 non-null   float64\n",
+      " 33  col_44  1400 non-null   float64\n",
+      " 34  col_45  1400 non-null   float64\n",
+      " 35  col_46  1400 non-null   float64\n",
+      " 36  col_47  1400 non-null   float64\n",
+      " 37  col_48  1400 non-null   float64\n",
+      " 38  col_49  1400 non-null   float64\n",
+      " 39  col_50  1400 non-null   float64\n",
+      " 40  col_54  1400 non-null   float64\n",
+      " 41  col_55  1400 non-null   float64\n",
+      " 42  col_56  1400 non-null   float64\n",
+      " 43  col_57  1400 non-null   float64\n",
+      " 44  col_58  1400 non-null   float64\n",
+      " 45  col_59  1400 non-null   float64\n",
+      " 46  col_60  1400 non-null   float64\n",
+      " 47  col_61  1400 non-null   float64\n",
+      " 48  col_62  1400 non-null   float64\n",
+      "dtypes: float64(49)\n",
+      "memory usage: 546.9 KB\n"
+     ]
+    }
+   ],
    "source": [
     "# grouping features\n",
-    " \n",
-    "features_a = x_train.loc[:,:'col_69']\n",
-    "features_b = x_train.loc[:,'col_69':]\n",
+    "split_point = x_train.columns[len(x_train.columns)//2]\n",
     "\n",
-    "features_a_test = x_test.loc[:,:'col_67']\n",
-    "features_b_test = x_test.loc[:,'col_67':]"
+    "features_a = x_train.loc[:,:split_point]\n",
+    "features_b = x_train.loc[:,split_point:]\n",
+    "\n",
+    "features_a_test = x_test.loc[:,:split_point]\n",
+    "features_b_test = x_test.loc[:,split_point:]\n",
+    "features_a.info()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## LDA Features"
+    "### LDA Features"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -820,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -833,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -863,23 +885,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.133929</td>\n",
+       "      <td>-1.233199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.529977</td>\n",
+       "      <td>0.517182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.922923</td>\n",
+       "      <td>0.405583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.487324</td>\n",
+       "      <td>0.811352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.368786</td>\n",
+       "      <td>-1.126657</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -887,14 +909,14 @@
       ],
       "text/plain": [
        "          0\n",
-       "0  0.133929\n",
-       "1  0.529977\n",
-       "2 -0.922923\n",
-       "3  0.487324\n",
-       "4  0.368786"
+       "0 -1.233199\n",
+       "1  0.517182\n",
+       "2  0.405583\n",
+       "3  0.811352\n",
+       "4 -1.126657"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -905,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -917,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -948,28 +970,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.133929</td>\n",
-       "      <td>-1.091509</td>\n",
+       "      <td>-1.233199</td>\n",
+       "      <td>0.185201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.529977</td>\n",
-       "      <td>-0.960667</td>\n",
+       "      <td>0.517182</td>\n",
+       "      <td>-0.051898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.922923</td>\n",
-       "      <td>-0.639466</td>\n",
+       "      <td>0.405583</td>\n",
+       "      <td>-0.051898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.487324</td>\n",
-       "      <td>0.204965</td>\n",
+       "      <td>0.811352</td>\n",
+       "      <td>-1.167328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.368786</td>\n",
-       "      <td>-0.639466</td>\n",
+       "      <td>-1.126657</td>\n",
+       "      <td>-0.696000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -978,28 +1000,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1395</th>\n",
-       "      <td>1.091340</td>\n",
-       "      <td>0.604241</td>\n",
+       "      <td>0.590027</td>\n",
+       "      <td>-0.962164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1396</th>\n",
-       "      <td>-1.423830</td>\n",
-       "      <td>-0.052359</td>\n",
+       "      <td>-2.931781</td>\n",
+       "      <td>-1.345132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1397</th>\n",
-       "      <td>-0.065439</td>\n",
-       "      <td>-0.052359</td>\n",
+       "      <td>0.410028</td>\n",
+       "      <td>0.269090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1398</th>\n",
-       "      <td>-1.012592</td>\n",
-       "      <td>-0.591393</td>\n",
+       "      <td>-2.902701</td>\n",
+       "      <td>-0.953910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
-       "      <td>0.136169</td>\n",
-       "      <td>-0.639466</td>\n",
+       "      <td>0.387806</td>\n",
+       "      <td>-0.886261</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1008,22 +1030,22 @@
       ],
       "text/plain": [
        "        0_left   0_right\n",
-       "0     0.133929 -1.091509\n",
-       "1     0.529977 -0.960667\n",
-       "2    -0.922923 -0.639466\n",
-       "3     0.487324  0.204965\n",
-       "4     0.368786 -0.639466\n",
+       "0    -1.233199  0.185201\n",
+       "1     0.517182 -0.051898\n",
+       "2     0.405583 -0.051898\n",
+       "3     0.811352 -1.167328\n",
+       "4    -1.126657 -0.696000\n",
        "...        ...       ...\n",
-       "1395  1.091340  0.604241\n",
-       "1396 -1.423830 -0.052359\n",
-       "1397 -0.065439 -0.052359\n",
-       "1398 -1.012592 -0.591393\n",
-       "1399  0.136169 -0.639466\n",
+       "1395  0.590027 -0.962164\n",
+       "1396 -2.931781 -1.345132\n",
+       "1397  0.410028  0.269090\n",
+       "1398 -2.902701 -0.953910\n",
+       "1399  0.387806 -0.886261\n",
        "\n",
        "[1400 rows x 2 columns]"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1034,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1065,28 +1087,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.262410</td>\n",
-       "      <td>-0.844093</td>\n",
+       "      <td>1.516931</td>\n",
+       "      <td>1.476801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.016687</td>\n",
-       "      <td>-0.403047</td>\n",
+       "      <td>0.038029</td>\n",
+       "      <td>-0.384427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.978751</td>\n",
-       "      <td>-1.711895</td>\n",
+       "      <td>0.516965</td>\n",
+       "      <td>1.708365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.111510</td>\n",
-       "      <td>-0.000622</td>\n",
+       "      <td>1.710725</td>\n",
+       "      <td>-0.208194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.286865</td>\n",
-       "      <td>-0.819957</td>\n",
+       "      <td>0.889208</td>\n",
+       "      <td>-1.515469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1095,28 +1117,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>595</th>\n",
-       "      <td>0.675788</td>\n",
-       "      <td>3.803108</td>\n",
+       "      <td>-0.540662</td>\n",
+       "      <td>-0.108684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>-0.522451</td>\n",
-       "      <td>-0.281238</td>\n",
+       "      <td>0.769335</td>\n",
+       "      <td>2.802327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
-       "      <td>-2.422944</td>\n",
-       "      <td>0.172625</td>\n",
+       "      <td>0.651577</td>\n",
+       "      <td>-0.117112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
-       "      <td>0.764962</td>\n",
-       "      <td>-0.097968</td>\n",
+       "      <td>-0.394235</td>\n",
+       "      <td>2.164792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
-       "      <td>0.873537</td>\n",
-       "      <td>2.639325</td>\n",
+       "      <td>0.861443</td>\n",
+       "      <td>-0.110812</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1125,22 +1147,22 @@
       ],
       "text/plain": [
        "       0_left   0_right\n",
-       "0    0.262410 -0.844093\n",
-       "1    0.016687 -0.403047\n",
-       "2   -0.978751 -1.711895\n",
-       "3   -0.111510 -0.000622\n",
-       "4    0.286865 -0.819957\n",
+       "0    1.516931  1.476801\n",
+       "1    0.038029 -0.384427\n",
+       "2    0.516965  1.708365\n",
+       "3    1.710725 -0.208194\n",
+       "4    0.889208 -1.515469\n",
        "..        ...       ...\n",
-       "595  0.675788  3.803108\n",
-       "596 -0.522451 -0.281238\n",
-       "597 -2.422944  0.172625\n",
-       "598  0.764962 -0.097968\n",
-       "599  0.873537  2.639325\n",
+       "595 -0.540662 -0.108684\n",
+       "596  0.769335  2.802327\n",
+       "597  0.651577 -0.117112\n",
+       "598 -0.394235  2.164792\n",
+       "599  0.861443 -0.110812\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1151,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1166,22 +1188,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.2270424 , -0.60888212],\n",
-       "       [ 0.01443808, -0.29073569],\n",
-       "       [-0.84683642, -1.23486658],\n",
+       "array([[ 1.32047703,  1.02866565],\n",
+       "       [ 0.03310356, -0.2677724 ],\n",
+       "       [ 0.45001448,  1.18996136],\n",
        "       ...,\n",
-       "       [-2.09638321,  0.12452206],\n",
-       "       [ 0.66186192, -0.07066894],\n",
-       "       [ 0.75580324,  1.90386338]])"
+       "       [ 0.56719272, -0.08157415],\n",
+       "       [-0.34317897,  1.50788522],\n",
+       "       [ 0.74987996, -0.07718603]])"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1201,19 +1223,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        73.857143\n",
-       "1.0        26.142857\n",
+       "0.0        73.071429\n",
+       "1.0        26.928571\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1226,19 +1248,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        72.5\n",
-       "1.0        27.5\n",
+       "0.0        72.0\n",
+       "1.0        28.0\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1253,14 +1275,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## QML\n",
+    "### QML\n",
     "\n",
     "We then perform quantum machine learning using the two LDA features."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1287,27 +1309,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.00644174 -0.00463604 -0.00809824]\n",
-      "  [-0.01525027 -0.00605393  0.00755927]]\n",
+      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
+      "  [ 0.0152303  -0.00234153 -0.00234137]]\n",
       "\n",
-      " [[ 0.00692921  0.02457865  0.00375194]\n",
-      "  [-0.01205123 -0.00389854 -0.01491166]]\n",
+      " [[ 0.01579213  0.00767435 -0.00469474]\n",
+      "  [ 0.0054256  -0.00463418 -0.0046573 ]]\n",
       "\n",
-      " [[-0.00675347  0.00046245 -0.01278641]\n",
-      "  [-0.01214017  0.00756313 -0.00371434]]\n",
+      " [[ 0.00241962 -0.0191328  -0.01724918]\n",
+      "  [-0.00562288 -0.01012831  0.00314247]]\n",
       "\n",
-      " [[-0.00388285  0.00926124 -0.00735844]\n",
-      "  [ 0.01285322 -0.02080725 -0.00739566]]\n",
+      " [[-0.00908024 -0.01412304  0.01465649]\n",
+      "  [-0.00225776  0.00067528 -0.01424748]]\n",
       "\n",
-      " [[-0.00029247  0.0012585   0.00145581]\n",
-      "  [ 0.00211849 -0.00099338  0.0103849 ]]] 0.0\n"
+      " [[-0.00544383  0.00110923 -0.01150994]\n",
+      "  [ 0.00375698 -0.00600639 -0.00291694]]] 0.0\n"
      ]
     }
    ],
@@ -1321,16 +1343,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(-0.078125, requires_grad=True)"
+       "tensor(-0.09765625, requires_grad=True)"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1341,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1351,7 +1373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1366,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1383,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1394,18 +1416,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X = [tensor(0.11799041, requires_grad=False), tensor(-0.82749202, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.46690762, requires_grad=False), tensor(-0.72829837, requires_grad=False)], Y = -1\n",
-      "X = [tensor(-0.81309064, requires_grad=False), tensor(-0.48478984, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.42933072, requires_grad=False), tensor(0.15538733, requires_grad=False)], Y =  1\n",
-      "X = [tensor(0.32489913, requires_grad=False), tensor(-0.48478984, requires_grad=False)], Y = -1\n"
+      "X = [tensor(-1.10428902, requires_grad=False), tensor(0.13841145, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.46311928, requires_grad=False), tensor(-0.03878681, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.36318635, requires_grad=False), tensor(-0.03878681, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.72653848, requires_grad=False), tensor(-0.87241389, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-1.0088843, requires_grad=False), tensor(-0.52016244, requires_grad=False)], Y = -1\n"
      ]
     }
    ],
@@ -1419,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1429,7 +1451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1437,63 +1459,73 @@
      "output_type": "stream",
      "text": [
       "New best\n",
-      "Iter:     1 | Cost: 0.7163056 | Accuracy: 0.7614286 \n",
+      "Iter:     1 | Cost: 0.8783847 | Accuracy: 0.6642857 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.7234059 | Accuracy: 0.7392857 \n",
       "New best\n",
-      "Iter:     2 | Cost: 0.5559769 | Accuracy: 0.8200000 \n",
+      "Iter:     3 | Cost: 0.6404744 | Accuracy: 0.7871429 \n",
       "New best\n",
-      "Iter:     3 | Cost: 0.4932610 | Accuracy: 0.8314286 \n",
+      "Iter:     4 | Cost: 0.6202939 | Accuracy: 0.8021429 \n",
       "New best\n",
-      "Iter:     4 | Cost: 0.4961598 | Accuracy: 0.8400000 \n",
-      "Iter:     5 | Cost: 0.5057743 | Accuracy: 0.8400000 \n",
-      "Iter:     6 | Cost: 0.5069953 | Accuracy: 0.8400000 \n",
+      "Iter:     5 | Cost: 0.5521156 | Accuracy: 0.8164286 \n",
       "New best\n",
-      "Iter:     7 | Cost: 0.4873486 | Accuracy: 0.8492857 \n",
+      "Iter:     6 | Cost: 0.4705954 | Accuracy: 0.8378571 \n",
+      "Iter:     7 | Cost: 0.4882907 | Accuracy: 0.8350000 \n",
+      "Iter:     8 | Cost: 0.4920552 | Accuracy: 0.8378571 \n",
       "New best\n",
-      "Iter:     8 | Cost: 0.4672314 | Accuracy: 0.8600000 \n",
+      "Iter:     9 | Cost: 0.4743783 | Accuracy: 0.8414286 \n",
       "New best\n",
-      "Iter:     9 | Cost: 0.4420190 | Accuracy: 0.8635714 \n",
-      "Iter:    10 | Cost: 0.4362290 | Accuracy: 0.8635714 \n",
-      "Iter:    11 | Cost: 0.4296042 | Accuracy: 0.8585714 \n",
-      "Iter:    12 | Cost: 0.4506719 | Accuracy: 0.8557143 \n",
-      "Iter:    13 | Cost: 0.4721339 | Accuracy: 0.8500000 \n",
-      "Iter:    14 | Cost: 0.4821543 | Accuracy: 0.8507143 \n",
-      "Iter:    15 | Cost: 0.4873148 | Accuracy: 0.8507143 \n",
-      "Iter:    16 | Cost: 0.4802705 | Accuracy: 0.8514286 \n",
-      "Iter:    17 | Cost: 0.4694841 | Accuracy: 0.8535714 \n",
-      "Iter:    18 | Cost: 0.4581866 | Accuracy: 0.8528571 \n",
-      "Iter:    19 | Cost: 0.4508369 | Accuracy: 0.8557143 \n",
-      "Iter:    20 | Cost: 0.4410900 | Accuracy: 0.8578571 \n",
-      "Iter:    21 | Cost: 0.4346116 | Accuracy: 0.8607143 \n",
-      "Iter:    22 | Cost: 0.4395575 | Accuracy: 0.8607143 \n",
-      "Iter:    23 | Cost: 0.4369053 | Accuracy: 0.8614286 \n",
-      "Iter:    24 | Cost: 0.4391714 | Accuracy: 0.8592857 \n",
-      "Iter:    25 | Cost: 0.4430851 | Accuracy: 0.8571429 \n",
-      "Iter:    26 | Cost: 0.4370470 | Accuracy: 0.8585714 \n",
-      "Iter:    27 | Cost: 0.4480947 | Accuracy: 0.8578571 \n",
-      "Iter:    28 | Cost: 0.4317516 | Accuracy: 0.8600000 \n",
+      "Iter:    10 | Cost: 0.4615172 | Accuracy: 0.8485714 \n",
+      "Iter:    11 | Cost: 0.4604754 | Accuracy: 0.8457143 \n",
       "New best\n",
-      "Iter:    29 | Cost: 0.4689808 | Accuracy: 0.8650000 \n",
-      "Iter:    30 | Cost: 0.4780110 | Accuracy: 0.8621429 \n",
-      "Iter:    31 | Cost: 0.5179296 | Accuracy: 0.8535714 \n",
-      "Iter:    32 | Cost: 0.5853279 | Accuracy: 0.8314286 \n",
-      "Iter:    33 | Cost: 0.5275210 | Accuracy: 0.8542857 \n",
-      "Iter:    34 | Cost: 0.4413556 | Accuracy: 0.8621429 \n",
-      "Iter:    35 | Cost: 0.4123321 | Accuracy: 0.8614286 \n",
-      "Iter:    36 | Cost: 0.4264267 | Accuracy: 0.8600000 \n",
-      "Iter:    37 | Cost: 0.4552265 | Accuracy: 0.8592857 \n",
-      "Iter:    38 | Cost: 0.4896763 | Accuracy: 0.8578571 \n",
-      "Iter:    39 | Cost: 0.5017294 | Accuracy: 0.8564286 \n",
-      "Iter:    40 | Cost: 0.4911548 | Accuracy: 0.8528571 \n",
-      "Iter:    41 | Cost: 0.4806956 | Accuracy: 0.8507143 \n",
-      "Iter:    42 | Cost: 0.4881041 | Accuracy: 0.8435714 \n",
-      "Iter:    43 | Cost: 0.4955334 | Accuracy: 0.8407143 \n",
-      "Iter:    44 | Cost: 0.5159703 | Accuracy: 0.8407143 \n",
-      "Iter:    45 | Cost: 0.5603780 | Accuracy: 0.8421429 \n",
-      "Iter:    46 | Cost: 0.6229637 | Accuracy: 0.8450000 \n",
-      "Iter:    47 | Cost: 0.6417202 | Accuracy: 0.8435714 \n",
-      "Iter:    48 | Cost: 0.5837545 | Accuracy: 0.8492857 \n",
-      "Iter:    49 | Cost: 0.4822762 | Accuracy: 0.8550000 \n",
-      "Iter:    50 | Cost: 0.4385916 | Accuracy: 0.8592857 \n"
+      "Iter:    12 | Cost: 0.4666229 | Accuracy: 0.8507143 \n",
+      "Iter:    13 | Cost: 0.4739211 | Accuracy: 0.8485714 \n",
+      "Iter:    14 | Cost: 0.4938755 | Accuracy: 0.8478571 \n",
+      "Iter:    15 | Cost: 0.4932492 | Accuracy: 0.8507143 \n",
+      "New best\n",
+      "Iter:    16 | Cost: 0.4804728 | Accuracy: 0.8557143 \n",
+      "New best\n",
+      "Iter:    17 | Cost: 0.4636001 | Accuracy: 0.8564286 \n",
+      "New best\n",
+      "Iter:    18 | Cost: 0.4547634 | Accuracy: 0.8592857 \n",
+      "New best\n",
+      "Iter:    19 | Cost: 0.4482752 | Accuracy: 0.8635714 \n",
+      "New best\n",
+      "Iter:    20 | Cost: 0.4517178 | Accuracy: 0.8657143 \n",
+      "New best\n",
+      "Iter:    21 | Cost: 0.4581175 | Accuracy: 0.8742857 \n",
+      "New best\n",
+      "Iter:    22 | Cost: 0.4630016 | Accuracy: 0.8750000 \n",
+      "New best\n",
+      "Iter:    23 | Cost: 0.4418877 | Accuracy: 0.8764286 \n",
+      "Iter:    24 | Cost: 0.4285966 | Accuracy: 0.8757143 \n",
+      "Iter:    25 | Cost: 0.4181510 | Accuracy: 0.8614286 \n",
+      "Iter:    26 | Cost: 0.4227318 | Accuracy: 0.8500000 \n",
+      "Iter:    27 | Cost: 0.4215839 | Accuracy: 0.8507143 \n",
+      "Iter:    28 | Cost: 0.4172597 | Accuracy: 0.8500000 \n",
+      "Iter:    29 | Cost: 0.4096631 | Accuracy: 0.8571429 \n",
+      "New best\n",
+      "Iter:    30 | Cost: 0.4321907 | Accuracy: 0.8792857 \n",
+      "Iter:    31 | Cost: 0.4978105 | Accuracy: 0.8478571 \n",
+      "Iter:    32 | Cost: 0.6312185 | Accuracy: 0.7442857 \n",
+      "Iter:    33 | Cost: 0.7515308 | Accuracy: 0.6700000 \n",
+      "Iter:    34 | Cost: 0.6126481 | Accuracy: 0.7542857 \n",
+      "Iter:    35 | Cost: 0.4772270 | Accuracy: 0.8600000 \n",
+      "Iter:    36 | Cost: 0.4138549 | Accuracy: 0.8692857 \n",
+      "Iter:    37 | Cost: 0.3972903 | Accuracy: 0.8692857 \n",
+      "Iter:    38 | Cost: 0.3988931 | Accuracy: 0.8650000 \n",
+      "Iter:    39 | Cost: 0.4060796 | Accuracy: 0.8650000 \n",
+      "Iter:    40 | Cost: 0.4150995 | Accuracy: 0.8600000 \n",
+      "Iter:    41 | Cost: 0.4265430 | Accuracy: 0.8564286 \n",
+      "Iter:    42 | Cost: 0.4303090 | Accuracy: 0.8535714 \n",
+      "Iter:    43 | Cost: 0.4375184 | Accuracy: 0.8571429 \n",
+      "Iter:    44 | Cost: 0.4494775 | Accuracy: 0.8535714 \n",
+      "Iter:    45 | Cost: 0.4971204 | Accuracy: 0.8378571 \n",
+      "Iter:    46 | Cost: 0.4675429 | Accuracy: 0.8521429 \n",
+      "Iter:    47 | Cost: 0.4459844 | Accuracy: 0.8542857 \n",
+      "Iter:    48 | Cost: 0.4309135 | Accuracy: 0.8521429 \n",
+      "Iter:    49 | Cost: 0.4408698 | Accuracy: 0.8492857 \n",
+      "Iter:    50 | Cost: 0.4509176 | Accuracy: 0.8471429 \n"
      ]
     }
    ],
@@ -1534,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1544,14 +1576,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cost: 0.4677206980912003, Accuracy: 85.0%\n"
+      "Cost: 0.46517865137593795, Accuracy: 84.0%\n"
      ]
     }
    ],
@@ -1565,7 +1597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1596,8 +1628,8 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1606,12 +1638,12 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1626,17 +1658,17 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>595</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1656,22 +1688,22 @@
       ],
       "text/plain": [
        "    Predictions  Test\n",
-       "0          -1.0  -1.0\n",
+       "0           1.0   1.0\n",
        "1          -1.0  -1.0\n",
-       "2          -1.0  -1.0\n",
-       "3          -1.0  -1.0\n",
+       "2           1.0   1.0\n",
+       "3           1.0  -1.0\n",
        "4          -1.0  -1.0\n",
        "..          ...   ...\n",
-       "595         1.0   1.0\n",
-       "596        -1.0  -1.0\n",
-       "597        -1.0  -1.0\n",
+       "595        -1.0  -1.0\n",
+       "596         1.0   1.0\n",
+       "597         1.0  -1.0\n",
        "598        -1.0   1.0\n",
        "599         1.0   1.0\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1682,7 +1714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1691,19 +1723,19 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.94      0.87      0.90       468\n",
-      "         1.0       0.63      0.79      0.70       132\n",
+      "        -1.0       0.86      0.92      0.89       405\n",
+      "         1.0       0.80      0.69      0.74       195\n",
       "\n",
-      "    accuracy                           0.85       600\n",
-      "   macro avg       0.78      0.83      0.80       600\n",
-      "weighted avg       0.87      0.85      0.86       600\n",
+      "    accuracy                           0.84       600\n",
+      "   macro avg       0.83      0.81      0.82       600\n",
+      "weighted avg       0.84      0.84      0.84       600\n",
       "\n",
-      "0.6303030303030303\n",
-      "0.7878787878787878\n",
-      "0.7003367003367004\n",
-      "0.8287684537684538\n",
-      "[[407  61]\n",
-      " [ 28 104]]\n"
+      "precisioin score: 0.8035714285714286\n",
+      "recall score: 0.6923076923076923\n",
+      "f1 score: 0.7438016528925621\n",
+      "balanced accuracy: 0.8054131054131054\n",
+      "confusion matrix: [[372  33]\n",
+      " [ 60 135]]\n"
      ]
     }
    ],
@@ -1711,19 +1743,1617 @@
     "# Print the classification report and important metrics\n",
     "\n",
     "print(metrics.classification_report(predictions,Yte))\n",
-    "print(metrics.precision_score(predictions,Yte))\n",
-    "print(metrics.recall_score(predictions,Yte))\n",
-    "print(metrics.f1_score(predictions,Yte))\n",
-    "print(metrics.balanced_accuracy_score(predictions,Yte))\n",
-    "print(metrics.confusion_matrix(predictions,Yte))"
+    "print(f\"precisioin score: {metrics.precision_score(predictions,Yte)}\")\n",
+    "print(f\"recall score: {metrics.recall_score(predictions,Yte)}\")\n",
+    "print(f\"f1 score: {metrics.f1_score(predictions,Yte)}\")\n",
+    "print(f\"balanced accuracy: {metrics.balanced_accuracy_score(predictions,Yte)}\")\n",
+    "print(f\"confusion matrix: {metrics.confusion_matrix(predictions,Yte)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Grouping\n",
+    "\n",
+    "We want to split the features into groups that are similar within but distinct between groups. Let's take a look at the correlation matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr = df_clean.corr()\n",
+    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some columns show a much stronger correlation than others. Let's see if we could group the more strongly correlated ones together. This is probably not optimized but it is a test of idea."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_22</th>\n",
+       "      <th>col_47</th>\n",
+       "      <th>col_66</th>\n",
+       "      <th>col_63</th>\n",
+       "      <th>col_57</th>\n",
+       "      <th>col_50</th>\n",
+       "      <th>col_36</th>\n",
+       "      <th>col_55</th>\n",
+       "      <th>col_40</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_74</th>\n",
+       "      <th>col_73</th>\n",
+       "      <th>col_84</th>\n",
+       "      <th>col_87</th>\n",
+       "      <th>col_89</th>\n",
+       "      <th>col_86</th>\n",
+       "      <th>col_83</th>\n",
+       "      <th>targets</th>\n",
+       "      <th>col_15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col_0  col_22  col_47  col_66  col_63  col_57  col_50  col_36  col_55  \\\n",
+       "0    9.0     9.0    36.0    32.0    32.0    11.0    15.0    97.0    56.0   \n",
+       "1    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "2    0.0     0.0    13.0     0.0     0.0     0.0     0.0     5.0     0.0   \n",
+       "3   17.0    23.0     0.0     0.0     0.0     0.0     2.0     3.0     0.0   \n",
+       "4    1.0     1.0    21.0    34.0    20.0    36.0    36.0    52.0    39.0   \n",
+       "\n",
+       "   col_40  ...  col_104  col_74  col_73  col_84  col_87  col_89  col_86  \\\n",
+       "0     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "1     0.0  ...      1.0     0.0     0.0     0.0     0.0     1.0     0.0   \n",
+       "2     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "3     0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "\n",
+       "   col_83  targets  col_15  \n",
+       "0     0.0      1.0     0.0  \n",
+       "1     0.0      1.0     0.0  \n",
+       "2     0.0      1.0     1.0  \n",
+       "3     0.0      1.0     0.0  \n",
+       "4     0.0      1.0     0.0  \n",
+       "\n",
+       "[5 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered = df_clean[corr['col_0'].sort_values(ascending=False).index]\n",
+    "ordered.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr_ordered = ordered.corr()\n",
+    "heatmap = sns.heatmap(corr_ordered, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr2 = ordered.loc[:,'col_64':'col_98'].corr()\n",
+    "heatmap = sns.heatmap(corr2, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will pick `col_37`/`col_30` as our splitting point for the two groups. This results in an uneven split (21,75). We perform the LDA and QML on the same sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordred_sample = df_sample[corr['col_0'].sort_values(ascending=False).index]\n",
+    "train,test = train_test_split(ordred_sample, test_size=0.30, random_state=10)\n",
+    "train_set = train\n",
+    "test_set = test\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_22</th>\n",
+       "      <th>col_47</th>\n",
+       "      <th>col_66</th>\n",
+       "      <th>col_63</th>\n",
+       "      <th>col_57</th>\n",
+       "      <th>col_50</th>\n",
+       "      <th>col_36</th>\n",
+       "      <th>col_55</th>\n",
+       "      <th>col_40</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_74</th>\n",
+       "      <th>col_73</th>\n",
+       "      <th>col_84</th>\n",
+       "      <th>col_87</th>\n",
+       "      <th>col_89</th>\n",
+       "      <th>col_86</th>\n",
+       "      <th>col_83</th>\n",
+       "      <th>targets</th>\n",
+       "      <th>col_15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7808</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7908</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14342</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9080</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14057</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12761</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15277</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6187</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15217</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4801</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col_0  col_22  col_47  col_66  col_63  col_57  col_50  col_36  col_55  \\\n",
+       "7808     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "7908     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14342    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "9080     1.0     1.0     2.0     0.0     0.0     0.0     0.0     4.0     0.0   \n",
+       "14057    0.0     0.0     0.0     0.0     0.0     0.0     0.0     2.0     1.0   \n",
+       "12761    2.0     2.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15277    2.0     3.0    12.0     0.0     0.0     0.0     5.0    43.0     0.0   \n",
+       "6187     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15217    3.0     3.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4801     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "\n",
+       "       col_40  ...  col_104  col_74  col_73  col_84  col_87  col_89  col_86  \\\n",
+       "7808      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "7908      0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14342     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "9080      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14057     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "12761     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15277     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "6187      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15217     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4801      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "\n",
+       "       col_83  targets  col_15  \n",
+       "7808      1.0      0.0     1.0  \n",
+       "7908      0.0      0.0     1.0  \n",
+       "14342     0.0      0.0     0.0  \n",
+       "9080      0.0      0.0     0.0  \n",
+       "14057     0.0      0.0     0.0  \n",
+       "12761     0.0      0.0     0.0  \n",
+       "15277     0.0      0.0     1.0  \n",
+       "6187      0.0      0.0     0.0  \n",
+       "15217     0.0      0.0     0.0  \n",
+       "4801      0.0      1.0     0.0  \n",
+       "\n",
+       "[10 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordred_sample.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separation of labels\n",
+    "\n",
+    "y_train = train_set[['targets']]\n",
+    "x_train = train_set.drop('targets', axis=1)\n",
+    "\n",
+    "\n",
+    "y_test = test_set[['targets']]\n",
+    "x_test = test_set.drop('targets', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((1400, 21), (1400, 75))"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# grouping features\n",
+    "\n",
+    "features_a = x_train.loc[:,:'col_37']\n",
+    "features_b = x_train.loc[:,'col_30':]\n",
+    "\n",
+    "features_a_test = x_test.loc[:,:'col_37']\n",
+    "features_b_test = x_test.loc[:,'col_30':]\n",
+    "features_a.shape, features_b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['col_0', 'col_22', 'col_47', 'col_66', 'col_63', 'col_57', 'col_50',\n",
+       "       'col_36', 'col_55', 'col_40', 'col_54', 'col_23', 'col_64', 'col_49',\n",
+       "       'col_60', 'col_38', 'col_65', 'col_48', 'col_42', 'col_1', 'col_37'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_a.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LDA Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transform the features\n",
+    "\n",
+    "lda = LDA(n_components=1)\n",
+    "features_lda_1 = lda.fit_transform(features_a, y_train.values.ravel())\n",
+    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "\n",
+    "features_lda_1_test = lda.fit_transform(features_a_test, y_test.values.ravel())\n",
+    "features_lda_2_test = lda.fit_transform(features_b_test, y_test.values.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_lda_1 = pd.DataFrame(features_lda_1)\n",
+    "features_lda_2 = pd.DataFrame(features_lda_2)\n",
+    "\n",
+    "features_lda_1_test = pd.DataFrame(features_lda_1_test)\n",
+    "features_lda_2_test = pd.DataFrame(features_lda_2_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.029022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.368815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.364754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.380754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.348290</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0\n",
+       "0  0.029022\n",
+       "1  0.368815\n",
+       "2  0.364754\n",
+       "3  0.380754\n",
+       "4  0.348290"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_lda_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# combine the 2 features\n",
+    "\n",
+    "x_train_lda = features_lda_1.join(features_lda_2, lsuffix=\"_left\", rsuffix=\"_right\")\n",
+    "x_test_lda = features_lda_1_test.join(features_lda_2_test, lsuffix=\"_left\", rsuffix=\"_right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_left</th>\n",
+       "      <th>0_right</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.029022</td>\n",
+       "      <td>-0.293507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.368815</td>\n",
+       "      <td>0.154781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.364754</td>\n",
+       "      <td>-0.136371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.380754</td>\n",
+       "      <td>-0.466693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.348290</td>\n",
+       "      <td>-1.429328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>0.239483</td>\n",
+       "      <td>-0.752545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>-9.149074</td>\n",
+       "      <td>-1.870988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1397</th>\n",
+       "      <td>0.298241</td>\n",
+       "      <td>0.392145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1398</th>\n",
+       "      <td>-0.939658</td>\n",
+       "      <td>-1.695210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1399</th>\n",
+       "      <td>0.301167</td>\n",
+       "      <td>-0.701224</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1400 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        0_left   0_right\n",
+       "0     0.029022 -0.293507\n",
+       "1     0.368815  0.154781\n",
+       "2     0.364754 -0.136371\n",
+       "3     0.380754 -0.466693\n",
+       "4     0.348290 -1.429328\n",
+       "...        ...       ...\n",
+       "1395  0.239483 -0.752545\n",
+       "1396 -9.149074 -1.870988\n",
+       "1397  0.298241  0.392145\n",
+       "1398 -0.939658 -1.695210\n",
+       "1399  0.301167 -0.701224\n",
+       "\n",
+       "[1400 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0_left</th>\n",
+       "      <th>0_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.552360</td>\n",
+       "      <td>2.152633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.387320</td>\n",
+       "      <td>-0.313518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.547085</td>\n",
+       "      <td>1.668552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.545153</td>\n",
+       "      <td>0.298175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.148260</td>\n",
+       "      <td>-1.430355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>0.510857</td>\n",
+       "      <td>-0.539991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>0.543589</td>\n",
+       "      <td>2.828810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>0.198730</td>\n",
+       "      <td>0.010667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>0.339441</td>\n",
+       "      <td>1.651147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>0.549595</td>\n",
+       "      <td>0.094715</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0_left   0_right\n",
+       "0    0.552360  2.152633\n",
+       "1    0.387320 -0.313518\n",
+       "2    0.547085  1.668552\n",
+       "3    0.545153  0.298175\n",
+       "4    0.148260 -1.430355\n",
+       "..        ...       ...\n",
+       "595  0.510857 -0.539991\n",
+       "596  0.543589  2.828810\n",
+       "597  0.198730  0.010667\n",
+       "598  0.339441  1.651147\n",
+       "599  0.549595  0.094715\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_train_lda)\n",
+    "data = std_scale.transform(x_train_lda)\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_test_lda)\n",
+    "x_test_lda_n = std_scale.transform(x_test_lda)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.53286889,  1.4488161 ],\n",
+       "       [ 0.37365254, -0.2110115 ],\n",
+       "       [ 0.52778047,  1.1230087 ],\n",
+       "       ...,\n",
+       "       [ 0.191717  ,  0.00717903],\n",
+       "       [ 0.32746337,  1.1112944 ],\n",
+       "       [ 0.53020126,  0.06374722]])"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_dim = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        73.071429\n",
+       "1.0        26.928571\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in train\n",
+    "\n",
+    "y_train.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        72.0\n",
+       "1.0        28.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in test\n",
+    "\n",
+    "y_test.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### QML\n",
+    "\n",
+    "We then perform quantum machine learning using the two LDA features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Angle Encoding\n",
+    "\n",
+    "num_qubits = n_dim\n",
+    "\n",
+    "dev = qml.device('default.qubit', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "#dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def circuit(parameters, data):\n",
+    "    for i in range(num_qubits):\n",
+    "        qml.Hadamard(wires = i)\n",
+    "    \n",
+    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "    \n",
+    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "    \n",
+    "    return qml.expval(qml.PauliZ(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
+      "  [ 0.0152303  -0.00234153 -0.00234137]]\n",
+      "\n",
+      " [[ 0.01579213  0.00767435 -0.00469474]\n",
+      "  [ 0.0054256  -0.00463418 -0.0046573 ]]\n",
+      "\n",
+      " [[ 0.00241962 -0.0191328  -0.01724918]\n",
+      "  [-0.00562288 -0.01012831  0.00314247]]\n",
+      "\n",
+      " [[-0.00908024 -0.01412304  0.01465649]\n",
+      "  [-0.00225776  0.00067528 -0.01424748]]\n",
+      "\n",
+      " [[-0.00544383  0.00110923 -0.01150994]\n",
+      "  [ 0.00375698 -0.00600639 -0.00291694]]] 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_layers = 5\n",
+    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
+    "bias_init = np.array(0.0, requires_grad=True)\n",
+    "\n",
+    "print(weights_init, bias_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.01757812, requires_grad=True)"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit(weights_init, data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X = [tensor(0.02854756, requires_grad=False), tensor(-0.20927842, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.36279179, requires_grad=False), tensor(0.11036303, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.35879728, requires_grad=False), tensor(-0.09723657, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.37453586, requires_grad=False), tensor(-0.33276499, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.34260141, requires_grad=False), tensor(-1.01914988, requires_grad=False)], Y = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "X = np.array(data, requires_grad=False)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
+    "batch_size = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.6805348 | Accuracy: 0.7614286 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.6148319 | Accuracy: 0.7678571 \n",
+      "Iter:     3 | Cost: 0.6655624 | Accuracy: 0.7585714 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.6156780 | Accuracy: 0.7814286 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.5232250 | Accuracy: 0.8035714 \n",
+      "New best\n",
+      "Iter:     6 | Cost: 0.4934695 | Accuracy: 0.8407143 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.5084396 | Accuracy: 0.8671429 \n",
+      "Iter:     8 | Cost: 0.4678253 | Accuracy: 0.8592857 \n",
+      "Iter:     9 | Cost: 0.4462772 | Accuracy: 0.8428571 \n",
+      "Iter:    10 | Cost: 0.4559256 | Accuracy: 0.8257143 \n",
+      "Iter:    11 | Cost: 0.4617289 | Accuracy: 0.8221429 \n",
+      "Iter:    12 | Cost: 0.4551549 | Accuracy: 0.8371429 \n",
+      "Iter:    13 | Cost: 0.4708027 | Accuracy: 0.8457143 \n",
+      "Iter:    14 | Cost: 0.4739704 | Accuracy: 0.8542857 \n",
+      "Iter:    15 | Cost: 0.4484922 | Accuracy: 0.8557143 \n",
+      "Iter:    16 | Cost: 0.4309202 | Accuracy: 0.8571429 \n",
+      "Iter:    17 | Cost: 0.4304361 | Accuracy: 0.8421429 \n",
+      "Iter:    18 | Cost: 0.4380497 | Accuracy: 0.8400000 \n",
+      "Iter:    19 | Cost: 0.4884551 | Accuracy: 0.8492857 \n",
+      "Iter:    20 | Cost: 0.5610199 | Accuracy: 0.8435714 \n",
+      "Iter:    21 | Cost: 0.5085669 | Accuracy: 0.8535714 \n",
+      "Iter:    22 | Cost: 0.4507345 | Accuracy: 0.8635714 \n",
+      "Iter:    23 | Cost: 0.4116641 | Accuracy: 0.8671429 \n",
+      "Iter:    24 | Cost: 0.3996521 | Accuracy: 0.8650000 \n",
+      "Iter:    25 | Cost: 0.3943720 | Accuracy: 0.8621429 \n",
+      "Iter:    26 | Cost: 0.3956733 | Accuracy: 0.8635714 \n",
+      "Iter:    27 | Cost: 0.3997569 | Accuracy: 0.8642857 \n",
+      "Iter:    28 | Cost: 0.4042525 | Accuracy: 0.8592857 \n",
+      "Iter:    29 | Cost: 0.4165210 | Accuracy: 0.8650000 \n",
+      "Iter:    30 | Cost: 0.4319129 | Accuracy: 0.8657143 \n",
+      "New best\n",
+      "Iter:    31 | Cost: 0.4441879 | Accuracy: 0.8678571 \n",
+      "New best\n",
+      "Iter:    32 | Cost: 0.4851312 | Accuracy: 0.8692857 \n",
+      "New best\n",
+      "Iter:    33 | Cost: 0.4647126 | Accuracy: 0.8757143 \n",
+      "Iter:    34 | Cost: 0.4018195 | Accuracy: 0.8700000 \n",
+      "Iter:    35 | Cost: 0.3970764 | Accuracy: 0.8614286 \n",
+      "Iter:    36 | Cost: 0.4077688 | Accuracy: 0.8557143 \n",
+      "Iter:    37 | Cost: 0.4022449 | Accuracy: 0.8571429 \n",
+      "Iter:    38 | Cost: 0.4065742 | Accuracy: 0.8557143 \n",
+      "Iter:    39 | Cost: 0.3877503 | Accuracy: 0.8585714 \n",
+      "Iter:    40 | Cost: 0.3806838 | Accuracy: 0.8607143 \n",
+      "Iter:    41 | Cost: 0.3807323 | Accuracy: 0.8607143 \n",
+      "Iter:    42 | Cost: 0.3761005 | Accuracy: 0.8657143 \n",
+      "Iter:    43 | Cost: 0.3762085 | Accuracy: 0.8742857 \n",
+      "Iter:    44 | Cost: 0.3792349 | Accuracy: 0.8735714 \n",
+      "Iter:    45 | Cost: 0.3946513 | Accuracy: 0.8742857 \n",
+      "Iter:    46 | Cost: 0.3932549 | Accuracy: 0.8757143 \n",
+      "Iter:    47 | Cost: 0.3902515 | Accuracy: 0.8742857 \n",
+      "Iter:    48 | Cost: 0.3778917 | Accuracy: 0.8750000 \n",
+      "Iter:    49 | Cost: 0.3754754 | Accuracy: 0.8678571 \n",
+      "Iter:    50 | Cost: 0.3804304 | Accuracy: 0.8585714 \n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = weights_init\n",
+    "bias = bias_init\n",
+    "\n",
+    "wbest = 0\n",
+    "bbest = 0\n",
+    "abest = 0\n",
+    "\n",
+    "for it in range(50):\n",
+    "\n",
+    "    # weights update by one optimizer step\n",
+    "\n",
+    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "    X_batch = X[batch_index]\n",
+    "    Y_batch = Y[batch_index]\n",
+    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "    # Compute the accuracy\n",
+    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "    \n",
+    "    if accuracy(Y, predictions) > abest:\n",
+    "        wbest = weights\n",
+    "        bbest = bias\n",
+    "        abest = accuracy(Y, predictions)\n",
+    "        print('New best')\n",
+    "\n",
+    "    acc = accuracy(Y, predictions)\n",
+    "\n",
+    "    print(\n",
+    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "            it + 1, cost(weights, bias, X, Y), acc\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
+    "Xte = np.array(normalize(x_test_lda_n), requires_grad=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost: 0.42114813175434823, Accuracy: 88.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
+    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
+    "acc = accuracy(Yte, predictions)\n",
+    "\n",
+    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions  Test\n",
+       "0           1.0   1.0\n",
+       "1          -1.0  -1.0\n",
+       "2           1.0   1.0\n",
+       "3           1.0  -1.0\n",
+       "4          -1.0  -1.0\n",
+       "..          ...   ...\n",
+       "595        -1.0  -1.0\n",
+       "596         1.0   1.0\n",
+       "597        -1.0  -1.0\n",
+       "598         1.0   1.0\n",
+       "599        -1.0   1.0\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.93      0.91      0.92       443\n",
+      "         1.0       0.75      0.80      0.78       157\n",
+      "\n",
+      "    accuracy                           0.88       600\n",
+      "   macro avg       0.84      0.85      0.85       600\n",
+      "weighted avg       0.88      0.88      0.88       600\n",
+      "\n",
+      "precisioin score: 0.75\n",
+      "recall score: 0.802547770700637\n",
+      "f1 score: 0.7753846153846153\n",
+      "balanced accuracy: 0.8538698221449008\n",
+      "confusion matrix: [[401  42]\n",
+      " [ 31 126]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the classification report and important metrics\n",
+    "\n",
+    "print(metrics.classification_report(predictions,Yte))\n",
+    "print(f\"precisioin score: {metrics.precision_score(predictions,Yte)}\")\n",
+    "print(f\"recall score: {metrics.recall_score(predictions,Yte)}\")\n",
+    "print(f\"f1 score: {metrics.f1_score(predictions,Yte)}\")\n",
+    "print(f\"balanced accuracy: {metrics.balanced_accuracy_score(predictions,Yte)}\")\n",
+    "print(f\"confusion matrix: {metrics.confusion_matrix(predictions,Yte)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the results are overall better than the half&half split."
+   ]
   }
  ],
  "metadata": {
diff --git a/QML4FRAUD/dataset_nonzero.csv b/QML4FRAUD/dataset_cleaned.csv
similarity index 52%
rename from QML4FRAUD/dataset_nonzero.csv
rename to QML4FRAUD/dataset_cleaned.csv
index 76b339e..276fc94 100644
--- a/QML4FRAUD/dataset_nonzero.csv
+++ b/QML4FRAUD/dataset_cleaned.csv
@@ -1,20469 +1,20469 @@
-,col_0,col_1,col_2,col_3,col_4,col_5,col_6,col_7,col_13,col_14,col_15,col_16,col_17,col_22,col_23,col_24,col_25,col_26,col_27,col_28,col_29,col_30,col_31,col_32,col_33,col_34,col_36,col_37,col_38,col_39,col_40,col_41,col_42,col_43,col_44,col_45,col_46,col_47,col_48,col_49,col_50,col_54,col_55,col_56,col_57,col_58,col_59,col_60,col_61,col_62,col_63,col_64,col_65,col_66,col_67,col_68,col_69,col_72,col_73,col_74,col_75,col_76,col_77,col_78,col_79,col_80,col_81,col_82,col_83,col_84,col_85,col_86,col_87,col_88,col_89,col_90,col_91,col_92,col_93,col_94,col_95,col_96,col_97,col_98,col_99,col_100,col_101,col_102,col_103,col_104,col_105,col_106,col_107,col_108,col_109,col_110,col_111,targets
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-1994,0.0,12.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,70.0,1.0
-1995,0.0,35.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,50.0,1.0
-1996,0.0,148.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,7.0,1.0,0.0,0.0,0.0,1.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,92.0,1.0
-1997,3.0,1210.0,0.0,1.0,0.0,1.0,0.0,3.0,0.0,1.0,0.0,1.0,0.0,3.0,97.0,1.0,0.0,0.0,0.0,1.0,0.0,22.0,0.0,0.0,0.0,15.0,13.0,0.0,1116.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,106.0,2.0,11.0,18.0,1084.0,8.0,0.0,15.0,0.0,0.0,2.0,0.0,0.0,101.0,2.0,10.0,46.0,3292.2101227,652.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-1998,0.0,78.0,0.0,1.0,0.0,1.0,7.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,1.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,77.0,1.0
-1999,0.0,23.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2000,0.0,11.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2001,0.0,74.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,72.0,1.0
-2002,2.0,202.0,0.0,0.0,0.0,2.0,0.0,2.0,0.0,1.0,0.0,1.0,0.0,2.0,18.0,2.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,82.0,1.0
-2003,0.0,67.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,112.0,1.0
-2004,0.0,59.0,0.0,1.0,0.0,2.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,2.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,65.0,1.0
-2005,0.0,17.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2006,0.0,44.0,0.0,1.0,0.0,1.0,-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,65.0,1.0
-2007,0.0,27.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4774.0,0.0,3.0,2.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2008,0.0,27.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,829.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2009,0.0,30.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,100.0,1.0
-2010,11.0,363.0,0.0,0.0,0.0,1.0,0.0,11.0,0.0,0.0,0.0,1.0,0.0,12.0,48.0,1.0,0.0,0.0,0.0,1.0,0.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
-2011,0.0,90.0,0.0,0.0,0.0,2.0,5.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,4.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,67.0,1.0
-2012,0.0,39.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,70.0,1.0
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-2019,0.0,23.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
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-2021,0.0,51.0,0.0,1.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,2.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,46.0,1.0
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From d4b73aee1f6e9de7868155881e531c8a177b7e0a Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 15:53:55 +0800
Subject: [PATCH 08/25] Update data.py

---
 QML4FRAUD/Factory/data.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/QML4FRAUD/Factory/data.py b/QML4FRAUD/Factory/data.py
index a5dcde5..070a0dc 100644
--- a/QML4FRAUD/Factory/data.py
+++ b/QML4FRAUD/Factory/data.py
@@ -79,19 +79,22 @@ def perform_LDA(self, n_dim = 2):
             features_test.append(new_set_test)
         
         # Run the LDA
-        lda = LDA(n_components= (n_dim - 1))
         features_lda_train = []
         features_lda_test = []
+        LDA_transformations = []
         
         for i in range(n_dim):
+            lda = LDA(n_components= (n_dim - 1))
             features_lda_train_new = lda.fit_transform(features_train[i], self.y_train)
             features_lda_train.append(pd.DataFrame(features_lda_train_new))
+            LDA_transformations.append(lda)
             
-            features_lda_test_new = lda.fit_transform(features_test[i], self.y_test)
+            features_lda_test_new = lda.transform(features_test[i])
             features_lda_test.append(pd.DataFrame(features_lda_test_new))
         
         x_train_data = features_lda_train[0]
         x_test_data = features_lda_test[0]
+        self.transformations = LDA_transformations
         
         # Join the results together
         for i in range(1, n_dim):
@@ -104,8 +107,7 @@ def perform_LDA(self, n_dim = 2):
         std_scale_train = StandardScaler().fit(x_train_data)
         x_train_data = std_scale_train.transform(x_train_data)
         
-        std_scale_test = StandardScaler().fit(x_test_data)
-        x_test_data = std_scale_test.transform(x_test_data)
+        x_test_data = std_scale_train.transform(x_test_data)
             
         # shift label from {0, 1} to {-1, 1}
         self.train_X_preprocessed = np.array(x_train_data, requires_grad=False)
@@ -125,8 +127,8 @@ def perform_PCA(self, n_dim = 2):
         pca = PCA(n_components=n_dim, svd_solver='full')
         pca.fit(self.x_train)
         x_train_pca = pca.transform(self.x_train)
-        pca.fit(self.x_test)
         x_test_pca = pca.transform(self.x_test)
+        self.transformations = pca
         
         train_X_preprocessed = normalize(x_train_pca)
         test_X_preprocessed = normalize(x_test_pca)

From 3c62e7684262ed771055b773dcdc00c2083b7ac2 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 17:13:46 +0800
Subject: [PATCH 09/25] Update data.py

---
 QML4FRAUD/Factory/data.py | 40 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 37 insertions(+), 3 deletions(-)

diff --git a/QML4FRAUD/Factory/data.py b/QML4FRAUD/Factory/data.py
index 070a0dc..002f7f7 100644
--- a/QML4FRAUD/Factory/data.py
+++ b/QML4FRAUD/Factory/data.py
@@ -1,5 +1,6 @@
 import pandas as pd
 import numpy as np
+import copy
 from pennylane import numpy as np
 from sklearn.preprocessing import normalize
 from sklearn.preprocessing import StandardScaler
@@ -36,8 +37,16 @@ def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):
         self.x_train = self.train_set.drop(target, axis=1)
         self.x_test = self.test_set.drop(target, axis=1)
         
+        self.x_train = self.x_train.loc[:, self.x_train.any()]
+        corr = self.x_train.corr()
+        values_dont_change = corr.isnull().values
+        for i in range(len(values_dont_change)):
+            if values_dont_change[0][i]:
+                index_to_drop = i
+        self.x_train = self.x_train.drop(self.x_train.columns[index_to_drop], axis = 1)
+        
     def view_info(self):
-        print(self.data_sample.info())
+        print(self.x_train.info())
         if self.preprocess_done == None:
             print("No preprocessing done yet.")
         else:
@@ -71,11 +80,36 @@ def perform_LDA(self, n_dim = 2):
         features_test = []
         
         # Split Features (for Yaqi to change)
+        group_columns = []
+        df_clean_train_copy = copy.deepcopy(self.x_train)
+        group_size = int(len(df_clean_train_copy.columns) / n_dim)
+        
+        for i in range(n_dim):
+            corr_train = df_clean_train_copy.corr()
+
+            max_value = 0
+            for j in df_clean_train_copy.columns:
+                #print(corr_train[j].abs().sort_values(ascending=False))
+                if corr_train[j].abs().sort_values(ascending=False)[group_size - 1] >= max_value:
+                    saved = j
+                    max_value = corr_train[j].abs().sort_values(ascending=False)[group_size - 1]
+            
+            #print(saved)
+            #print(max_value)
+            #print(corr_train)
+            #print(df_clean_train_copy.columns)
+            #print(corr_train.columns)
+            indices = corr_train[saved].abs().sort_values(ascending=False).index
+            df_clean_train_copy = df_clean_train_copy[indices]
+            new_columns = df_clean_train_copy.columns[:group_size]
+            group_columns.append(new_columns)
+            df_clean_train_copy = df_clean_train_copy.iloc[:, (group_size):]
+
         for i in range(n_dim):
-            new_set_train = self.x_train.iloc[:,(i*split_feature):((i+1)*split_feature)]
+            new_set_train = self.x_train[group_columns[i]]
             features_train.append(new_set_train)
             
-            new_set_test = self.x_test.iloc[:,(i*split_feature):((i+1)*split_feature)]
+            new_set_test = self.x_test[group_columns[i]]
             features_test.append(new_set_test)
         
         # Run the LDA

From fe68d78b53e6a6c7369ac59880b4b4e62d0ba2e9 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 17:21:02 +0800
Subject: [PATCH 10/25] Update data.py

---
 QML4FRAUD/Factory/data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/QML4FRAUD/Factory/data.py b/QML4FRAUD/Factory/data.py
index 002f7f7..aaf798d 100644
--- a/QML4FRAUD/Factory/data.py
+++ b/QML4FRAUD/Factory/data.py
@@ -43,7 +43,7 @@ def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):
         for i in range(len(values_dont_change)):
             if values_dont_change[0][i]:
                 index_to_drop = i
-        self.x_train = self.x_train.drop(self.x_train.columns[index_to_drop], axis = 1)
+                self.x_train = self.x_train.drop(self.x_train.columns[index_to_drop], axis = 1)
         
     def view_info(self):
         print(self.x_train.info())

From f30d5d15ba800a3b7bb2935d8ad98e4c2f85acb7 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 20:07:20 +0800
Subject: [PATCH 11/25] update experiments

---
 .../Experimentation-checkpoint.ipynb          | 1922 +++++++++
 QML4FRAUD/Factory/data.py                     |    4 +-
 QML4FRAUD/Solution via architecture.ipynb     | 3434 +++++++++++++----
 3 files changed, 4542 insertions(+), 818 deletions(-)
 create mode 100644 QML4FRAUD/.ipynb_checkpoints/Experimentation-checkpoint.ipynb

diff --git a/QML4FRAUD/.ipynb_checkpoints/Experimentation-checkpoint.ipynb b/QML4FRAUD/.ipynb_checkpoints/Experimentation-checkpoint.ipynb
new file mode 100644
index 0000000..e6b9166
--- /dev/null
+++ b/QML4FRAUD/.ipynb_checkpoints/Experimentation-checkpoint.ipynb
@@ -0,0 +1,1922 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f66fdd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\_distutils_hack\\__init__.py:30: UserWarning: Setuptools is replacing distutils.\n",
+      "  warnings.warn(\"Setuptools is replacing distutils.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pennylane import numpy as np\n",
+    "from Factory.data import PrepareData\n",
+    "from Factory.model import QBC\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0344a9",
+   "metadata": {},
+   "source": [
+    "# Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4006e08",
+   "metadata": {},
+   "source": [
+    "Load data in"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3722cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('fraud_detection_bank_dataset.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8e1fd042",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
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+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_8</th>\n",
+       "      <th>col_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1354.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>682.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20463</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20464</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20465</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>393.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20466</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20467</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>399.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20468 rows × 113 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col_0   col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9  \\\n",
+       "0        9.0  1354.0    0.0   18.0    0.0    1.0    7.0    9.0    0.0    0.0   \n",
+       "1        0.0   239.0    0.0    1.0    0.0    1.0    0.0    0.0    0.0    0.0   \n",
+       "2        0.0   260.0    0.0    4.0    0.0    3.0    6.0    0.0    0.0    0.0   \n",
+       "3       17.0   682.0    0.0    1.0    0.0    0.0    8.0   17.0    0.0    0.0   \n",
+       "4        1.0   540.0    0.0    2.0    0.0    1.0    7.0    1.0    0.0    0.0   \n",
+       "...      ...     ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
+       "20463    0.0    88.0    0.0    0.0    0.0    2.0   -1.0    0.0    0.0    0.0   \n",
+       "20464    0.0   134.0    0.0    2.0    0.0    0.0    6.0    0.0    0.0    0.0   \n",
+       "20465    4.0   393.0    1.0    1.0    0.0    0.0   -1.0    4.0    0.0    0.0   \n",
+       "20466    0.0    10.0    0.0    1.0    0.0    0.0   -1.0    0.0    0.0    0.0   \n",
+       "20467    4.0   399.0    0.0    3.0    0.0    1.0    7.0    4.0    0.0    0.0   \n",
+       "\n",
+       "       ...  col_103  col_104  col_105  col_106  col_107  col_108  col_109  \\\n",
+       "0      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "1      ...      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "2      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "3      ...      0.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "4      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "...    ...      ...      ...      ...      ...      ...      ...      ...   \n",
+       "20463  ...      0.0      1.0      0.0      0.0      0.0      0.0      1.0   \n",
+       "20464  ...      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20465  ...      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20466  ...      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20467  ...      0.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "\n",
+       "       col_110  col_111  targets  \n",
+       "0          0.0     49.0      1.0  \n",
+       "1          0.0     55.0      1.0  \n",
+       "2          0.0     56.0      1.0  \n",
+       "3          0.0     65.0      1.0  \n",
+       "4          0.0    175.0      1.0  \n",
+       "...        ...      ...      ...  \n",
+       "20463      0.0     85.0      0.0  \n",
+       "20464      0.0      7.0      0.0  \n",
+       "20465      0.0     45.0      0.0  \n",
+       "20466      0.0      5.0      0.0  \n",
+       "20467      0.0     72.0      0.0  \n",
+       "\n",
+       "[20468 rows x 113 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f66a23f",
+   "metadata": {},
+   "source": [
+    "Clean data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "39997733",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_7</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>294.793043</td>\n",
+       "      <td>0.420021</td>\n",
+       "      <td>2.329343</td>\n",
+       "      <td>0.083594</td>\n",
+       "      <td>0.939857</td>\n",
+       "      <td>2.386066</td>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>0.038646</td>\n",
+       "      <td>0.363152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004935</td>\n",
+       "      <td>0.358120</td>\n",
+       "      <td>0.002443</td>\n",
+       "      <td>0.308384</td>\n",
+       "      <td>0.190737</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.049345</td>\n",
+       "      <td>0.024282</td>\n",
+       "      <td>43.486125</td>\n",
+       "      <td>0.265683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>717.541984</td>\n",
+       "      <td>7.367275</td>\n",
+       "      <td>10.068512</td>\n",
+       "      <td>0.840537</td>\n",
+       "      <td>4.222896</td>\n",
+       "      <td>3.018140</td>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>0.192754</td>\n",
+       "      <td>0.480920</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.073478</td>\n",
+       "      <td>0.479459</td>\n",
+       "      <td>0.049366</td>\n",
+       "      <td>0.461837</td>\n",
+       "      <td>0.392892</td>\n",
+       "      <td>0.006990</td>\n",
+       "      <td>0.216593</td>\n",
+       "      <td>0.305079</td>\n",
+       "      <td>59.217560</td>\n",
+       "      <td>0.441707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>283.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>37808.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>772.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 98 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              col_0         col_1         col_2         col_3         col_4  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       3.226256    294.793043      0.420021      2.329343      0.083594   \n",
+       "std       20.564308    717.541984      7.367275     10.068512      0.840537   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000     38.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000     97.000000      0.000000      1.000000      0.000000   \n",
+       "75%        2.000000    283.000000      0.000000      2.000000      0.000000   \n",
+       "max     2301.000000  37808.000000    904.000000    772.000000     54.000000   \n",
+       "\n",
+       "              col_5         col_6         col_7        col_13        col_14  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.939857      2.386066      3.226256      0.038646      0.363152   \n",
+       "std        4.222896      3.018140     20.564308      0.192754      0.480920   \n",
+       "min        0.000000     -1.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000      2.000000      0.000000      0.000000      0.000000   \n",
+       "75%        1.000000      6.000000      2.000000      0.000000      1.000000   \n",
+       "max      230.000000     11.000000   2301.000000      1.000000      1.000000   \n",
+       "\n",
+       "       ...       col_103       col_104       col_105       col_106  \\\n",
+       "count  ...  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean   ...      0.004935      0.358120      0.002443      0.308384   \n",
+       "std    ...      0.073478      0.479459      0.049366      0.461837   \n",
+       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%    ...      0.000000      1.000000      0.000000      1.000000   \n",
+       "max    ...      2.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "            col_107       col_108       col_109       col_110       col_111  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.190737      0.000049      0.049345      0.024282     43.486125   \n",
+       "std        0.392892      0.006990      0.216593      0.305079     59.217560   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      5.000000   \n",
+       "50%        0.000000      0.000000      0.000000      0.000000     19.000000   \n",
+       "75%        0.000000      0.000000      0.000000      0.000000     61.250000   \n",
+       "max        1.000000      1.000000      1.000000     17.000000    747.000000   \n",
+       "\n",
+       "            targets  \n",
+       "count  20468.000000  \n",
+       "mean       0.265683  \n",
+       "std        0.441707  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        1.000000  \n",
+       "max        1.000000  \n",
+       "\n",
+       "[8 rows x 98 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean = df.loc[:, df.any()]\n",
+    "df_clean.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5689e1dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_clean = df_clean.drop('col_7', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ba78a9cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_clean = df_clean.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ae7d5c80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    20468.000000\n",
+       "mean         0.999951\n",
+       "std          0.006990\n",
+       "min          0.000000\n",
+       "25%          1.000000\n",
+       "50%          1.000000\n",
+       "75%          1.000000\n",
+       "max          1.000000\n",
+       "Name: col_28, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean[\"col_28\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd014c56",
+   "metadata": {},
+   "source": [
+    "Prepare data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b9722cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = PrepareData(data = df_clean,target = \"targets\", sample_size = 4000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a48959d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2800 entries, 18511 to 18390\n",
+      "Data columns (total 93 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   col_0    2800 non-null   float64\n",
+      " 1   col_1    2800 non-null   float64\n",
+      " 2   col_2    2800 non-null   float64\n",
+      " 3   col_3    2800 non-null   float64\n",
+      " 4   col_4    2800 non-null   float64\n",
+      " 5   col_5    2800 non-null   float64\n",
+      " 6   col_6    2800 non-null   float64\n",
+      " 7   col_13   2800 non-null   float64\n",
+      " 8   col_14   2800 non-null   float64\n",
+      " 9   col_15   2800 non-null   float64\n",
+      " 10  col_16   2800 non-null   float64\n",
+      " 11  col_17   2800 non-null   float64\n",
+      " 12  col_22   2800 non-null   float64\n",
+      " 13  col_23   2800 non-null   float64\n",
+      " 14  col_24   2800 non-null   float64\n",
+      " 15  col_25   2800 non-null   float64\n",
+      " 16  col_26   2800 non-null   float64\n",
+      " 17  col_27   2800 non-null   float64\n",
+      " 18  col_28   2800 non-null   float64\n",
+      " 19  col_29   2800 non-null   float64\n",
+      " 20  col_30   2800 non-null   float64\n",
+      " 21  col_31   2800 non-null   float64\n",
+      " 22  col_32   2800 non-null   float64\n",
+      " 23  col_33   2800 non-null   float64\n",
+      " 24  col_34   2800 non-null   float64\n",
+      " 25  col_36   2800 non-null   float64\n",
+      " 26  col_37   2800 non-null   float64\n",
+      " 27  col_38   2800 non-null   float64\n",
+      " 28  col_39   2800 non-null   float64\n",
+      " 29  col_40   2800 non-null   float64\n",
+      " 30  col_41   2800 non-null   float64\n",
+      " 31  col_42   2800 non-null   float64\n",
+      " 32  col_43   2800 non-null   float64\n",
+      " 33  col_44   2800 non-null   float64\n",
+      " 34  col_45   2800 non-null   float64\n",
+      " 35  col_46   2800 non-null   float64\n",
+      " 36  col_47   2800 non-null   float64\n",
+      " 37  col_48   2800 non-null   float64\n",
+      " 38  col_49   2800 non-null   float64\n",
+      " 39  col_50   2800 non-null   float64\n",
+      " 40  col_54   2800 non-null   float64\n",
+      " 41  col_55   2800 non-null   float64\n",
+      " 42  col_56   2800 non-null   float64\n",
+      " 43  col_57   2800 non-null   float64\n",
+      " 44  col_59   2800 non-null   float64\n",
+      " 45  col_60   2800 non-null   float64\n",
+      " 46  col_61   2800 non-null   float64\n",
+      " 47  col_62   2800 non-null   float64\n",
+      " 48  col_63   2800 non-null   float64\n",
+      " 49  col_64   2800 non-null   float64\n",
+      " 50  col_65   2800 non-null   float64\n",
+      " 51  col_66   2800 non-null   float64\n",
+      " 52  col_67   2800 non-null   float64\n",
+      " 53  col_68   2800 non-null   float64\n",
+      " 54  col_69   2800 non-null   float64\n",
+      " 55  col_72   2800 non-null   float64\n",
+      " 56  col_73   2800 non-null   float64\n",
+      " 57  col_74   2800 non-null   float64\n",
+      " 58  col_75   2800 non-null   float64\n",
+      " 59  col_76   2800 non-null   float64\n",
+      " 60  col_77   2800 non-null   float64\n",
+      " 61  col_79   2800 non-null   float64\n",
+      " 62  col_80   2800 non-null   float64\n",
+      " 63  col_81   2800 non-null   float64\n",
+      " 64  col_82   2800 non-null   float64\n",
+      " 65  col_83   2800 non-null   float64\n",
+      " 66  col_84   2800 non-null   float64\n",
+      " 67  col_85   2800 non-null   float64\n",
+      " 68  col_86   2800 non-null   float64\n",
+      " 69  col_87   2800 non-null   float64\n",
+      " 70  col_88   2800 non-null   float64\n",
+      " 71  col_89   2800 non-null   float64\n",
+      " 72  col_90   2800 non-null   float64\n",
+      " 73  col_91   2800 non-null   float64\n",
+      " 74  col_92   2800 non-null   float64\n",
+      " 75  col_93   2800 non-null   float64\n",
+      " 76  col_94   2800 non-null   float64\n",
+      " 77  col_95   2800 non-null   float64\n",
+      " 78  col_96   2800 non-null   float64\n",
+      " 79  col_97   2800 non-null   float64\n",
+      " 80  col_98   2800 non-null   float64\n",
+      " 81  col_99   2800 non-null   float64\n",
+      " 82  col_100  2800 non-null   float64\n",
+      " 83  col_101  2800 non-null   float64\n",
+      " 84  col_102  2800 non-null   float64\n",
+      " 85  col_103  2800 non-null   float64\n",
+      " 86  col_104  2800 non-null   float64\n",
+      " 87  col_105  2800 non-null   float64\n",
+      " 88  col_106  2800 non-null   float64\n",
+      " 89  col_107  2800 non-null   float64\n",
+      " 90  col_109  2800 non-null   float64\n",
+      " 91  col_110  2800 non-null   float64\n",
+      " 92  col_111  2800 non-null   float64\n",
+      "dtypes: float64(93)\n",
+      "memory usage: 2.0 MB\n",
+      "None\n",
+      "No preprocessing done yet.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.00000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.00000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.0</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.107750</td>\n",
+       "      <td>303.98450</td>\n",
+       "      <td>0.350500</td>\n",
+       "      <td>2.55325</td>\n",
+       "      <td>0.085500</td>\n",
+       "      <td>0.876500</td>\n",
+       "      <td>2.418500</td>\n",
+       "      <td>0.044500</td>\n",
+       "      <td>0.374250</td>\n",
+       "      <td>0.335250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005750</td>\n",
+       "      <td>0.357500</td>\n",
+       "      <td>0.002250</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.197500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.052500</td>\n",
+       "      <td>0.028000</td>\n",
+       "      <td>45.087750</td>\n",
+       "      <td>0.254000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.545192</td>\n",
+       "      <td>693.90412</td>\n",
+       "      <td>3.502601</td>\n",
+       "      <td>15.23119</td>\n",
+       "      <td>0.702006</td>\n",
+       "      <td>3.092028</td>\n",
+       "      <td>3.037213</td>\n",
+       "      <td>0.206229</td>\n",
+       "      <td>0.483989</td>\n",
+       "      <td>0.472137</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084963</td>\n",
+       "      <td>0.479324</td>\n",
+       "      <td>0.047387</td>\n",
+       "      <td>0.464374</td>\n",
+       "      <td>0.398163</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.223061</td>\n",
+       "      <td>0.340213</td>\n",
+       "      <td>61.890857</td>\n",
+       "      <td>0.435352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>296.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>312.000000</td>\n",
+       "      <td>19687.00000</td>\n",
+       "      <td>194.000000</td>\n",
+       "      <td>772.00000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>125.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>581.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  4000.000000   4000.00000  4000.000000  4000.00000  4000.000000   \n",
+       "mean      3.107750    303.98450     0.350500     2.55325     0.085500   \n",
+       "std      11.545192    693.90412     3.502601    15.23119     0.702006   \n",
+       "min       0.000000      0.00000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000     38.00000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000     98.00000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000    296.00000     0.000000     2.00000     0.000000   \n",
+       "max     312.000000  19687.00000   194.000000   772.00000    26.000000   \n",
+       "\n",
+       "             col_5        col_6       col_13       col_14       col_15  ...  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  ...   \n",
+       "mean      0.876500     2.418500     0.044500     0.374250     0.335250  ...   \n",
+       "std       3.092028     3.037213     0.206229     0.483989     0.472137  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.000000     0.000000  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.000000     0.000000  ...   \n",
+       "75%       1.000000     6.000000     0.000000     1.000000     1.000000  ...   \n",
+       "max     125.000000     8.000000     1.000000     1.000000     1.000000  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      0.005750     0.357500     0.002250     0.314500     0.197500   \n",
+       "std       0.084963     0.479324     0.047387     0.464374     0.398163   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   4000.0  4000.000000  4000.000000  4000.000000  4000.000000  \n",
+       "mean       0.0     0.052500     0.028000    45.087750     0.254000  \n",
+       "std        0.0     0.223061     0.340213    61.890857     0.435352  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    19.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.000000    10.000000   581.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00c69d2",
+   "metadata": {},
+   "source": [
+    "# Use LDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "383f00dc",
+   "metadata": {},
+   "source": [
+    "Preprocess LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6b284065",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18511     1.0\n",
+       "16368     3.0\n",
+       "14317     4.0\n",
+       "17822     2.0\n",
+       "86       20.0\n",
+       "         ... \n",
+       "18427     1.0\n",
+       "13519     4.0\n",
+       "15688     3.0\n",
+       "11065     0.0\n",
+       "18390     2.0\n",
+       "Name: col_69, Length: 2800, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.x_train[\"col_69\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dd8fb0b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing LDA...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d64e1dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2800 entries, 18511 to 18390\n",
+      "Data columns (total 93 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   col_0    2800 non-null   float64\n",
+      " 1   col_1    2800 non-null   float64\n",
+      " 2   col_2    2800 non-null   float64\n",
+      " 3   col_3    2800 non-null   float64\n",
+      " 4   col_4    2800 non-null   float64\n",
+      " 5   col_5    2800 non-null   float64\n",
+      " 6   col_6    2800 non-null   float64\n",
+      " 7   col_13   2800 non-null   float64\n",
+      " 8   col_14   2800 non-null   float64\n",
+      " 9   col_15   2800 non-null   float64\n",
+      " 10  col_16   2800 non-null   float64\n",
+      " 11  col_17   2800 non-null   float64\n",
+      " 12  col_22   2800 non-null   float64\n",
+      " 13  col_23   2800 non-null   float64\n",
+      " 14  col_24   2800 non-null   float64\n",
+      " 15  col_25   2800 non-null   float64\n",
+      " 16  col_26   2800 non-null   float64\n",
+      " 17  col_27   2800 non-null   float64\n",
+      " 18  col_28   2800 non-null   float64\n",
+      " 19  col_29   2800 non-null   float64\n",
+      " 20  col_30   2800 non-null   float64\n",
+      " 21  col_31   2800 non-null   float64\n",
+      " 22  col_32   2800 non-null   float64\n",
+      " 23  col_33   2800 non-null   float64\n",
+      " 24  col_34   2800 non-null   float64\n",
+      " 25  col_36   2800 non-null   float64\n",
+      " 26  col_37   2800 non-null   float64\n",
+      " 27  col_38   2800 non-null   float64\n",
+      " 28  col_39   2800 non-null   float64\n",
+      " 29  col_40   2800 non-null   float64\n",
+      " 30  col_41   2800 non-null   float64\n",
+      " 31  col_42   2800 non-null   float64\n",
+      " 32  col_43   2800 non-null   float64\n",
+      " 33  col_44   2800 non-null   float64\n",
+      " 34  col_45   2800 non-null   float64\n",
+      " 35  col_46   2800 non-null   float64\n",
+      " 36  col_47   2800 non-null   float64\n",
+      " 37  col_48   2800 non-null   float64\n",
+      " 38  col_49   2800 non-null   float64\n",
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+      " 40  col_54   2800 non-null   float64\n",
+      " 41  col_55   2800 non-null   float64\n",
+      " 42  col_56   2800 non-null   float64\n",
+      " 43  col_57   2800 non-null   float64\n",
+      " 44  col_59   2800 non-null   float64\n",
+      " 45  col_60   2800 non-null   float64\n",
+      " 46  col_61   2800 non-null   float64\n",
+      " 47  col_62   2800 non-null   float64\n",
+      " 48  col_63   2800 non-null   float64\n",
+      " 49  col_64   2800 non-null   float64\n",
+      " 50  col_65   2800 non-null   float64\n",
+      " 51  col_66   2800 non-null   float64\n",
+      " 52  col_67   2800 non-null   float64\n",
+      " 53  col_68   2800 non-null   float64\n",
+      " 54  col_69   2800 non-null   float64\n",
+      " 55  col_72   2800 non-null   float64\n",
+      " 56  col_73   2800 non-null   float64\n",
+      " 57  col_74   2800 non-null   float64\n",
+      " 58  col_75   2800 non-null   float64\n",
+      " 59  col_76   2800 non-null   float64\n",
+      " 60  col_77   2800 non-null   float64\n",
+      " 61  col_79   2800 non-null   float64\n",
+      " 62  col_80   2800 non-null   float64\n",
+      " 63  col_81   2800 non-null   float64\n",
+      " 64  col_82   2800 non-null   float64\n",
+      " 65  col_83   2800 non-null   float64\n",
+      " 66  col_84   2800 non-null   float64\n",
+      " 67  col_85   2800 non-null   float64\n",
+      " 68  col_86   2800 non-null   float64\n",
+      " 69  col_87   2800 non-null   float64\n",
+      " 70  col_88   2800 non-null   float64\n",
+      " 71  col_89   2800 non-null   float64\n",
+      " 72  col_90   2800 non-null   float64\n",
+      " 73  col_91   2800 non-null   float64\n",
+      " 74  col_92   2800 non-null   float64\n",
+      " 75  col_93   2800 non-null   float64\n",
+      " 76  col_94   2800 non-null   float64\n",
+      " 77  col_95   2800 non-null   float64\n",
+      " 78  col_96   2800 non-null   float64\n",
+      " 79  col_97   2800 non-null   float64\n",
+      " 80  col_98   2800 non-null   float64\n",
+      " 81  col_99   2800 non-null   float64\n",
+      " 82  col_100  2800 non-null   float64\n",
+      " 83  col_101  2800 non-null   float64\n",
+      " 84  col_102  2800 non-null   float64\n",
+      " 85  col_103  2800 non-null   float64\n",
+      " 86  col_104  2800 non-null   float64\n",
+      " 87  col_105  2800 non-null   float64\n",
+      " 88  col_106  2800 non-null   float64\n",
+      " 89  col_107  2800 non-null   float64\n",
+      " 90  col_109  2800 non-null   float64\n",
+      " 91  col_110  2800 non-null   float64\n",
+      " 92  col_111  2800 non-null   float64\n",
+      "dtypes: float64(93)\n",
+      "memory usage: 2.0 MB\n",
+      "None\n",
+      "Preprocessing done via:  LDA\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.00000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.00000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.0</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.107750</td>\n",
+       "      <td>303.98450</td>\n",
+       "      <td>0.350500</td>\n",
+       "      <td>2.55325</td>\n",
+       "      <td>0.085500</td>\n",
+       "      <td>0.876500</td>\n",
+       "      <td>2.418500</td>\n",
+       "      <td>0.044500</td>\n",
+       "      <td>0.374250</td>\n",
+       "      <td>0.335250</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005750</td>\n",
+       "      <td>0.357500</td>\n",
+       "      <td>0.002250</td>\n",
+       "      <td>0.314500</td>\n",
+       "      <td>0.197500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.052500</td>\n",
+       "      <td>0.028000</td>\n",
+       "      <td>45.087750</td>\n",
+       "      <td>0.254000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.545192</td>\n",
+       "      <td>693.90412</td>\n",
+       "      <td>3.502601</td>\n",
+       "      <td>15.23119</td>\n",
+       "      <td>0.702006</td>\n",
+       "      <td>3.092028</td>\n",
+       "      <td>3.037213</td>\n",
+       "      <td>0.206229</td>\n",
+       "      <td>0.483989</td>\n",
+       "      <td>0.472137</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084963</td>\n",
+       "      <td>0.479324</td>\n",
+       "      <td>0.047387</td>\n",
+       "      <td>0.464374</td>\n",
+       "      <td>0.398163</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.223061</td>\n",
+       "      <td>0.340213</td>\n",
+       "      <td>61.890857</td>\n",
+       "      <td>0.435352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>296.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>312.000000</td>\n",
+       "      <td>19687.00000</td>\n",
+       "      <td>194.000000</td>\n",
+       "      <td>772.00000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>125.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>581.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0        col_1        col_2       col_3        col_4  \\\n",
+       "count  4000.000000   4000.00000  4000.000000  4000.00000  4000.000000   \n",
+       "mean      3.107750    303.98450     0.350500     2.55325     0.085500   \n",
+       "std      11.545192    693.90412     3.502601    15.23119     0.702006   \n",
+       "min       0.000000      0.00000     0.000000     0.00000     0.000000   \n",
+       "25%       0.000000     38.00000     0.000000     0.00000     0.000000   \n",
+       "50%       0.000000     98.00000     0.000000     1.00000     0.000000   \n",
+       "75%       2.000000    296.00000     0.000000     2.00000     0.000000   \n",
+       "max     312.000000  19687.00000   194.000000   772.00000    26.000000   \n",
+       "\n",
+       "             col_5        col_6       col_13       col_14       col_15  ...  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  ...   \n",
+       "mean      0.876500     2.418500     0.044500     0.374250     0.335250  ...   \n",
+       "std       3.092028     3.037213     0.206229     0.483989     0.472137  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.000000     0.000000  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.000000     0.000000  ...   \n",
+       "75%       1.000000     6.000000     0.000000     1.000000     1.000000  ...   \n",
+       "max     125.000000     8.000000     1.000000     1.000000     1.000000  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      0.005750     0.357500     0.002250     0.314500     0.197500   \n",
+       "std       0.084963     0.479324     0.047387     0.464374     0.398163   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "       col_108      col_109      col_110      col_111      targets  \n",
+       "count   4000.0  4000.000000  4000.000000  4000.000000  4000.000000  \n",
+       "mean       0.0     0.052500     0.028000    45.087750     0.254000  \n",
+       "std        0.0     0.223061     0.340213    61.890857     0.435352  \n",
+       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%        0.0     0.000000     0.000000    19.000000     0.000000  \n",
+       "75%        0.0     0.000000     0.000000    62.000000     1.000000  \n",
+       "max        0.0     1.000000    10.000000   581.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9020cf",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "488ed7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_LDA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f59a837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7771661 | Accuracy: 0.7435714 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.5792928 | Accuracy: 0.8142857 \n",
+      "Iter:     3 | Cost: 0.5811377 | Accuracy: 0.8125000 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.5696112 | Accuracy: 0.8292857 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.4818806 | Accuracy: 0.8442857 \n",
+      "New best\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_LDA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384518e",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "572a4735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, X_test, Y_test = data.get_preprocessed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b26df72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_LDA = model_LDA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce01db5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
+    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
+    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
+    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ea9f5b",
+   "metadata": {},
+   "source": [
+    "# Use PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e6c02d",
+   "metadata": {},
+   "source": [
+    "Preprocess PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f2da505",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.perform_PCA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d722a49c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df4d46d",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df69026d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PCA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "401edaee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_PCA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb757835",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "700192cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_PCA = model_PCA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a3f0a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(metrics.classification_report(predictions_PCA,Y_test))\n",
+    "print(metrics.precision_score(predictions_PCA,Y_test))\n",
+    "print(metrics.recall_score(predictions_PCA,Y_test))\n",
+    "print(metrics.f1_score(predictions_PCA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_PCA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_PCA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58662136",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QML4FRAUD/Factory/data.py b/QML4FRAUD/Factory/data.py
index aaf798d..7039a1c 100644
--- a/QML4FRAUD/Factory/data.py
+++ b/QML4FRAUD/Factory/data.py
@@ -44,6 +44,8 @@ def __init__(self, data, target, sample_size = 0, test_split = 0.3, seed = 10):
             if values_dont_change[0][i]:
                 index_to_drop = i
                 self.x_train = self.x_train.drop(self.x_train.columns[index_to_drop], axis = 1)
+                
+        self.x_test = self.x_test[self.x_train.columns]
         
     def view_info(self):
         print(self.x_train.info())
@@ -118,7 +120,7 @@ def perform_LDA(self, n_dim = 2):
         LDA_transformations = []
         
         for i in range(n_dim):
-            lda = LDA(n_components= (n_dim - 1))
+            lda = LDA(n_components= 1)
             features_lda_train_new = lda.fit_transform(features_train[i], self.y_train)
             features_lda_train.append(pd.DataFrame(features_lda_train_new))
             LDA_transformations.append(lda)
diff --git a/QML4FRAUD/Solution via architecture.ipynb b/QML4FRAUD/Solution via architecture.ipynb
index a0d549e..10d2735 100644
--- a/QML4FRAUD/Solution via architecture.ipynb	
+++ b/QML4FRAUD/Solution via architecture.ipynb	
@@ -31,6 +31,14 @@
     "# Prepare Data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "012381cc",
+   "metadata": {},
+   "source": [
+    "Load data in"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -46,32 +54,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b9722cb4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = PrepareData(data = df,target = \"targets\", sample_size = 2000, test_split = 0.3, seed = 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a48959d5",
+   "id": "0ba1e89b",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 17991 to 19934\n",
-      "Columns: 113 entries, col_0 to targets\n",
-      "dtypes: float64(113)\n",
-      "memory usage: 1.7 MB\n",
-      "None\n",
-      "No preprocessing done yet.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -118,322 +103,340 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
+       "      <th>0</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1354.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.126500</td>\n",
-       "      <td>287.945500</td>\n",
-       "      <td>0.325500</td>\n",
-       "      <td>2.10300</td>\n",
-       "      <td>0.067000</td>\n",
-       "      <td>0.930500</td>\n",
-       "      <td>2.516500</td>\n",
-       "      <td>3.126500</td>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>239.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.004000</td>\n",
-       "      <td>0.370000</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>0.314500</td>\n",
-       "      <td>0.190000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.050500</td>\n",
-       "      <td>0.018500</td>\n",
-       "      <td>43.798000</td>\n",
-       "      <td>0.248500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>11.301963</td>\n",
-       "      <td>605.090262</td>\n",
-       "      <td>2.742318</td>\n",
-       "      <td>7.29685</td>\n",
-       "      <td>0.410604</td>\n",
-       "      <td>3.780584</td>\n",
-       "      <td>3.052767</td>\n",
-       "      <td>11.301963</td>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.063135</td>\n",
-       "      <td>0.482925</td>\n",
-       "      <td>0.031615</td>\n",
-       "      <td>0.464433</td>\n",
-       "      <td>0.392399</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.219029</td>\n",
-       "      <td>0.241219</td>\n",
-       "      <td>56.534726</td>\n",
-       "      <td>0.432251</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>3</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>682.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>17.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>37.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>101.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20463</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>20.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>280.250000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
+       "      <th>20464</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>62.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>230.000000</td>\n",
-       "      <td>9319.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>183.00000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>230.000000</td>\n",
+       "      <th>20465</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>393.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>543.000000</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20466</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20467</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>399.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8 rows × 113 columns</p>\n",
+       "<p>20468 rows × 113 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "             col_0        col_1        col_2       col_3        col_4  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
-       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
-       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
-       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
-       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
-       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
-       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
-       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "       col_0   col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9  \\\n",
+       "0        9.0  1354.0    0.0   18.0    0.0    1.0    7.0    9.0    0.0    0.0   \n",
+       "1        0.0   239.0    0.0    1.0    0.0    1.0    0.0    0.0    0.0    0.0   \n",
+       "2        0.0   260.0    0.0    4.0    0.0    3.0    6.0    0.0    0.0    0.0   \n",
+       "3       17.0   682.0    0.0    1.0    0.0    0.0    8.0   17.0    0.0    0.0   \n",
+       "4        1.0   540.0    0.0    2.0    0.0    1.0    7.0    1.0    0.0    0.0   \n",
+       "...      ...     ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
+       "20463    0.0    88.0    0.0    0.0    0.0    2.0   -1.0    0.0    0.0    0.0   \n",
+       "20464    0.0   134.0    0.0    2.0    0.0    0.0    6.0    0.0    0.0    0.0   \n",
+       "20465    4.0   393.0    1.0    1.0    0.0    0.0   -1.0    4.0    0.0    0.0   \n",
+       "20466    0.0    10.0    0.0    1.0    0.0    0.0   -1.0    0.0    0.0    0.0   \n",
+       "20467    4.0   399.0    0.0    3.0    0.0    1.0    7.0    4.0    0.0    0.0   \n",
        "\n",
-       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
-       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
-       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
-       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
-       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
-       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
-       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
-       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "       ...  col_103  col_104  col_105  col_106  col_107  col_108  col_109  \\\n",
+       "0      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "1      ...      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "2      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "3      ...      0.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "4      ...      0.0      0.0      0.0      1.0      1.0      0.0      0.0   \n",
+       "...    ...      ...      ...      ...      ...      ...      ...      ...   \n",
+       "20463  ...      0.0      1.0      0.0      0.0      0.0      0.0      1.0   \n",
+       "20464  ...      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20465  ...      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20466  ...      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "20467  ...      0.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
        "\n",
-       "           col_103      col_104      col_105      col_106      col_107  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
-       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
-       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
-       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
-       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "       col_110  col_111  targets  \n",
+       "0          0.0     49.0      1.0  \n",
+       "1          0.0     55.0      1.0  \n",
+       "2          0.0     56.0      1.0  \n",
+       "3          0.0     65.0      1.0  \n",
+       "4          0.0    175.0      1.0  \n",
+       "...        ...      ...      ...  \n",
+       "20463      0.0     85.0      0.0  \n",
+       "20464      0.0      7.0      0.0  \n",
+       "20465      0.0     45.0      0.0  \n",
+       "20466      0.0      5.0      0.0  \n",
+       "20467      0.0     72.0      0.0  \n",
        "\n",
-       "       col_108      col_109      col_110      col_111      targets  \n",
-       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
-       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
-       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
-       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
-       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
-       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
-       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
-       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
-       "\n",
-       "[8 rows x 113 columns]"
+       "[20468 rows x 113 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.view_info()"
+    "df"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f00c69d2",
+   "id": "d5fbafeb",
    "metadata": {},
    "source": [
-    "# Use LDA"
+    "Clean data"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "383f00dc",
-   "metadata": {},
-   "source": [
-    "Preprocess LDA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dd8fb0b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performing LDA...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.perform_LDA()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d64e1dde",
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8ea31c16",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 17991 to 19934\n",
-      "Columns: 113 entries, col_0 to targets\n",
-      "dtypes: float64(113)\n",
-      "memory usage: 1.7 MB\n",
-      "None\n",
-      "Preprocessing done via:  LDA\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -463,8 +466,8 @@
        "      <th>col_5</th>\n",
        "      <th>col_6</th>\n",
        "      <th>col_7</th>\n",
-       "      <th>col_8</th>\n",
-       "      <th>col_9</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
        "      <th>...</th>\n",
        "      <th>col_103</th>\n",
        "      <th>col_104</th>\n",
@@ -481,95 +484,95 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>3.126500</td>\n",
-       "      <td>287.945500</td>\n",
-       "      <td>0.325500</td>\n",
-       "      <td>2.10300</td>\n",
-       "      <td>0.067000</td>\n",
-       "      <td>0.930500</td>\n",
-       "      <td>2.516500</td>\n",
-       "      <td>3.126500</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>294.793043</td>\n",
+       "      <td>0.420021</td>\n",
+       "      <td>2.329343</td>\n",
+       "      <td>0.083594</td>\n",
+       "      <td>0.939857</td>\n",
+       "      <td>2.386066</td>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>0.038646</td>\n",
+       "      <td>0.363152</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.004000</td>\n",
-       "      <td>0.370000</td>\n",
-       "      <td>0.001000</td>\n",
-       "      <td>0.314500</td>\n",
-       "      <td>0.190000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.050500</td>\n",
-       "      <td>0.018500</td>\n",
-       "      <td>43.798000</td>\n",
-       "      <td>0.248500</td>\n",
+       "      <td>0.004935</td>\n",
+       "      <td>0.358120</td>\n",
+       "      <td>0.002443</td>\n",
+       "      <td>0.308384</td>\n",
+       "      <td>0.190737</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.049345</td>\n",
+       "      <td>0.024282</td>\n",
+       "      <td>43.486125</td>\n",
+       "      <td>0.265683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>11.301963</td>\n",
-       "      <td>605.090262</td>\n",
-       "      <td>2.742318</td>\n",
-       "      <td>7.29685</td>\n",
-       "      <td>0.410604</td>\n",
-       "      <td>3.780584</td>\n",
-       "      <td>3.052767</td>\n",
-       "      <td>11.301963</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>717.541984</td>\n",
+       "      <td>7.367275</td>\n",
+       "      <td>10.068512</td>\n",
+       "      <td>0.840537</td>\n",
+       "      <td>4.222896</td>\n",
+       "      <td>3.018140</td>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>0.192754</td>\n",
+       "      <td>0.480920</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.063135</td>\n",
-       "      <td>0.482925</td>\n",
-       "      <td>0.031615</td>\n",
-       "      <td>0.464433</td>\n",
-       "      <td>0.392399</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.219029</td>\n",
-       "      <td>0.241219</td>\n",
-       "      <td>56.534726</td>\n",
-       "      <td>0.432251</td>\n",
+       "      <td>0.073478</td>\n",
+       "      <td>0.479459</td>\n",
+       "      <td>0.049366</td>\n",
+       "      <td>0.461837</td>\n",
+       "      <td>0.392892</td>\n",
+       "      <td>0.006990</td>\n",
+       "      <td>0.216593</td>\n",
+       "      <td>0.305079</td>\n",
+       "      <td>59.217560</td>\n",
+       "      <td>0.441707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -578,22 +581,22 @@
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>37.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>5.000000</td>\n",
@@ -602,318 +605,2024 @@
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>101.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>1.00000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>283.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>37808.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>772.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 98 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              col_0         col_1         col_2         col_3         col_4  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       3.226256    294.793043      0.420021      2.329343      0.083594   \n",
+       "std       20.564308    717.541984      7.367275     10.068512      0.840537   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000     38.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000     97.000000      0.000000      1.000000      0.000000   \n",
+       "75%        2.000000    283.000000      0.000000      2.000000      0.000000   \n",
+       "max     2301.000000  37808.000000    904.000000    772.000000     54.000000   \n",
+       "\n",
+       "              col_5         col_6         col_7        col_13        col_14  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.939857      2.386066      3.226256      0.038646      0.363152   \n",
+       "std        4.222896      3.018140     20.564308      0.192754      0.480920   \n",
+       "min        0.000000     -1.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000      2.000000      0.000000      0.000000      0.000000   \n",
+       "75%        1.000000      6.000000      2.000000      0.000000      1.000000   \n",
+       "max      230.000000     11.000000   2301.000000      1.000000      1.000000   \n",
+       "\n",
+       "       ...       col_103       col_104       col_105       col_106  \\\n",
+       "count  ...  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean   ...      0.004935      0.358120      0.002443      0.308384   \n",
+       "std    ...      0.073478      0.479459      0.049366      0.461837   \n",
+       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%    ...      0.000000      1.000000      0.000000      1.000000   \n",
+       "max    ...      2.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "            col_107       col_108       col_109       col_110       col_111  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.190737      0.000049      0.049345      0.024282     43.486125   \n",
+       "std        0.392892      0.006990      0.216593      0.305079     59.217560   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      5.000000   \n",
+       "50%        0.000000      0.000000      0.000000      0.000000     19.000000   \n",
+       "75%        0.000000      0.000000      0.000000      0.000000     61.250000   \n",
+       "max        1.000000      1.000000      1.000000     17.000000    747.000000   \n",
+       "\n",
+       "            targets  \n",
+       "count  20468.000000  \n",
+       "mean       0.265683  \n",
+       "std        0.441707  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        1.000000  \n",
+       "max        1.000000  \n",
+       "\n",
+       "[8 rows x 98 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean = df.loc[:, df.any()]\n",
+    "df_clean.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bbf654a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_clean = df_clean.drop('col_7', axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3382d431",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_clean = df_clean.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cdb80e54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    20468.000000\n",
+       "mean         0.999951\n",
+       "std          0.006990\n",
+       "min          0.000000\n",
+       "25%          1.000000\n",
+       "50%          1.000000\n",
+       "75%          1.000000\n",
+       "max          1.000000\n",
+       "Name: col_28, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean[\"col_28\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f58654b",
+   "metadata": {},
+   "source": [
+    "Prepare data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b9722cb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = PrepareData(data = df_clean,target = \"targets\", sample_size = 4000, test_split = 0.3, seed = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a48959d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2800 entries, 738 to 14645\n",
+      "Data columns (total 91 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   col_0    2800 non-null   float64\n",
+      " 1   col_1    2800 non-null   float64\n",
+      " 2   col_2    2800 non-null   float64\n",
+      " 3   col_3    2800 non-null   float64\n",
+      " 4   col_4    2800 non-null   float64\n",
+      " 5   col_5    2800 non-null   float64\n",
+      " 6   col_6    2800 non-null   float64\n",
+      " 7   col_13   2800 non-null   float64\n",
+      " 8   col_14   2800 non-null   float64\n",
+      " 9   col_15   2800 non-null   float64\n",
+      " 10  col_16   2800 non-null   float64\n",
+      " 11  col_17   2800 non-null   float64\n",
+      " 12  col_22   2800 non-null   float64\n",
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+      " 36  col_48   2800 non-null   float64\n",
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+      " 44  col_60   2800 non-null   float64\n",
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+      " 48  col_64   2800 non-null   float64\n",
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+      " 50  col_66   2800 non-null   float64\n",
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+      " 52  col_68   2800 non-null   float64\n",
+      " 53  col_69   2800 non-null   float64\n",
+      " 54  col_72   2800 non-null   float64\n",
+      " 55  col_73   2800 non-null   float64\n",
+      " 56  col_74   2800 non-null   float64\n",
+      " 57  col_75   2800 non-null   float64\n",
+      " 58  col_76   2800 non-null   float64\n",
+      " 59  col_77   2800 non-null   float64\n",
+      " 60  col_79   2800 non-null   float64\n",
+      " 61  col_80   2800 non-null   float64\n",
+      " 62  col_81   2800 non-null   float64\n",
+      " 63  col_82   2800 non-null   float64\n",
+      " 64  col_83   2800 non-null   float64\n",
+      " 65  col_84   2800 non-null   float64\n",
+      " 66  col_85   2800 non-null   float64\n",
+      " 67  col_86   2800 non-null   float64\n",
+      " 68  col_87   2800 non-null   float64\n",
+      " 69  col_88   2800 non-null   float64\n",
+      " 70  col_89   2800 non-null   float64\n",
+      " 71  col_90   2800 non-null   float64\n",
+      " 72  col_92   2800 non-null   float64\n",
+      " 73  col_93   2800 non-null   float64\n",
+      " 74  col_94   2800 non-null   float64\n",
+      " 75  col_95   2800 non-null   float64\n",
+      " 76  col_96   2800 non-null   float64\n",
+      " 77  col_97   2800 non-null   float64\n",
+      " 78  col_98   2800 non-null   float64\n",
+      " 79  col_99   2800 non-null   float64\n",
+      " 80  col_100  2800 non-null   float64\n",
+      " 81  col_101  2800 non-null   float64\n",
+      " 82  col_102  2800 non-null   float64\n",
+      " 83  col_103  2800 non-null   float64\n",
+      " 84  col_104  2800 non-null   float64\n",
+      " 85  col_105  2800 non-null   float64\n",
+      " 86  col_106  2800 non-null   float64\n",
+      " 87  col_107  2800 non-null   float64\n",
+      " 88  col_109  2800 non-null   float64\n",
+      " 89  col_110  2800 non-null   float64\n",
+      " 90  col_111  2800 non-null   float64\n",
+      "dtypes: float64(91)\n",
+      "memory usage: 2.0 MB\n",
+      "None\n",
+      "No preprocessing done yet.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.016000</td>\n",
+       "      <td>276.914500</td>\n",
+       "      <td>0.381750</td>\n",
+       "      <td>2.232000</td>\n",
+       "      <td>0.073750</td>\n",
+       "      <td>0.979750</td>\n",
+       "      <td>2.397500</td>\n",
+       "      <td>0.037000</td>\n",
+       "      <td>0.364000</td>\n",
+       "      <td>0.323000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004250</td>\n",
+       "      <td>0.355000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.303750</td>\n",
+       "      <td>0.189250</td>\n",
+       "      <td>0.000250</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>0.030500</td>\n",
+       "      <td>43.570250</td>\n",
+       "      <td>0.269750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12.684497</td>\n",
+       "      <td>606.945801</td>\n",
+       "      <td>3.353107</td>\n",
+       "      <td>7.274435</td>\n",
+       "      <td>0.485664</td>\n",
+       "      <td>4.250807</td>\n",
+       "      <td>3.022297</td>\n",
+       "      <td>0.188785</td>\n",
+       "      <td>0.481209</td>\n",
+       "      <td>0.467681</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.065061</td>\n",
+       "      <td>0.478573</td>\n",
+       "      <td>0.031611</td>\n",
+       "      <td>0.459933</td>\n",
+       "      <td>0.391756</td>\n",
+       "      <td>0.015811</td>\n",
+       "      <td>0.221043</td>\n",
+       "      <td>0.390651</td>\n",
+       "      <td>60.558277</td>\n",
+       "      <td>0.443885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>272.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>342.000000</td>\n",
+       "      <td>12980.000000</td>\n",
+       "      <td>162.000000</td>\n",
+       "      <td>206.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  4000.000000   4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      3.016000    276.914500     0.381750     2.232000     0.073750   \n",
+       "std      12.684497    606.945801     3.353107     7.274435     0.485664   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     37.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     95.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    272.250000     0.000000     2.000000     0.000000   \n",
+       "max     342.000000  12980.000000   162.000000   206.000000    20.000000   \n",
+       "\n",
+       "             col_5        col_6       col_13       col_14       col_15  ...  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  ...   \n",
+       "mean      0.979750     2.397500     0.037000     0.364000     0.323000  ...   \n",
+       "std       4.250807     3.022297     0.188785     0.481209     0.467681  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.000000     0.000000  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.000000     0.000000  ...   \n",
+       "75%       1.000000     6.000000     0.000000     1.000000     1.000000  ...   \n",
+       "max     143.000000    11.000000     1.000000     1.000000     1.000000  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      0.004250     0.355000     0.001000     0.303750     0.189250   \n",
+       "std       0.065061     0.478573     0.031611     0.459933     0.391756   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "           col_108      col_109      col_110      col_111      targets  \n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  \n",
+       "mean      0.000250     0.051500     0.030500    43.570250     0.269750  \n",
+       "std       0.015811     0.221043     0.390651    60.558277     0.443885  \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%       0.000000     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%       0.000000     0.000000     0.000000    19.000000     0.000000  \n",
+       "75%       0.000000     0.000000     0.000000    62.000000     1.000000  \n",
+       "max       1.000000     1.000000    13.000000   747.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00c69d2",
+   "metadata": {},
+   "source": [
+    "# Use LDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "383f00dc",
+   "metadata": {},
+   "source": [
+    "Preprocess LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ac54ca1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "738      0.0\n",
+       "10760    0.0\n",
+       "12934    9.0\n",
+       "6958     6.0\n",
+       "1255     1.0\n",
+       "        ... \n",
+       "16509    2.0\n",
+       "19140    1.0\n",
+       "18861    0.0\n",
+       "6091     1.0\n",
+       "14645    2.0\n",
+       "Name: col_69, Length: 2800, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.x_train[\"col_69\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dd8fb0b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing LDA...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\Users\\jonas\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.perform_LDA()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d64e1dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 2800 entries, 738 to 14645\n",
+      "Data columns (total 91 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   col_0    2800 non-null   float64\n",
+      " 1   col_1    2800 non-null   float64\n",
+      " 2   col_2    2800 non-null   float64\n",
+      " 3   col_3    2800 non-null   float64\n",
+      " 4   col_4    2800 non-null   float64\n",
+      " 5   col_5    2800 non-null   float64\n",
+      " 6   col_6    2800 non-null   float64\n",
+      " 7   col_13   2800 non-null   float64\n",
+      " 8   col_14   2800 non-null   float64\n",
+      " 9   col_15   2800 non-null   float64\n",
+      " 10  col_16   2800 non-null   float64\n",
+      " 11  col_17   2800 non-null   float64\n",
+      " 12  col_22   2800 non-null   float64\n",
+      " 13  col_23   2800 non-null   float64\n",
+      " 14  col_24   2800 non-null   float64\n",
+      " 15  col_25   2800 non-null   float64\n",
+      " 16  col_26   2800 non-null   float64\n",
+      " 17  col_27   2800 non-null   float64\n",
+      " 18  col_29   2800 non-null   float64\n",
+      " 19  col_30   2800 non-null   float64\n",
+      " 20  col_31   2800 non-null   float64\n",
+      " 21  col_32   2800 non-null   float64\n",
+      " 22  col_33   2800 non-null   float64\n",
+      " 23  col_34   2800 non-null   float64\n",
+      " 24  col_36   2800 non-null   float64\n",
+      " 25  col_37   2800 non-null   float64\n",
+      " 26  col_38   2800 non-null   float64\n",
+      " 27  col_39   2800 non-null   float64\n",
+      " 28  col_40   2800 non-null   float64\n",
+      " 29  col_41   2800 non-null   float64\n",
+      " 30  col_42   2800 non-null   float64\n",
+      " 31  col_43   2800 non-null   float64\n",
+      " 32  col_44   2800 non-null   float64\n",
+      " 33  col_45   2800 non-null   float64\n",
+      " 34  col_46   2800 non-null   float64\n",
+      " 35  col_47   2800 non-null   float64\n",
+      " 36  col_48   2800 non-null   float64\n",
+      " 37  col_49   2800 non-null   float64\n",
+      " 38  col_50   2800 non-null   float64\n",
+      " 39  col_54   2800 non-null   float64\n",
+      " 40  col_55   2800 non-null   float64\n",
+      " 41  col_56   2800 non-null   float64\n",
+      " 42  col_57   2800 non-null   float64\n",
+      " 43  col_59   2800 non-null   float64\n",
+      " 44  col_60   2800 non-null   float64\n",
+      " 45  col_61   2800 non-null   float64\n",
+      " 46  col_62   2800 non-null   float64\n",
+      " 47  col_63   2800 non-null   float64\n",
+      " 48  col_64   2800 non-null   float64\n",
+      " 49  col_65   2800 non-null   float64\n",
+      " 50  col_66   2800 non-null   float64\n",
+      " 51  col_67   2800 non-null   float64\n",
+      " 52  col_68   2800 non-null   float64\n",
+      " 53  col_69   2800 non-null   float64\n",
+      " 54  col_72   2800 non-null   float64\n",
+      " 55  col_73   2800 non-null   float64\n",
+      " 56  col_74   2800 non-null   float64\n",
+      " 57  col_75   2800 non-null   float64\n",
+      " 58  col_76   2800 non-null   float64\n",
+      " 59  col_77   2800 non-null   float64\n",
+      " 60  col_79   2800 non-null   float64\n",
+      " 61  col_80   2800 non-null   float64\n",
+      " 62  col_81   2800 non-null   float64\n",
+      " 63  col_82   2800 non-null   float64\n",
+      " 64  col_83   2800 non-null   float64\n",
+      " 65  col_84   2800 non-null   float64\n",
+      " 66  col_85   2800 non-null   float64\n",
+      " 67  col_86   2800 non-null   float64\n",
+      " 68  col_87   2800 non-null   float64\n",
+      " 69  col_88   2800 non-null   float64\n",
+      " 70  col_89   2800 non-null   float64\n",
+      " 71  col_90   2800 non-null   float64\n",
+      " 72  col_92   2800 non-null   float64\n",
+      " 73  col_93   2800 non-null   float64\n",
+      " 74  col_94   2800 non-null   float64\n",
+      " 75  col_95   2800 non-null   float64\n",
+      " 76  col_96   2800 non-null   float64\n",
+      " 77  col_97   2800 non-null   float64\n",
+      " 78  col_98   2800 non-null   float64\n",
+      " 79  col_99   2800 non-null   float64\n",
+      " 80  col_100  2800 non-null   float64\n",
+      " 81  col_101  2800 non-null   float64\n",
+      " 82  col_102  2800 non-null   float64\n",
+      " 83  col_103  2800 non-null   float64\n",
+      " 84  col_104  2800 non-null   float64\n",
+      " 85  col_105  2800 non-null   float64\n",
+      " 86  col_106  2800 non-null   float64\n",
+      " 87  col_107  2800 non-null   float64\n",
+      " 88  col_109  2800 non-null   float64\n",
+      " 89  col_110  2800 non-null   float64\n",
+      " 90  col_111  2800 non-null   float64\n",
+      "dtypes: float64(91)\n",
+      "memory usage: 2.0 MB\n",
+      "None\n",
+      "Preprocessing done via:  LDA\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.016000</td>\n",
+       "      <td>276.914500</td>\n",
+       "      <td>0.381750</td>\n",
+       "      <td>2.232000</td>\n",
+       "      <td>0.073750</td>\n",
+       "      <td>0.979750</td>\n",
+       "      <td>2.397500</td>\n",
+       "      <td>0.037000</td>\n",
+       "      <td>0.364000</td>\n",
+       "      <td>0.323000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004250</td>\n",
+       "      <td>0.355000</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.303750</td>\n",
+       "      <td>0.189250</td>\n",
+       "      <td>0.000250</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>0.030500</td>\n",
+       "      <td>43.570250</td>\n",
+       "      <td>0.269750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12.684497</td>\n",
+       "      <td>606.945801</td>\n",
+       "      <td>3.353107</td>\n",
+       "      <td>7.274435</td>\n",
+       "      <td>0.485664</td>\n",
+       "      <td>4.250807</td>\n",
+       "      <td>3.022297</td>\n",
+       "      <td>0.188785</td>\n",
+       "      <td>0.481209</td>\n",
+       "      <td>0.467681</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.065061</td>\n",
+       "      <td>0.478573</td>\n",
+       "      <td>0.031611</td>\n",
+       "      <td>0.459933</td>\n",
+       "      <td>0.391756</td>\n",
+       "      <td>0.015811</td>\n",
+       "      <td>0.221043</td>\n",
+       "      <td>0.390651</td>\n",
+       "      <td>60.558277</td>\n",
+       "      <td>0.443885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>272.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>342.000000</td>\n",
+       "      <td>12980.000000</td>\n",
+       "      <td>162.000000</td>\n",
+       "      <td>206.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  4000.000000   4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      3.016000    276.914500     0.381750     2.232000     0.073750   \n",
+       "std      12.684497    606.945801     3.353107     7.274435     0.485664   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     37.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     95.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    272.250000     0.000000     2.000000     0.000000   \n",
+       "max     342.000000  12980.000000   162.000000   206.000000    20.000000   \n",
+       "\n",
+       "             col_5        col_6       col_13       col_14       col_15  ...  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  ...   \n",
+       "mean      0.979750     2.397500     0.037000     0.364000     0.323000  ...   \n",
+       "std       4.250807     3.022297     0.188785     0.481209     0.467681  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.000000     0.000000  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.000000     0.000000  ...   \n",
+       "75%       1.000000     6.000000     0.000000     1.000000     1.000000  ...   \n",
+       "max     143.000000    11.000000     1.000000     1.000000     1.000000  ...   \n",
+       "\n",
+       "           col_103      col_104      col_105      col_106      col_107  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      0.004250     0.355000     0.001000     0.303750     0.189250   \n",
+       "std       0.065061     0.478573     0.031611     0.459933     0.391756   \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
+       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
+       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "\n",
+       "           col_108      col_109      col_110      col_111      targets  \n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  \n",
+       "mean      0.000250     0.051500     0.030500    43.570250     0.269750  \n",
+       "std       0.015811     0.221043     0.390651    60.558277     0.443885  \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%       0.000000     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%       0.000000     0.000000     0.000000    19.000000     0.000000  \n",
+       "75%       0.000000     0.000000     0.000000    62.000000     1.000000  \n",
+       "max       1.000000     1.000000    13.000000   747.000000     1.000000  \n",
+       "\n",
+       "[8 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.view_info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9020cf",
+   "metadata": {},
+   "source": [
+    "Define and train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "488ed7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_LDA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6f59a837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.7942621 | Accuracy: 0.7342857 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.6010749 | Accuracy: 0.8164286 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.5796202 | Accuracy: 0.8192857 \n",
+      "Iter:     4 | Cost: 0.5408751 | Accuracy: 0.8160714 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.5182431 | Accuracy: 0.8289286 \n",
+      "New best\n",
+      "Iter:     6 | Cost: 0.4991535 | Accuracy: 0.8467857 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.5013836 | Accuracy: 0.8521429 \n",
+      "New best\n",
+      "Iter:     8 | Cost: 0.5088154 | Accuracy: 0.8532143 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.4736870 | Accuracy: 0.8539286 \n",
+      "Iter:    10 | Cost: 0.4708895 | Accuracy: 0.8500000 \n",
+      "Iter:    11 | Cost: 0.4977035 | Accuracy: 0.8275000 \n",
+      "Iter:    12 | Cost: 0.5057134 | Accuracy: 0.8217857 \n",
+      "Iter:    13 | Cost: 0.4852301 | Accuracy: 0.8296429 \n",
+      "Iter:    14 | Cost: 0.4718846 | Accuracy: 0.8396429 \n",
+      "Iter:    15 | Cost: 0.4545640 | Accuracy: 0.8532143 \n",
+      "Iter:    16 | Cost: 0.4554596 | Accuracy: 0.8510714 \n",
+      "Iter:    17 | Cost: 0.4633778 | Accuracy: 0.8485714 \n",
+      "Iter:    18 | Cost: 0.4622037 | Accuracy: 0.8489286 \n",
+      "Iter:    19 | Cost: 0.4626170 | Accuracy: 0.8503571 \n",
+      "Iter:    20 | Cost: 0.4525216 | Accuracy: 0.8514286 \n",
+      "Iter:    21 | Cost: 0.4529450 | Accuracy: 0.8535714 \n",
+      "Iter:    22 | Cost: 0.4767429 | Accuracy: 0.8403571 \n",
+      "Iter:    23 | Cost: 0.4764725 | Accuracy: 0.8396429 \n",
+      "Iter:    24 | Cost: 0.4586153 | Accuracy: 0.8507143 \n",
+      "New best\n",
+      "Iter:    25 | Cost: 0.4451016 | Accuracy: 0.8564286 \n",
+      "New best\n",
+      "Iter:    26 | Cost: 0.4335151 | Accuracy: 0.8578571 \n",
+      "Iter:    27 | Cost: 0.4412003 | Accuracy: 0.8564286 \n",
+      "Iter:    28 | Cost: 0.4636693 | Accuracy: 0.8517857 \n",
+      "Iter:    29 | Cost: 0.4920476 | Accuracy: 0.8496429 \n",
+      "Iter:    30 | Cost: 0.5072174 | Accuracy: 0.8489286 \n",
+      "Iter:    31 | Cost: 0.4944467 | Accuracy: 0.8492857 \n",
+      "Iter:    32 | Cost: 0.4692543 | Accuracy: 0.8517857 \n",
+      "Iter:    33 | Cost: 0.4681454 | Accuracy: 0.8542857 \n",
+      "Iter:    34 | Cost: 0.5168040 | Accuracy: 0.8335714 \n",
+      "Iter:    35 | Cost: 0.5326655 | Accuracy: 0.8300000 \n",
+      "Iter:    36 | Cost: 0.5639011 | Accuracy: 0.8192857 \n",
+      "Iter:    37 | Cost: 0.5122344 | Accuracy: 0.8357143 \n",
+      "Iter:    38 | Cost: 0.4867200 | Accuracy: 0.8550000 \n",
+      "Iter:    39 | Cost: 0.5049011 | Accuracy: 0.8496429 \n",
+      "Iter:    40 | Cost: 0.5311656 | Accuracy: 0.8435714 \n",
+      "Iter:    41 | Cost: 0.5289308 | Accuracy: 0.8432143 \n",
+      "Iter:    42 | Cost: 0.5190388 | Accuracy: 0.8442857 \n",
+      "Iter:    43 | Cost: 0.4922646 | Accuracy: 0.8489286 \n",
+      "Iter:    44 | Cost: 0.4749206 | Accuracy: 0.8539286 \n",
+      "Iter:    45 | Cost: 0.4756538 | Accuracy: 0.8525000 \n",
+      "Iter:    46 | Cost: 0.4792483 | Accuracy: 0.8507143 \n",
+      "Iter:    47 | Cost: 0.4881554 | Accuracy: 0.8492857 \n",
+      "Iter:    48 | Cost: 0.4972278 | Accuracy: 0.8489286 \n",
+      "Iter:    49 | Cost: 0.4883159 | Accuracy: 0.8496429 \n",
+      "Iter:    50 | Cost: 0.4743978 | Accuracy: 0.8510714 \n"
+     ]
+    }
+   ],
+   "source": [
+    "model_LDA.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384518e",
+   "metadata": {},
+   "source": [
+    "Get the test set from the data to predict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "572a4735",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, X_test, Y_test = data.get_preprocessed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3b26df72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_LDA = model_LDA.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ce01db5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.97      0.86      0.91       991\n",
+      "         1.0       0.57      0.86      0.68       209\n",
+      "\n",
+      "    accuracy                           0.86      1200\n",
+      "   macro avg       0.77      0.86      0.80      1200\n",
+      "weighted avg       0.90      0.86      0.87      1200\n",
+      "\n",
+      "0.5682539682539682\n",
+      "0.8564593301435407\n",
+      "0.683206106870229\n",
+      "0.8596121070495706\n",
+      "[[855 136]\n",
+      " [ 30 179]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
+    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
+    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
+    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
+    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
+    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ea9f5b",
+   "metadata": {},
+   "source": [
+    "# Use PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e6c02d",
+   "metadata": {},
+   "source": [
+    "Preprocess PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "00669c65",
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2800 rows × 91 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_13  col_14  \\\n",
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+       "\n",
+       "       col_15  ...  col_101  col_102  col_103  col_104  col_105  col_106  \\\n",
+       "738       0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
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+       "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
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+       "\n",
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+       "...        ...      ...      ...      ...  \n",
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+       "\n",
+       "[2800 rows x 91 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "63471930",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
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+       "      <th>col_13</th>\n",
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+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_101</th>\n",
+       "      <th>col_102</th>\n",
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+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4181</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>7592</th>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>618.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18507</th>\n",
+       "      <td>7.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9106</th>\n",
+       "      <td>2.0</td>\n",
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+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9822</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19204</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>3.0</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12449</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
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-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>20.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>280.250000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
+       "      <th>16268</th>\n",
        "      <td>0.0</td>\n",
+       "      <td>209.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>62.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>154.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>230.000000</td>\n",
-       "      <td>9319.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>183.00000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>230.000000</td>\n",
+       "      <th>8975</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>543.000000</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8 rows × 113 columns</p>\n",
+       "<p>1200 rows × 91 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "             col_0        col_1        col_2       col_3        col_4  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
-       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
-       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
-       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
-       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
-       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
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-       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
-       "\n",
-       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
-       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
-       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
-       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
-       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
-       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
-       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
-       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "       col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_13  col_14  \\\n",
+       "4181     0.0   34.0    0.0    0.0    0.0    2.0    0.0     0.0     0.0   \n",
+       "7592     0.0    5.0    0.0    1.0    0.0    0.0   -1.0     0.0     1.0   \n",
+       "33      45.0  618.0    0.0    1.0    0.0   10.0    0.0     0.0     1.0   \n",
+       "18507    7.0  340.0    0.0    2.0    0.0    0.0   -1.0     0.0     0.0   \n",
+       "9106     2.0   36.0    0.0    1.0    0.0    0.0    6.0     0.0     1.0   \n",
+       "...      ...    ...    ...    ...    ...    ...    ...     ...     ...   \n",
+       "9822     2.0  375.0    0.0    2.0    0.0    0.0    6.0     0.0     0.0   \n",
+       "19204    2.0  190.0    0.0    1.0    0.0    3.0    2.0     0.0     1.0   \n",
+       "12449    0.0   23.0    0.0    1.0    0.0    0.0    2.0     0.0     0.0   \n",
+       "16268    0.0  209.0    0.0    8.0    0.0    0.0    0.0     0.0     1.0   \n",
+       "8975     0.0   86.0    1.0    0.0    1.0    0.0    6.0     0.0     0.0   \n",
        "\n",
-       "           col_103      col_104      col_105      col_106      col_107  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
-       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
-       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
-       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
-       "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
-       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
+       "       col_15  ...  col_101  col_102  col_103  col_104  col_105  col_106  \\\n",
+       "4181      0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "7592      1.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "33        0.0  ...      0.0      0.0      0.0      1.0      0.0      0.0   \n",
+       "18507     0.0  ...      0.0      0.0      0.0      0.0      0.0      1.0   \n",
+       "9106      1.0  ...      0.0      1.0      0.0      0.0      0.0      0.0   \n",
+       "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
+       "9822      0.0  ...      0.0      1.0      0.0      0.0      0.0      1.0   \n",
+       "19204     1.0  ...      0.0      1.0      0.0      0.0      0.0      1.0   \n",
+       "12449     0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "16268     0.0  ...      0.0      0.0      0.0      0.0      0.0      1.0   \n",
+       "8975      0.0  ...      0.0      0.0      0.0      0.0      0.0      1.0   \n",
        "\n",
-       "       col_108      col_109      col_110      col_111      targets  \n",
-       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
-       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
-       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
-       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
-       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
-       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
-       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
-       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "       col_107  col_109  col_110  col_111  \n",
+       "4181       0.0      0.0      0.0      0.0  \n",
+       "7592       0.0      0.0      0.0      0.0  \n",
+       "33         0.0      0.0      1.0    181.0  \n",
+       "18507      1.0      0.0      0.0     22.0  \n",
+       "9106       0.0      0.0      0.0      7.0  \n",
+       "...        ...      ...      ...      ...  \n",
+       "9822       0.0      0.0      0.0     20.0  \n",
+       "19204      1.0      0.0      0.0     25.0  \n",
+       "12449      0.0      0.0      0.0      0.0  \n",
+       "16268      1.0      1.0      0.0    154.0  \n",
+       "8975       0.0      0.0      0.0      6.0  \n",
        "\n",
-       "[8 rows x 113 columns]"
+       "[1200 rows x 91 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.view_info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb9020cf",
-   "metadata": {},
-   "source": [
-    "Define and train model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "488ed7a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_LDA = QBC(data, n_dim = 2, n_layers = 5, backend = \"lightning.qubit\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "6f59a837",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New best\n",
-      "Iter:     1 | Cost: 0.7146527 | Accuracy: 0.7557143 \n",
-      "New best\n",
-      "Iter:     2 | Cost: 0.5467249 | Accuracy: 0.8171429 \n",
-      "New best\n",
-      "Iter:     3 | Cost: 0.4751983 | Accuracy: 0.8307143 \n",
-      "New best\n",
-      "Iter:     4 | Cost: 0.4734549 | Accuracy: 0.8385714 \n",
-      "New best\n",
-      "Iter:     5 | Cost: 0.4472145 | Accuracy: 0.8471429 \n",
-      "New best\n",
-      "Iter:     6 | Cost: 0.4475311 | Accuracy: 0.8478571 \n",
-      "Iter:     7 | Cost: 0.6056652 | Accuracy: 0.8064286 \n",
-      "Iter:     8 | Cost: 0.5346172 | Accuracy: 0.8457143 \n",
-      "New best\n",
-      "Iter:     9 | Cost: 0.4346356 | Accuracy: 0.8571429 \n",
-      "Iter:    10 | Cost: 0.4457327 | Accuracy: 0.8528571 \n",
-      "Iter:    11 | Cost: 0.5464202 | Accuracy: 0.8464286 \n",
-      "Iter:    12 | Cost: 0.5721968 | Accuracy: 0.8464286 \n",
-      "Iter:    13 | Cost: 0.5576279 | Accuracy: 0.8442857 \n",
-      "Iter:    14 | Cost: 0.5400980 | Accuracy: 0.8435714 \n",
-      "Iter:    15 | Cost: 0.5297521 | Accuracy: 0.8457143 \n",
-      "Iter:    16 | Cost: 0.5273266 | Accuracy: 0.8471429 \n",
-      "Iter:    17 | Cost: 0.4711755 | Accuracy: 0.8442857 \n",
-      "Iter:    18 | Cost: 0.4586101 | Accuracy: 0.8492857 \n",
-      "Iter:    19 | Cost: 0.4710555 | Accuracy: 0.8471429 \n",
-      "Iter:    20 | Cost: 0.4496019 | Accuracy: 0.8564286 \n",
-      "New best\n",
-      "Iter:    21 | Cost: 0.4334069 | Accuracy: 0.8628571 \n",
-      "New best\n",
-      "Iter:    22 | Cost: 0.4350645 | Accuracy: 0.8635714 \n",
-      "New best\n",
-      "Iter:    23 | Cost: 0.4307490 | Accuracy: 0.8678571 \n",
-      "New best\n",
-      "Iter:    24 | Cost: 0.4142857 | Accuracy: 0.8728571 \n",
-      "New best\n",
-      "Iter:    25 | Cost: 0.4104879 | Accuracy: 0.8757143 \n",
-      "Iter:    26 | Cost: 0.4025811 | Accuracy: 0.8750000 \n",
-      "New best\n",
-      "Iter:    27 | Cost: 0.3971891 | Accuracy: 0.8778571 \n",
-      "Iter:    28 | Cost: 0.3814916 | Accuracy: 0.8757143 \n",
-      "Iter:    29 | Cost: 0.3685935 | Accuracy: 0.8757143 \n",
-      "Iter:    30 | Cost: 0.3622557 | Accuracy: 0.8757143 \n",
-      "Iter:    31 | Cost: 0.3592110 | Accuracy: 0.8742857 \n",
-      "Iter:    32 | Cost: 0.3595640 | Accuracy: 0.8778571 \n",
-      "New best\n",
-      "Iter:    33 | Cost: 0.3677971 | Accuracy: 0.8785714 \n",
-      "Iter:    34 | Cost: 0.4011392 | Accuracy: 0.8771429 \n",
-      "Iter:    35 | Cost: 0.4123842 | Accuracy: 0.8742857 \n",
-      "New best\n",
-      "Iter:    36 | Cost: 0.4398058 | Accuracy: 0.8807143 \n",
-      "Iter:    37 | Cost: 0.4797217 | Accuracy: 0.8700000 \n",
-      "Iter:    38 | Cost: 0.5145149 | Accuracy: 0.8550000 \n",
-      "Iter:    39 | Cost: 0.5033828 | Accuracy: 0.8600000 \n",
-      "Iter:    40 | Cost: 0.4766182 | Accuracy: 0.8792857 \n",
-      "New best\n",
-      "Iter:    41 | Cost: 0.4339206 | Accuracy: 0.8814286 \n",
-      "Iter:    42 | Cost: 0.4180167 | Accuracy: 0.8757143 \n",
-      "Iter:    43 | Cost: 0.3960513 | Accuracy: 0.8721429 \n",
-      "Iter:    44 | Cost: 0.3896418 | Accuracy: 0.8714286 \n",
-      "Iter:    45 | Cost: 0.3829375 | Accuracy: 0.8714286 \n",
-      "Iter:    46 | Cost: 0.3750100 | Accuracy: 0.8728571 \n",
-      "Iter:    47 | Cost: 0.3678940 | Accuracy: 0.8742857 \n",
-      "Iter:    48 | Cost: 0.3640737 | Accuracy: 0.8764286 \n",
-      "Iter:    49 | Cost: 0.3642824 | Accuracy: 0.8785714 \n",
-      "Iter:    50 | Cost: 0.3671699 | Accuracy: 0.8814286 \n"
-     ]
-    }
-   ],
-   "source": [
-    "model_LDA.train()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8384518e",
-   "metadata": {},
-   "source": [
-    "Get the test set from the data to predict:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "572a4735",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_, _, X_test, Y_test = data.get_preprocessed()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3b26df72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions_LDA = model_LDA.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ce01db5f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "        -1.0       0.97      0.89      0.93       497\n",
-      "         1.0       0.63      0.87      0.73       103\n",
-      "\n",
-      "    accuracy                           0.89       600\n",
-      "   macro avg       0.80      0.88      0.83       600\n",
-      "weighted avg       0.91      0.89      0.90       600\n",
-      "\n",
-      "0.6293706293706294\n",
-      "0.8737864077669902\n",
-      "0.7317073170731707\n",
-      "0.8835732843663925\n",
-      "[[444  53]\n",
-      " [ 13  90]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
-    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
-    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
-    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
-    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
-    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ea9f5b",
-   "metadata": {},
-   "source": [
-    "# Use PCA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9e6c02d",
-   "metadata": {},
-   "source": [
-    "Preprocess PCA"
+    "data.x_test"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "9f2da505",
    "metadata": {},
    "outputs": [
@@ -931,7 +2640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "d722a49c",
    "metadata": {},
    "outputs": [
@@ -940,10 +2649,103 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 2000 entries, 17991 to 19934\n",
-      "Columns: 113 entries, col_0 to targets\n",
-      "dtypes: float64(113)\n",
-      "memory usage: 1.7 MB\n",
+      "Int64Index: 2800 entries, 738 to 14645\n",
+      "Data columns (total 91 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   col_0    2800 non-null   float64\n",
+      " 1   col_1    2800 non-null   float64\n",
+      " 2   col_2    2800 non-null   float64\n",
+      " 3   col_3    2800 non-null   float64\n",
+      " 4   col_4    2800 non-null   float64\n",
+      " 5   col_5    2800 non-null   float64\n",
+      " 6   col_6    2800 non-null   float64\n",
+      " 7   col_13   2800 non-null   float64\n",
+      " 8   col_14   2800 non-null   float64\n",
+      " 9   col_15   2800 non-null   float64\n",
+      " 10  col_16   2800 non-null   float64\n",
+      " 11  col_17   2800 non-null   float64\n",
+      " 12  col_22   2800 non-null   float64\n",
+      " 13  col_23   2800 non-null   float64\n",
+      " 14  col_24   2800 non-null   float64\n",
+      " 15  col_25   2800 non-null   float64\n",
+      " 16  col_26   2800 non-null   float64\n",
+      " 17  col_27   2800 non-null   float64\n",
+      " 18  col_29   2800 non-null   float64\n",
+      " 19  col_30   2800 non-null   float64\n",
+      " 20  col_31   2800 non-null   float64\n",
+      " 21  col_32   2800 non-null   float64\n",
+      " 22  col_33   2800 non-null   float64\n",
+      " 23  col_34   2800 non-null   float64\n",
+      " 24  col_36   2800 non-null   float64\n",
+      " 25  col_37   2800 non-null   float64\n",
+      " 26  col_38   2800 non-null   float64\n",
+      " 27  col_39   2800 non-null   float64\n",
+      " 28  col_40   2800 non-null   float64\n",
+      " 29  col_41   2800 non-null   float64\n",
+      " 30  col_42   2800 non-null   float64\n",
+      " 31  col_43   2800 non-null   float64\n",
+      " 32  col_44   2800 non-null   float64\n",
+      " 33  col_45   2800 non-null   float64\n",
+      " 34  col_46   2800 non-null   float64\n",
+      " 35  col_47   2800 non-null   float64\n",
+      " 36  col_48   2800 non-null   float64\n",
+      " 37  col_49   2800 non-null   float64\n",
+      " 38  col_50   2800 non-null   float64\n",
+      " 39  col_54   2800 non-null   float64\n",
+      " 40  col_55   2800 non-null   float64\n",
+      " 41  col_56   2800 non-null   float64\n",
+      " 42  col_57   2800 non-null   float64\n",
+      " 43  col_59   2800 non-null   float64\n",
+      " 44  col_60   2800 non-null   float64\n",
+      " 45  col_61   2800 non-null   float64\n",
+      " 46  col_62   2800 non-null   float64\n",
+      " 47  col_63   2800 non-null   float64\n",
+      " 48  col_64   2800 non-null   float64\n",
+      " 49  col_65   2800 non-null   float64\n",
+      " 50  col_66   2800 non-null   float64\n",
+      " 51  col_67   2800 non-null   float64\n",
+      " 52  col_68   2800 non-null   float64\n",
+      " 53  col_69   2800 non-null   float64\n",
+      " 54  col_72   2800 non-null   float64\n",
+      " 55  col_73   2800 non-null   float64\n",
+      " 56  col_74   2800 non-null   float64\n",
+      " 57  col_75   2800 non-null   float64\n",
+      " 58  col_76   2800 non-null   float64\n",
+      " 59  col_77   2800 non-null   float64\n",
+      " 60  col_79   2800 non-null   float64\n",
+      " 61  col_80   2800 non-null   float64\n",
+      " 62  col_81   2800 non-null   float64\n",
+      " 63  col_82   2800 non-null   float64\n",
+      " 64  col_83   2800 non-null   float64\n",
+      " 65  col_84   2800 non-null   float64\n",
+      " 66  col_85   2800 non-null   float64\n",
+      " 67  col_86   2800 non-null   float64\n",
+      " 68  col_87   2800 non-null   float64\n",
+      " 69  col_88   2800 non-null   float64\n",
+      " 70  col_89   2800 non-null   float64\n",
+      " 71  col_90   2800 non-null   float64\n",
+      " 72  col_92   2800 non-null   float64\n",
+      " 73  col_93   2800 non-null   float64\n",
+      " 74  col_94   2800 non-null   float64\n",
+      " 75  col_95   2800 non-null   float64\n",
+      " 76  col_96   2800 non-null   float64\n",
+      " 77  col_97   2800 non-null   float64\n",
+      " 78  col_98   2800 non-null   float64\n",
+      " 79  col_99   2800 non-null   float64\n",
+      " 80  col_100  2800 non-null   float64\n",
+      " 81  col_101  2800 non-null   float64\n",
+      " 82  col_102  2800 non-null   float64\n",
+      " 83  col_103  2800 non-null   float64\n",
+      " 84  col_104  2800 non-null   float64\n",
+      " 85  col_105  2800 non-null   float64\n",
+      " 86  col_106  2800 non-null   float64\n",
+      " 87  col_107  2800 non-null   float64\n",
+      " 88  col_109  2800 non-null   float64\n",
+      " 89  col_110  2800 non-null   float64\n",
+      " 90  col_111  2800 non-null   float64\n",
+      "dtypes: float64(91)\n",
+      "memory usage: 2.0 MB\n",
       "None\n",
       "Preprocessing done via:  PCA\n"
      ]
@@ -976,9 +2778,9 @@
        "      <th>col_4</th>\n",
        "      <th>col_5</th>\n",
        "      <th>col_6</th>\n",
-       "      <th>col_7</th>\n",
-       "      <th>col_8</th>\n",
-       "      <th>col_9</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
        "      <th>...</th>\n",
        "      <th>col_103</th>\n",
        "      <th>col_104</th>\n",
@@ -995,95 +2797,95 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.00000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
-       "      <td>2000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
+       "      <td>4000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>3.126500</td>\n",
-       "      <td>287.945500</td>\n",
-       "      <td>0.325500</td>\n",
-       "      <td>2.10300</td>\n",
-       "      <td>0.067000</td>\n",
-       "      <td>0.930500</td>\n",
-       "      <td>2.516500</td>\n",
-       "      <td>3.126500</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>3.016000</td>\n",
+       "      <td>276.914500</td>\n",
+       "      <td>0.381750</td>\n",
+       "      <td>2.232000</td>\n",
+       "      <td>0.073750</td>\n",
+       "      <td>0.979750</td>\n",
+       "      <td>2.397500</td>\n",
+       "      <td>0.037000</td>\n",
+       "      <td>0.364000</td>\n",
+       "      <td>0.323000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.004000</td>\n",
-       "      <td>0.370000</td>\n",
+       "      <td>0.004250</td>\n",
+       "      <td>0.355000</td>\n",
        "      <td>0.001000</td>\n",
-       "      <td>0.314500</td>\n",
-       "      <td>0.190000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.050500</td>\n",
-       "      <td>0.018500</td>\n",
-       "      <td>43.798000</td>\n",
-       "      <td>0.248500</td>\n",
+       "      <td>0.303750</td>\n",
+       "      <td>0.189250</td>\n",
+       "      <td>0.000250</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>0.030500</td>\n",
+       "      <td>43.570250</td>\n",
+       "      <td>0.269750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>11.301963</td>\n",
-       "      <td>605.090262</td>\n",
-       "      <td>2.742318</td>\n",
-       "      <td>7.29685</td>\n",
-       "      <td>0.410604</td>\n",
-       "      <td>3.780584</td>\n",
-       "      <td>3.052767</td>\n",
-       "      <td>11.301963</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>12.684497</td>\n",
+       "      <td>606.945801</td>\n",
+       "      <td>3.353107</td>\n",
+       "      <td>7.274435</td>\n",
+       "      <td>0.485664</td>\n",
+       "      <td>4.250807</td>\n",
+       "      <td>3.022297</td>\n",
+       "      <td>0.188785</td>\n",
+       "      <td>0.481209</td>\n",
+       "      <td>0.467681</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.063135</td>\n",
-       "      <td>0.482925</td>\n",
-       "      <td>0.031615</td>\n",
-       "      <td>0.464433</td>\n",
-       "      <td>0.392399</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.219029</td>\n",
-       "      <td>0.241219</td>\n",
-       "      <td>56.534726</td>\n",
-       "      <td>0.432251</td>\n",
+       "      <td>0.065061</td>\n",
+       "      <td>0.478573</td>\n",
+       "      <td>0.031611</td>\n",
+       "      <td>0.459933</td>\n",
+       "      <td>0.391756</td>\n",
+       "      <td>0.015811</td>\n",
+       "      <td>0.221043</td>\n",
+       "      <td>0.390651</td>\n",
+       "      <td>60.558277</td>\n",
+       "      <td>0.443885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -1094,20 +2896,20 @@
        "      <td>0.000000</td>\n",
        "      <td>37.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>5.000000</td>\n",
@@ -1116,125 +2918,125 @@
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.000000</td>\n",
-       "      <td>101.000000</td>\n",
+       "      <td>95.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>2.000000</td>\n",
-       "      <td>280.250000</td>\n",
+       "      <td>272.250000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>2.00000</td>\n",
+       "      <td>2.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>62.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>230.000000</td>\n",
-       "      <td>9319.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>183.00000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>230.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>342.000000</td>\n",
+       "      <td>12980.000000</td>\n",
+       "      <td>162.000000</td>\n",
+       "      <td>206.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>543.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>747.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8 rows × 113 columns</p>\n",
+       "<p>8 rows × 97 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "             col_0        col_1        col_2       col_3        col_4  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.00000  2000.000000   \n",
-       "mean      3.126500   287.945500     0.325500     2.10300     0.067000   \n",
-       "std      11.301963   605.090262     2.742318     7.29685     0.410604   \n",
-       "min       0.000000     0.000000     0.000000     0.00000     0.000000   \n",
-       "25%       0.000000    37.000000     0.000000     0.00000     0.000000   \n",
-       "50%       0.000000   101.000000     0.000000     1.00000     0.000000   \n",
-       "75%       2.000000   280.250000     0.000000     2.00000     0.000000   \n",
-       "max     230.000000  9319.000000   107.000000   183.00000     9.000000   \n",
+       "             col_0         col_1        col_2        col_3        col_4  \\\n",
+       "count  4000.000000   4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      3.016000    276.914500     0.381750     2.232000     0.073750   \n",
+       "std      12.684497    606.945801     3.353107     7.274435     0.485664   \n",
+       "min       0.000000      0.000000     0.000000     0.000000     0.000000   \n",
+       "25%       0.000000     37.000000     0.000000     0.000000     0.000000   \n",
+       "50%       0.000000     95.000000     0.000000     1.000000     0.000000   \n",
+       "75%       2.000000    272.250000     0.000000     2.000000     0.000000   \n",
+       "max     342.000000  12980.000000   162.000000   206.000000    20.000000   \n",
        "\n",
-       "             col_5        col_6        col_7   col_8   col_9  ...  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.0  2000.0  ...   \n",
-       "mean      0.930500     2.516500     3.126500     0.0     0.0  ...   \n",
-       "std       3.780584     3.052767    11.301963     0.0     0.0  ...   \n",
-       "min       0.000000    -1.000000     0.000000     0.0     0.0  ...   \n",
-       "25%       0.000000     0.000000     0.000000     0.0     0.0  ...   \n",
-       "50%       0.000000     2.000000     0.000000     0.0     0.0  ...   \n",
-       "75%       1.000000     6.000000     2.000000     0.0     0.0  ...   \n",
-       "max     107.000000     8.000000   230.000000     0.0     0.0  ...   \n",
+       "             col_5        col_6       col_13       col_14       col_15  ...  \\\n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  ...   \n",
+       "mean      0.979750     2.397500     0.037000     0.364000     0.323000  ...   \n",
+       "std       4.250807     3.022297     0.188785     0.481209     0.467681  ...   \n",
+       "min       0.000000    -1.000000     0.000000     0.000000     0.000000  ...   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
+       "50%       0.000000     2.000000     0.000000     0.000000     0.000000  ...   \n",
+       "75%       1.000000     6.000000     0.000000     1.000000     1.000000  ...   \n",
+       "max     143.000000    11.000000     1.000000     1.000000     1.000000  ...   \n",
        "\n",
        "           col_103      col_104      col_105      col_106      col_107  \\\n",
-       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
-       "mean      0.004000     0.370000     0.001000     0.314500     0.190000   \n",
-       "std       0.063135     0.482925     0.031615     0.464433     0.392399   \n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
+       "mean      0.004250     0.355000     0.001000     0.303750     0.189250   \n",
+       "std       0.065061     0.478573     0.031611     0.459933     0.391756   \n",
        "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
        "75%       0.000000     1.000000     0.000000     1.000000     0.000000   \n",
        "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
        "\n",
-       "       col_108      col_109      col_110      col_111      targets  \n",
-       "count   2000.0  2000.000000  2000.000000  2000.000000  2000.000000  \n",
-       "mean       0.0     0.050500     0.018500    43.798000     0.248500  \n",
-       "std        0.0     0.219029     0.241219    56.534726     0.432251  \n",
-       "min        0.0     0.000000     0.000000     0.000000     0.000000  \n",
-       "25%        0.0     0.000000     0.000000     5.000000     0.000000  \n",
-       "50%        0.0     0.000000     0.000000    20.000000     0.000000  \n",
-       "75%        0.0     0.000000     0.000000    62.000000     0.000000  \n",
-       "max        0.0     1.000000     6.000000   543.000000     1.000000  \n",
+       "           col_108      col_109      col_110      col_111      targets  \n",
+       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000  \n",
+       "mean      0.000250     0.051500     0.030500    43.570250     0.269750  \n",
+       "std       0.015811     0.221043     0.390651    60.558277     0.443885  \n",
+       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
+       "25%       0.000000     0.000000     0.000000     5.000000     0.000000  \n",
+       "50%       0.000000     0.000000     0.000000    19.000000     0.000000  \n",
+       "75%       0.000000     0.000000     0.000000    62.000000     1.000000  \n",
+       "max       1.000000     1.000000    13.000000   747.000000     1.000000  \n",
        "\n",
-       "[8 rows x 113 columns]"
+       "[8 rows x 97 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1253,7 +3055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "df69026d",
    "metadata": {},
    "outputs": [],
@@ -1263,7 +3065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "401edaee",
    "metadata": {},
    "outputs": [
@@ -1272,60 +3074,58 @@
      "output_type": "stream",
      "text": [
       "New best\n",
-      "Iter:     1 | Cost: 0.9922963 | Accuracy: 0.6050000 \n",
+      "Iter:     1 | Cost: 0.9093360 | Accuracy: 0.7242857 \n",
       "New best\n",
-      "Iter:     2 | Cost: 0.9133256 | Accuracy: 0.7357143 \n",
+      "Iter:     2 | Cost: 0.8465034 | Accuracy: 0.7257143 \n",
       "New best\n",
-      "Iter:     3 | Cost: 0.8372795 | Accuracy: 0.7428571 \n",
-      "New best\n",
-      "Iter:     4 | Cost: 0.8082843 | Accuracy: 0.7464286 \n",
-      "Iter:     5 | Cost: 0.8039261 | Accuracy: 0.7464286 \n",
-      "Iter:     6 | Cost: 0.8042474 | Accuracy: 0.7464286 \n",
-      "New best\n",
-      "Iter:     7 | Cost: 0.8297579 | Accuracy: 0.7471429 \n",
-      "Iter:     8 | Cost: 0.8814247 | Accuracy: 0.7471429 \n",
-      "Iter:     9 | Cost: 0.9090855 | Accuracy: 0.7471429 \n",
-      "Iter:    10 | Cost: 0.9006644 | Accuracy: 0.7471429 \n",
-      "Iter:    11 | Cost: 0.8605321 | Accuracy: 0.7471429 \n",
-      "Iter:    12 | Cost: 0.8280091 | Accuracy: 0.7471429 \n",
-      "Iter:    13 | Cost: 0.7825952 | Accuracy: 0.7471429 \n",
-      "Iter:    14 | Cost: 0.7447115 | Accuracy: 0.7471429 \n",
-      "Iter:    15 | Cost: 0.7485581 | Accuracy: 0.7471429 \n",
-      "Iter:    16 | Cost: 0.8402239 | Accuracy: 0.7278571 \n",
-      "Iter:    17 | Cost: 0.8835448 | Accuracy: 0.6978571 \n",
-      "Iter:    18 | Cost: 0.9145426 | Accuracy: 0.3971429 \n",
-      "Iter:    19 | Cost: 0.8670825 | Accuracy: 0.7150000 \n",
-      "Iter:    20 | Cost: 0.7791539 | Accuracy: 0.7428571 \n",
-      "Iter:    21 | Cost: 0.7432145 | Accuracy: 0.7471429 \n",
-      "Iter:    22 | Cost: 0.7460729 | Accuracy: 0.7471429 \n",
-      "Iter:    23 | Cost: 0.7734032 | Accuracy: 0.7471429 \n",
-      "Iter:    24 | Cost: 0.7911368 | Accuracy: 0.7471429 \n",
-      "Iter:    25 | Cost: 0.8098764 | Accuracy: 0.7471429 \n",
-      "Iter:    26 | Cost: 0.8396121 | Accuracy: 0.7471429 \n",
-      "Iter:    27 | Cost: 0.8571642 | Accuracy: 0.7471429 \n",
-      "Iter:    28 | Cost: 0.8267991 | Accuracy: 0.7471429 \n",
-      "Iter:    29 | Cost: 0.7796929 | Accuracy: 0.7464286 \n",
-      "Iter:    30 | Cost: 0.7718953 | Accuracy: 0.7464286 \n",
-      "Iter:    31 | Cost: 0.7747632 | Accuracy: 0.7464286 \n",
-      "Iter:    32 | Cost: 0.7990050 | Accuracy: 0.7464286 \n",
-      "Iter:    33 | Cost: 0.8508553 | Accuracy: 0.7357143 \n",
-      "Iter:    34 | Cost: 0.8222431 | Accuracy: 0.7435714 \n",
-      "Iter:    35 | Cost: 0.7661793 | Accuracy: 0.7471429 \n",
-      "Iter:    36 | Cost: 0.7379706 | Accuracy: 0.7471429 \n",
-      "Iter:    37 | Cost: 0.7647438 | Accuracy: 0.7471429 \n",
-      "Iter:    38 | Cost: 0.8199030 | Accuracy: 0.7471429 \n",
-      "Iter:    39 | Cost: 0.8230046 | Accuracy: 0.7471429 \n",
-      "Iter:    40 | Cost: 0.8318804 | Accuracy: 0.7471429 \n",
-      "Iter:    41 | Cost: 0.8325269 | Accuracy: 0.7471429 \n",
-      "Iter:    42 | Cost: 0.8229071 | Accuracy: 0.7471429 \n",
-      "Iter:    43 | Cost: 0.7878330 | Accuracy: 0.7471429 \n",
-      "Iter:    44 | Cost: 0.7527745 | Accuracy: 0.7471429 \n",
-      "Iter:    45 | Cost: 0.7394627 | Accuracy: 0.7471429 \n",
-      "Iter:    46 | Cost: 0.7763476 | Accuracy: 0.7471429 \n",
-      "Iter:    47 | Cost: 0.8037952 | Accuracy: 0.7464286 \n",
-      "Iter:    48 | Cost: 0.7987263 | Accuracy: 0.7464286 \n",
-      "Iter:    49 | Cost: 0.7850808 | Accuracy: 0.7464286 \n",
-      "Iter:    50 | Cost: 0.7664157 | Accuracy: 0.7471429 \n"
+      "Iter:     3 | Cost: 0.8082671 | Accuracy: 0.7271429 \n",
+      "Iter:     4 | Cost: 0.8092411 | Accuracy: 0.7271429 \n",
+      "Iter:     5 | Cost: 0.7952753 | Accuracy: 0.7271429 \n",
+      "Iter:     6 | Cost: 0.7996801 | Accuracy: 0.7271429 \n",
+      "Iter:     7 | Cost: 0.8025520 | Accuracy: 0.7271429 \n",
+      "Iter:     8 | Cost: 0.8014394 | Accuracy: 0.7271429 \n",
+      "Iter:     9 | Cost: 0.7855185 | Accuracy: 0.7271429 \n",
+      "Iter:    10 | Cost: 0.7721740 | Accuracy: 0.7271429 \n",
+      "Iter:    11 | Cost: 0.8083123 | Accuracy: 0.7253571 \n",
+      "Iter:    12 | Cost: 0.8672649 | Accuracy: 0.7221429 \n",
+      "Iter:    13 | Cost: 0.8521487 | Accuracy: 0.7239286 \n",
+      "Iter:    14 | Cost: 0.8174532 | Accuracy: 0.7253571 \n",
+      "Iter:    15 | Cost: 0.7822240 | Accuracy: 0.7271429 \n",
+      "Iter:    16 | Cost: 0.7806030 | Accuracy: 0.7271429 \n",
+      "Iter:    17 | Cost: 0.7815206 | Accuracy: 0.7271429 \n",
+      "Iter:    18 | Cost: 0.7857112 | Accuracy: 0.7271429 \n",
+      "Iter:    19 | Cost: 0.7925279 | Accuracy: 0.7267857 \n",
+      "Iter:    20 | Cost: 0.8176178 | Accuracy: 0.7253571 \n",
+      "Iter:    21 | Cost: 0.8368659 | Accuracy: 0.7246429 \n",
+      "Iter:    22 | Cost: 0.8376231 | Accuracy: 0.7246429 \n",
+      "Iter:    23 | Cost: 0.8996315 | Accuracy: 0.6689286 \n",
+      "Iter:    24 | Cost: 0.8975679 | Accuracy: 0.6889286 \n",
+      "Iter:    25 | Cost: 0.8714653 | Accuracy: 0.7217857 \n",
+      "Iter:    26 | Cost: 0.8175544 | Accuracy: 0.7253571 \n",
+      "Iter:    27 | Cost: 0.7764102 | Accuracy: 0.7271429 \n",
+      "Iter:    28 | Cost: 0.7929777 | Accuracy: 0.7271429 \n",
+      "Iter:    29 | Cost: 0.8598532 | Accuracy: 0.7271429 \n",
+      "Iter:    30 | Cost: 0.9354658 | Accuracy: 0.7271429 \n",
+      "Iter:    31 | Cost: 0.9793867 | Accuracy: 0.7271429 \n",
+      "Iter:    32 | Cost: 1.0048926 | Accuracy: 0.7271429 \n",
+      "Iter:    33 | Cost: 0.9523856 | Accuracy: 0.7271429 \n",
+      "Iter:    34 | Cost: 0.8198176 | Accuracy: 0.7271429 \n",
+      "Iter:    35 | Cost: 0.7856561 | Accuracy: 0.7271429 \n",
+      "Iter:    36 | Cost: 0.8271008 | Accuracy: 0.7271429 \n",
+      "Iter:    37 | Cost: 0.8524915 | Accuracy: 0.7260714 \n",
+      "Iter:    38 | Cost: 0.8559090 | Accuracy: 0.7253571 \n",
+      "Iter:    39 | Cost: 0.8014215 | Accuracy: 0.7271429 \n",
+      "Iter:    40 | Cost: 0.7845155 | Accuracy: 0.7271429 \n",
+      "Iter:    41 | Cost: 0.7811499 | Accuracy: 0.7271429 \n",
+      "Iter:    42 | Cost: 0.7803873 | Accuracy: 0.7271429 \n",
+      "Iter:    43 | Cost: 0.7905525 | Accuracy: 0.7271429 \n",
+      "Iter:    44 | Cost: 0.8357561 | Accuracy: 0.7253571 \n",
+      "Iter:    45 | Cost: 0.8380950 | Accuracy: 0.7250000 \n",
+      "Iter:    46 | Cost: 0.8090034 | Accuracy: 0.7260714 \n",
+      "Iter:    47 | Cost: 0.7801591 | Accuracy: 0.7271429 \n",
+      "Iter:    48 | Cost: 0.7756364 | Accuracy: 0.7271429 \n",
+      "Iter:    49 | Cost: 0.7860659 | Accuracy: 0.7271429 \n",
+      "Iter:    50 | Cost: 0.7901499 | Accuracy: 0.7271429 \n"
      ]
     }
    ],
@@ -1343,7 +3143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "id": "700192cf",
    "metadata": {},
    "outputs": [],
@@ -1353,7 +3153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "id": "0a3f0a5a",
    "metadata": {},
    "outputs": [
@@ -1363,19 +3163,19 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.91      0.84      0.88       497\n",
-      "         1.0       0.45      0.62      0.52       103\n",
+      "        -1.0       0.86      0.83      0.84       920\n",
+      "         1.0       0.49      0.55      0.52       280\n",
       "\n",
-      "    accuracy                           0.80       600\n",
-      "   macro avg       0.68      0.73      0.70       600\n",
-      "weighted avg       0.83      0.80      0.82       600\n",
+      "    accuracy                           0.76      1200\n",
+      "   macro avg       0.68      0.69      0.68      1200\n",
+      "weighted avg       0.77      0.76      0.77      1200\n",
       "\n",
-      "0.44755244755244755\n",
-      "0.6213592233009708\n",
-      "0.5203252032520325\n",
-      "0.7312027504834834\n",
-      "[[418  79]\n",
-      " [ 39  64]]\n"
+      "0.49206349206349204\n",
+      "0.5535714285714286\n",
+      "0.5210084033613446\n",
+      "0.6898291925465838\n",
+      "[[760 160]\n",
+      " [125 155]]\n"
      ]
     }
    ],

From 9d15b61912c94a5fbb453888dcc9b175ee341442 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 20:30:56 +0800
Subject: [PATCH 12/25] Create Business_application.md

---
 QML4FRAUD/Business_application.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 QML4FRAUD/Business_application.md

diff --git a/QML4FRAUD/Business_application.md b/QML4FRAUD/Business_application.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/QML4FRAUD/Business_application.md
@@ -0,0 +1 @@
+

From bb0a63820a763320b90ff61ba2902b0a3ec4e217 Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Wed, 27 Jul 2022 08:34:19 -0400
Subject: [PATCH 13/25] Updated EDA and 3 qubit notebooks

---
 QML4FRAUD/3 qubit test.ipynb | 2137 ++++++++++++++++++++++++++++++++++
 QML4FRAUD/EDA.ipynb          | 1022 ++++++++--------
 2 files changed, 2652 insertions(+), 507 deletions(-)
 create mode 100644 QML4FRAUD/3 qubit test.ipynb

diff --git a/QML4FRAUD/3 qubit test.ipynb b/QML4FRAUD/3 qubit test.ipynb
new file mode 100644
index 0000000..94d7635
--- /dev/null
+++ b/QML4FRAUD/3 qubit test.ipynb	
@@ -0,0 +1,2137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Exploration and Feature Grouping\n",
+    "\n",
+    "In this notebook we do some quick cleaning of the dataset and perform LDA with features grouped according to correlation.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import libraries needed\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pennylane as qml\n",
+    "from pennylane_qiskit import IBMQDevice\n",
+    "from pennylane_qiskit import BasicAerDevice\n",
+    "from pennylane.templates.embeddings import AngleEmbedding, AmplitudeEmbedding\n",
+    "from pennylane.optimize import AdamOptimizer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "# visualization\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import time\n",
+    "start = time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean-up and Quick Run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "      <td>20468.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.226256</td>\n",
+       "      <td>294.793043</td>\n",
+       "      <td>0.420021</td>\n",
+       "      <td>2.329343</td>\n",
+       "      <td>0.083594</td>\n",
+       "      <td>0.939857</td>\n",
+       "      <td>2.386066</td>\n",
+       "      <td>0.038646</td>\n",
+       "      <td>0.363152</td>\n",
+       "      <td>0.330760</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004935</td>\n",
+       "      <td>0.358120</td>\n",
+       "      <td>0.002443</td>\n",
+       "      <td>0.308384</td>\n",
+       "      <td>0.190737</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.049345</td>\n",
+       "      <td>0.024282</td>\n",
+       "      <td>43.486125</td>\n",
+       "      <td>0.265683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>20.564308</td>\n",
+       "      <td>717.541984</td>\n",
+       "      <td>7.367275</td>\n",
+       "      <td>10.068512</td>\n",
+       "      <td>0.840537</td>\n",
+       "      <td>4.222896</td>\n",
+       "      <td>3.018140</td>\n",
+       "      <td>0.192754</td>\n",
+       "      <td>0.480920</td>\n",
+       "      <td>0.470498</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.073478</td>\n",
+       "      <td>0.479459</td>\n",
+       "      <td>0.049366</td>\n",
+       "      <td>0.461837</td>\n",
+       "      <td>0.392892</td>\n",
+       "      <td>0.006990</td>\n",
+       "      <td>0.216593</td>\n",
+       "      <td>0.305079</td>\n",
+       "      <td>59.217560</td>\n",
+       "      <td>0.441707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>283.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2301.000000</td>\n",
+       "      <td>37808.000000</td>\n",
+       "      <td>904.000000</td>\n",
+       "      <td>772.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>230.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>747.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              col_0         col_1         col_2         col_3         col_4  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       3.226256    294.793043      0.420021      2.329343      0.083594   \n",
+       "std       20.564308    717.541984      7.367275     10.068512      0.840537   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000     38.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000     97.000000      0.000000      1.000000      0.000000   \n",
+       "75%        2.000000    283.000000      0.000000      2.000000      0.000000   \n",
+       "max     2301.000000  37808.000000    904.000000    772.000000     54.000000   \n",
+       "\n",
+       "              col_5         col_6        col_13        col_14        col_15  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.939857      2.386066      0.038646      0.363152      0.330760   \n",
+       "std        4.222896      3.018140      0.192754      0.480920      0.470498   \n",
+       "min        0.000000     -1.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%        0.000000      2.000000      0.000000      0.000000      0.000000   \n",
+       "75%        1.000000      6.000000      0.000000      1.000000      1.000000   \n",
+       "max      230.000000     11.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "       ...       col_103       col_104       col_105       col_106  \\\n",
+       "count  ...  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean   ...      0.004935      0.358120      0.002443      0.308384   \n",
+       "std    ...      0.073478      0.479459      0.049366      0.461837   \n",
+       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%    ...      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%    ...      0.000000      1.000000      0.000000      1.000000   \n",
+       "max    ...      2.000000      1.000000      1.000000      1.000000   \n",
+       "\n",
+       "            col_107       col_108       col_109       col_110       col_111  \\\n",
+       "count  20468.000000  20468.000000  20468.000000  20468.000000  20468.000000   \n",
+       "mean       0.190737      0.000049      0.049345      0.024282     43.486125   \n",
+       "std        0.392892      0.006990      0.216593      0.305079     59.217560   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.000000      0.000000      0.000000      0.000000      5.000000   \n",
+       "50%        0.000000      0.000000      0.000000      0.000000     19.000000   \n",
+       "75%        0.000000      0.000000      0.000000      0.000000     61.250000   \n",
+       "max        1.000000      1.000000      1.000000     17.000000    747.000000   \n",
+       "\n",
+       "            targets  \n",
+       "count  20468.000000  \n",
+       "mean       0.265683  \n",
+       "std        0.441707  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        1.000000  \n",
+       "max        1.000000  \n",
+       "\n",
+       "[8 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read out CSV and sets/samples creation\n",
+    "\n",
+    "df = pd.read_csv('dataset_cleaned.csv', sep=',')\n",
+    "df = df.astype(float)\n",
+    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Grouping\n",
+    "\n",
+    "We want to split the features into groups that are similar within but distinct between groups. Let's take a look at the correlation matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr = df.corr()\n",
+    "heatmap = sns.heatmap(corr, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some columns show a much stronger correlation than others. Let's see if we could group the more strongly correlated ones together. This is probably not optimized but it is a test of idea."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_22</th>\n",
+       "      <th>col_47</th>\n",
+       "      <th>col_66</th>\n",
+       "      <th>col_63</th>\n",
+       "      <th>col_57</th>\n",
+       "      <th>col_50</th>\n",
+       "      <th>col_36</th>\n",
+       "      <th>col_55</th>\n",
+       "      <th>col_40</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_77</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_74</th>\n",
+       "      <th>col_73</th>\n",
+       "      <th>col_84</th>\n",
+       "      <th>col_87</th>\n",
+       "      <th>col_89</th>\n",
+       "      <th>col_86</th>\n",
+       "      <th>col_83</th>\n",
+       "      <th>col_15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 96 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col_0  col_22  col_47  col_66  col_63  col_57  col_50  col_36  col_55  \\\n",
+       "0    9.0     9.0    36.0    32.0    32.0    11.0    15.0    97.0    56.0   \n",
+       "1    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "2    0.0     0.0    13.0     0.0     0.0     0.0     0.0     5.0     0.0   \n",
+       "3   17.0    23.0     0.0     0.0     0.0     0.0     2.0     3.0     0.0   \n",
+       "4    1.0     1.0    21.0    34.0    20.0    36.0    36.0    52.0    39.0   \n",
+       "\n",
+       "   col_40  ...  col_77  col_104  col_74  col_73  col_84  col_87  col_89  \\\n",
+       "0     0.0  ...     0.0      0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "1     0.0  ...     0.0      1.0     0.0     0.0     0.0     0.0     1.0   \n",
+       "2     0.0  ...     0.0      0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "3     0.0  ...     0.0      1.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4     0.0  ...     0.0      0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "\n",
+       "   col_86  col_83  col_15  \n",
+       "0     0.0     0.0     0.0  \n",
+       "1     0.0     0.0     0.0  \n",
+       "2     0.0     0.0     1.0  \n",
+       "3     0.0     0.0     0.0  \n",
+       "4     0.0     0.0     0.0  \n",
+       "\n",
+       "[5 rows x 96 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered = df[corr['col_0'].sort_values(ascending=False).index].drop('targets', axis = 1)\n",
+    "ordered.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr_ordered = ordered.corr()\n",
+    "heatmap = sns.heatmap(corr_ordered, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr2 = ordered.loc[:,'col_64':'col_98'].corr()\n",
+    "heatmap = sns.heatmap(corr2, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will pick `col_37`/`col_30` as our first splitting point for the two groups. This results in an uneven split (21,75). We perform the LDA and QML on the same sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_group = ordered.loc[:, :'col_37']\n",
+    "Left_over = ordered.loc[:, 'col_30':]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr_left = Left_over.corr()\n",
+    "heatmap = sns.heatmap(corr_left, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordered_left = Left_over[corr_left['col_69'].sort_values(ascending=False).index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,8))\n",
+    "sns.set_theme(style=\"white\")\n",
+    "corr_left_o = ordered_left.corr()\n",
+    "heatmap = sns.heatmap(corr_left_o, annot=False, cmap=\"Blues\", fmt='.1g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Less obvious for these, well simply split the ordered in half."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group1 = ordered.loc[:, :'col_37'].columns\n",
+    "group2 = ordered_left.iloc[:, :ordered_left.shape[1]//2].columns\n",
+    "group3 = ordered_left.iloc[:, ordered_left.shape[1]//2:].columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordred_sample = df_clean.sample(2000)\n",
+    "train,test = train_test_split(ordred_sample, test_size=0.30, random_state=10)\n",
+    "train_set = train\n",
+    "test_set = test\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_0</th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_3</th>\n",
+       "      <th>col_4</th>\n",
+       "      <th>col_5</th>\n",
+       "      <th>col_6</th>\n",
+       "      <th>col_13</th>\n",
+       "      <th>col_14</th>\n",
+       "      <th>col_15</th>\n",
+       "      <th>...</th>\n",
+       "      <th>col_103</th>\n",
+       "      <th>col_104</th>\n",
+       "      <th>col_105</th>\n",
+       "      <th>col_106</th>\n",
+       "      <th>col_107</th>\n",
+       "      <th>col_108</th>\n",
+       "      <th>col_109</th>\n",
+       "      <th>col_110</th>\n",
+       "      <th>col_111</th>\n",
+       "      <th>targets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10074</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11366</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15121</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12527</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9992</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>271.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3432</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10958</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>873.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8639</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13965</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5026</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_13  col_14  \\\n",
+       "10074    6.0  146.0    0.0    3.0    0.0    0.0    0.0     0.0     1.0   \n",
+       "11366    0.0  125.0    0.0    2.0    0.0    0.0    6.0     0.0     1.0   \n",
+       "15121    0.0  163.0    0.0    1.0    0.0    3.0    2.0     0.0     1.0   \n",
+       "12527    4.0  390.0    0.0    2.0    0.0    4.0   -1.0     0.0     0.0   \n",
+       "9992     1.0  271.0    0.0    9.0    0.0    0.0    0.0     0.0     1.0   \n",
+       "3432     0.0  177.0    0.0    0.0    0.0   11.0    0.0     0.0     1.0   \n",
+       "10958    5.0  873.0    0.0    5.0    0.0    0.0    7.0     0.0     1.0   \n",
+       "8639     5.0  302.0    0.0    2.0    0.0    0.0    0.0     0.0     0.0   \n",
+       "13965    0.0    9.0    0.0    0.0    0.0    1.0    2.0     0.0     0.0   \n",
+       "5026     0.0   54.0    0.0    0.0    0.0    1.0    2.0     0.0     0.0   \n",
+       "\n",
+       "       col_15  ...  col_103  col_104  col_105  col_106  col_107  col_108  \\\n",
+       "10074     0.0  ...      0.0      0.0      0.0      1.0      1.0      0.0   \n",
+       "11366     0.0  ...      0.0      1.0      0.0      0.0      0.0      0.0   \n",
+       "15121     0.0  ...      0.0      1.0      0.0      1.0      1.0      0.0   \n",
+       "12527     0.0  ...      0.0      0.0      0.0      1.0      1.0      0.0   \n",
+       "9992      0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "3432      0.0  ...      0.0      1.0      0.0      0.0      0.0      0.0   \n",
+       "10958     0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "8639      0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "13965     0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "5026      1.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
+       "\n",
+       "       col_109  col_110  col_111  targets  \n",
+       "10074      0.0      0.0     37.0      0.0  \n",
+       "11366      0.0      0.0     52.0      0.0  \n",
+       "15121      0.0      0.0     78.0      0.0  \n",
+       "12527      0.0      0.0     21.0      0.0  \n",
+       "9992       0.0      0.0     13.0      0.0  \n",
+       "3432       0.0      0.0     58.0      1.0  \n",
+       "10958      0.0      0.0     16.0      0.0  \n",
+       "8639       0.0      0.0      0.0      0.0  \n",
+       "13965      0.0      0.0      0.0      0.0  \n",
+       "5026       0.0      0.0      0.0      1.0  \n",
+       "\n",
+       "[10 rows x 97 columns]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordred_sample.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separation of labels\n",
+    "\n",
+    "y_train = train_set[['targets']]\n",
+    "x_train = train_set.drop('targets', axis=1)\n",
+    "\n",
+    "\n",
+    "y_test = test_set[['targets']]\n",
+    "x_test = test_set.drop('targets', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((1400, 21), (1400, 37), (1400, 38))"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# grouping features\n",
+    "\n",
+    "features_a = x_train[group1]\n",
+    "features_b = x_train[group2]\n",
+    "features_c = x_train[group3]\n",
+    "\n",
+    "features_a_test = x_test[group1]\n",
+    "features_b_test = x_test[group2]\n",
+    "features_c_test = x_test[group3]\n",
+    "features_a.shape, features_b.shape,features_c.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['col_0', 'col_22', 'col_47', 'col_66', 'col_63', 'col_57', 'col_50',\n",
+       "       'col_36', 'col_55', 'col_40', 'col_54', 'col_23', 'col_64', 'col_49',\n",
+       "       'col_60', 'col_38', 'col_65', 'col_48', 'col_42', 'col_1', 'col_37'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_a.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LDA Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transform the features\n",
+    "\n",
+    "lda = LDA(n_components=1)\n",
+    "features_lda_1 = lda.fit_transform(features_a, y_train.values.ravel())\n",
+    "features_lda_1_test = lda.transform(features_a_test)\n",
+    "\n",
+    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "features_lda_2_test = lda.transform(features_b_test)\n",
+    "\n",
+    "features_lda_3 = lda.fit_transform(features_c, y_train.values.ravel())\n",
+    "features_lda_3_test = lda.transform(features_c_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_lda_1 = pd.DataFrame(features_lda_1)\n",
+    "features_lda_2 = pd.DataFrame(features_lda_2)\n",
+    "features_lda_3 = pd.DataFrame(features_lda_3)\n",
+    "\n",
+    "features_lda_1_test = pd.DataFrame(features_lda_1_test)\n",
+    "features_lda_2_test = pd.DataFrame(features_lda_2_test)\n",
+    "features_lda_3_test = pd.DataFrame(features_lda_3_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(           0\n",
+       " 0   0.401935\n",
+       " 1  10.605835\n",
+       " 2  -1.048846\n",
+       " 3   0.418574\n",
+       " 4   0.069082,\n",
+       "           0\n",
+       " 0 -0.569282\n",
+       " 1 -1.484085\n",
+       " 2 -1.254430\n",
+       " 3 -0.791507\n",
+       " 4 -1.349090,\n",
+       "           0\n",
+       " 0 -1.051580\n",
+       " 1  3.429497\n",
+       " 2 -1.255544\n",
+       " 3  0.420660\n",
+       " 4 -1.392853)"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_lda_1.head(), features_lda_2.head(),features_lda_3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# combine the 2 features\n",
+    "\n",
+    "x_train_lda = pd.concat([features_lda_1,features_lda_2,features_lda_3], axis = 1)\n",
+    "x_train_lda.columns = [\"feature1\", \"feature2\", \"feature3\"]\n",
+    "x_test_lda = pd.concat([features_lda_1_test,features_lda_2_test,features_lda_3_test], axis = 1)\n",
+    "x_test_lda.columns = [\"feature1\", \"feature2\", \"feature3\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature1</th>\n",
+       "      <th>feature2</th>\n",
+       "      <th>feature3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.401935</td>\n",
+       "      <td>-0.569282</td>\n",
+       "      <td>-1.051580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.605835</td>\n",
+       "      <td>-1.484085</td>\n",
+       "      <td>3.429497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.048846</td>\n",
+       "      <td>-1.254430</td>\n",
+       "      <td>-1.255544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.418574</td>\n",
+       "      <td>-0.791507</td>\n",
+       "      <td>0.420660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.069082</td>\n",
+       "      <td>-1.349090</td>\n",
+       "      <td>-1.392853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>0.317657</td>\n",
+       "      <td>-0.216462</td>\n",
+       "      <td>0.460720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>0.356630</td>\n",
+       "      <td>0.873146</td>\n",
+       "      <td>2.704771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1397</th>\n",
+       "      <td>0.053135</td>\n",
+       "      <td>0.277998</td>\n",
+       "      <td>-1.137192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1398</th>\n",
+       "      <td>0.170560</td>\n",
+       "      <td>-1.248066</td>\n",
+       "      <td>0.690121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1399</th>\n",
+       "      <td>0.389344</td>\n",
+       "      <td>-1.428968</td>\n",
+       "      <td>-0.923898</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1400 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feature1  feature2  feature3\n",
+       "0      0.401935 -0.569282 -1.051580\n",
+       "1     10.605835 -1.484085  3.429497\n",
+       "2     -1.048846 -1.254430 -1.255544\n",
+       "3      0.418574 -0.791507  0.420660\n",
+       "4      0.069082 -1.349090 -1.392853\n",
+       "...         ...       ...       ...\n",
+       "1395   0.317657 -0.216462  0.460720\n",
+       "1396   0.356630  0.873146  2.704771\n",
+       "1397   0.053135  0.277998 -1.137192\n",
+       "1398   0.170560 -1.248066  0.690121\n",
+       "1399   0.389344 -1.428968 -0.923898\n",
+       "\n",
+       "[1400 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature1</th>\n",
+       "      <th>feature2</th>\n",
+       "      <th>feature3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.303358</td>\n",
+       "      <td>-1.156117</td>\n",
+       "      <td>-0.768065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.381894</td>\n",
+       "      <td>-0.262228</td>\n",
+       "      <td>0.485719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.380457</td>\n",
+       "      <td>0.729934</td>\n",
+       "      <td>3.630054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.476069</td>\n",
+       "      <td>-0.650674</td>\n",
+       "      <td>0.309824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.123563</td>\n",
+       "      <td>-0.931123</td>\n",
+       "      <td>0.205417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>-2.107703</td>\n",
+       "      <td>-0.542479</td>\n",
+       "      <td>-0.164825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-0.735956</td>\n",
+       "      <td>-0.421222</td>\n",
+       "      <td>-1.722645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>0.337215</td>\n",
+       "      <td>-0.523927</td>\n",
+       "      <td>0.393819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>0.656739</td>\n",
+       "      <td>-3.772050</td>\n",
+       "      <td>0.354136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>-0.297005</td>\n",
+       "      <td>-1.029904</td>\n",
+       "      <td>-1.527515</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     feature1  feature2  feature3\n",
+       "0   -1.303358 -1.156117 -0.768065\n",
+       "1    0.381894 -0.262228  0.485719\n",
+       "2    0.380457  0.729934  3.630054\n",
+       "3    0.476069 -0.650674  0.309824\n",
+       "4    0.123563 -0.931123  0.205417\n",
+       "..        ...       ...       ...\n",
+       "595 -2.107703 -0.542479 -0.164825\n",
+       "596 -0.735956 -0.421222 -1.722645\n",
+       "597  0.337215 -0.523927  0.393819\n",
+       "598  0.656739 -3.772050  0.354136\n",
+       "599 -0.297005 -1.029904 -1.527515\n",
+       "\n",
+       "[600 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_train_lda)\n",
+    "data = std_scale.transform(x_train_lda)\n",
+    "\n",
+    "std_scale = StandardScaler().fit(x_test_lda)\n",
+    "x_test_lda_n = std_scale.transform(x_test_lda)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.05687867, -0.91322353, -0.29833986],\n",
+       "       [ 0.30861562, -0.27105695,  0.2037446 ],\n",
+       "       [ 0.3074508 ,  0.44170903,  1.46291118],\n",
+       "       ...,\n",
+       "       [ 0.27241381, -0.45906056,  0.1669431 ],\n",
+       "       [ 0.53131213, -2.79250071,  0.15105149],\n",
+       "       [-0.2414701 , -0.82255264, -0.60246607]])"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_lda_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_dim = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        73.714286\n",
+       "1.0        26.285714\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in train\n",
+    "\n",
+    "y_train.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "targets\n",
+       "0.0        71.333333\n",
+       "1.0        28.666667\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the balance of the target variable in test\n",
+    "\n",
+    "y_test.value_counts(normalize=True)*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### QML\n",
+    "\n",
+    "We then perform quantum machine learning using the two LDA features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Angle Encoding\n",
+    "\n",
+    "num_qubits = n_dim\n",
+    "\n",
+    "dev = qml.device('default.qubit', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('default.qubit.tf', wires = num_qubits, shots=1024)\n",
+    "#dev = qml.device('qiskit.ibmq', wires = num_qubits, backend='ibmq_manila', ibmqx_token=\"6cc75c58fc80fea56cb8dd391f8fbcfdb676a3dc7005493728bc9da7ea753e31a2110a01e3a0cc83f1a98f5ca79e32956fc66c11b5eea4cae163b3fa996be356\", shots=256)\n",
+    "#dev = qml.device('qiskit.basicaer', wires = num_qubits, shots = 256)\n",
+    "\n",
+    "@qml.qnode(dev)\n",
+    "def circuit(parameters, data):\n",
+    "    for i in range(num_qubits):\n",
+    "        qml.Hadamard(wires = i)\n",
+    "    \n",
+    "    AngleEmbedding(features = data, wires = range(num_qubits), rotation = 'Y')\n",
+    "    \n",
+    "    qml.StronglyEntanglingLayers(weights = parameters, wires = range(num_qubits))\n",
+    "    \n",
+    "    return qml.expval(qml.PauliZ(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 0.00496714 -0.00138264  0.00647689]\n",
+      "  [ 0.0152303  -0.00234153 -0.00234137]\n",
+      "  [ 0.01579213  0.00767435 -0.00469474]]\n",
+      "\n",
+      " [[ 0.0054256  -0.00463418 -0.0046573 ]\n",
+      "  [ 0.00241962 -0.0191328  -0.01724918]\n",
+      "  [-0.00562288 -0.01012831  0.00314247]]\n",
+      "\n",
+      " [[-0.00908024 -0.01412304  0.01465649]\n",
+      "  [-0.00225776  0.00067528 -0.01424748]\n",
+      "  [-0.00544383  0.00110923 -0.01150994]]\n",
+      "\n",
+      " [[ 0.00375698 -0.00600639 -0.00291694]\n",
+      "  [-0.00601707  0.01852278 -0.00013497]\n",
+      "  [-0.01057711  0.00822545 -0.01220844]]\n",
+      "\n",
+      " [[ 0.00208864 -0.0195967  -0.01328186]\n",
+      "  [ 0.00196861  0.00738467  0.00171368]\n",
+      "  [-0.00115648 -0.00301104 -0.01478522]]] 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_layers = 5\n",
+    "weights_init = 0.01 * np.random.randn(num_layers, num_qubits, 3, requires_grad=True)\n",
+    "bias_init = np.array(0.0, requires_grad=True)\n",
+    "\n",
+    "print(weights_init, bias_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(-0.11132812, requires_grad=True)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit(weights_init, data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def variational_classifier(weights, bias, x):\n",
+    "    return circuit(weights, x) + bias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def square_loss(labels, predictions):\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        loss = loss + (l - p) ** 2\n",
+    "\n",
+    "    loss = loss / len(labels)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(labels, predictions):\n",
+    "\n",
+    "    loss = 0\n",
+    "    for l, p in zip(labels, predictions):\n",
+    "        if abs(l - p) < 1e-5:\n",
+    "            loss = loss + 1\n",
+    "    loss = loss / len(labels)\n",
+    "\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost(weights, bias, X, Y):\n",
+    "    predictions = [variational_classifier(weights, bias, x) for x in X]\n",
+    "    return square_loss(Y, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X = [tensor(0.39601828, requires_grad=False), tensor(-0.43432824, requires_grad=False), tensor(-0.96778402, requires_grad=False)], Y = -1\n",
+      "X = [tensor(10.44971129, requires_grad=False), tensor(-1.13226888, requires_grad=False), tensor(3.1562148, requires_grad=False)], Y =  1\n",
+      "X = [tensor(-1.03340618, requires_grad=False), tensor(-0.95705552, requires_grad=False), tensor(-1.15549491, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.41241242, requires_grad=False), tensor(-0.60387295, requires_grad=False), tensor(0.38713954, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.06806546, requires_grad=False), tensor(-1.02927561, requires_grad=False), tensor(-1.2818625, requires_grad=False)], Y = -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "Y = np.array(y_train.values[:,0] * 2 - np.ones(len(y_train.values[:,0])), requires_grad = False)  # shift label from {0, 1} to {-1, 1}\n",
+    "X = np.array(data, requires_grad=False)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    print(\"X = {}, Y = {: d}\".format(list(X[i]), int(Y[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = AdamOptimizer(stepsize=0.1, beta1=0.9, beta2=0.99, eps=1e-08)\n",
+    "batch_size = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New best\n",
+      "Iter:     1 | Cost: 0.8321581 | Accuracy: 0.7392857 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.7032348 | Accuracy: 0.7700000 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.6458986 | Accuracy: 0.7857143 \n",
+      "New best\n",
+      "Iter:     4 | Cost: 0.6408274 | Accuracy: 0.7900000 \n",
+      "New best\n",
+      "Iter:     5 | Cost: 0.6352138 | Accuracy: 0.7964286 \n",
+      "Iter:     6 | Cost: 0.6395144 | Accuracy: 0.7950000 \n",
+      "New best\n",
+      "Iter:     7 | Cost: 0.6218306 | Accuracy: 0.8014286 \n",
+      "New best\n",
+      "Iter:     8 | Cost: 0.6089275 | Accuracy: 0.8071429 \n",
+      "New best\n",
+      "Iter:     9 | Cost: 0.5994851 | Accuracy: 0.8078571 \n",
+      "Iter:    10 | Cost: 0.6291717 | Accuracy: 0.8050000 \n",
+      "Iter:    11 | Cost: 0.6285832 | Accuracy: 0.7914286 \n",
+      "Iter:    12 | Cost: 0.6118298 | Accuracy: 0.7685714 \n",
+      "Iter:    13 | Cost: 0.6146949 | Accuracy: 0.7414286 \n",
+      "Iter:    14 | Cost: 0.6155200 | Accuracy: 0.7507143 \n",
+      "Iter:    15 | Cost: 0.6112061 | Accuracy: 0.7785714 \n",
+      "Iter:    16 | Cost: 0.6052122 | Accuracy: 0.7907143 \n",
+      "Iter:    17 | Cost: 0.5948368 | Accuracy: 0.8064286 \n",
+      "New best\n",
+      "Iter:    18 | Cost: 0.5745249 | Accuracy: 0.8128571 \n",
+      "New best\n",
+      "Iter:    19 | Cost: 0.5594161 | Accuracy: 0.8214286 \n",
+      "Iter:    20 | Cost: 0.5443221 | Accuracy: 0.8185714 \n",
+      "Iter:    21 | Cost: 0.5496765 | Accuracy: 0.8192857 \n",
+      "Iter:    22 | Cost: 0.5729430 | Accuracy: 0.8135714 \n",
+      "Iter:    23 | Cost: 0.6316143 | Accuracy: 0.8114286 \n",
+      "Iter:    24 | Cost: 0.6767216 | Accuracy: 0.8114286 \n",
+      "Iter:    25 | Cost: 0.6374084 | Accuracy: 0.8107143 \n",
+      "Iter:    26 | Cost: 0.5839198 | Accuracy: 0.8157143 \n",
+      "Iter:    27 | Cost: 0.5532858 | Accuracy: 0.8114286 \n",
+      "Iter:    28 | Cost: 0.5660211 | Accuracy: 0.8178571 \n",
+      "Iter:    29 | Cost: 0.6020919 | Accuracy: 0.8200000 \n",
+      "Iter:    30 | Cost: 0.6225557 | Accuracy: 0.8157143 \n",
+      "Iter:    31 | Cost: 0.6040789 | Accuracy: 0.8207143 \n",
+      "New best\n",
+      "Iter:    32 | Cost: 0.5484756 | Accuracy: 0.8292857 \n",
+      "Iter:    33 | Cost: 0.5228564 | Accuracy: 0.8285714 \n",
+      "Iter:    34 | Cost: 0.5313163 | Accuracy: 0.8200000 \n",
+      "Iter:    35 | Cost: 0.5537613 | Accuracy: 0.8200000 \n",
+      "Iter:    36 | Cost: 0.5778290 | Accuracy: 0.8192857 \n",
+      "Iter:    37 | Cost: 0.5478331 | Accuracy: 0.8235714 \n",
+      "Iter:    38 | Cost: 0.5103273 | Accuracy: 0.8278571 \n",
+      "Iter:    39 | Cost: 0.4940754 | Accuracy: 0.8292857 \n",
+      "New best\n",
+      "Iter:    40 | Cost: 0.5085600 | Accuracy: 0.8357143 \n",
+      "Iter:    41 | Cost: 0.5453510 | Accuracy: 0.8314286 \n",
+      "Iter:    42 | Cost: 0.5835839 | Accuracy: 0.8142857 \n",
+      "Iter:    43 | Cost: 0.5855793 | Accuracy: 0.8200000 \n",
+      "Iter:    44 | Cost: 0.5561064 | Accuracy: 0.8342857 \n",
+      "Iter:    45 | Cost: 0.5391562 | Accuracy: 0.8235714 \n",
+      "Iter:    46 | Cost: 0.5527499 | Accuracy: 0.8114286 \n",
+      "Iter:    47 | Cost: 0.5862379 | Accuracy: 0.8100000 \n",
+      "Iter:    48 | Cost: 0.5941040 | Accuracy: 0.8100000 \n",
+      "Iter:    49 | Cost: 0.5623034 | Accuracy: 0.8142857 \n",
+      "Iter:    50 | Cost: 0.5431768 | Accuracy: 0.8221429 \n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = weights_init\n",
+    "bias = bias_init\n",
+    "\n",
+    "wbest = 0\n",
+    "bbest = 0\n",
+    "abest = 0\n",
+    "\n",
+    "for it in range(50):\n",
+    "\n",
+    "    # weights update by one optimizer step\n",
+    "\n",
+    "    batch_index = np.random.randint(0, len(X), (batch_size,))\n",
+    "    X_batch = X[batch_index]\n",
+    "    Y_batch = Y[batch_index]\n",
+    "    weights, bias, _, _ = opt.step(cost, weights, bias, X_batch, Y_batch)\n",
+    "\n",
+    "    # Compute the accuracy\n",
+    "    predictions = [np.sign(variational_classifier(weights, bias, x)) for x in X]\n",
+    "    \n",
+    "    if accuracy(Y, predictions) > abest:\n",
+    "        wbest = weights\n",
+    "        bbest = bias\n",
+    "        abest = accuracy(Y, predictions)\n",
+    "        print('New best')\n",
+    "\n",
+    "    acc = accuracy(Y, predictions)\n",
+    "\n",
+    "    print(\n",
+    "        \"Iter: {:5d} | Cost: {:0.7f} | Accuracy: {:0.7f} \".format(\n",
+    "            it + 1, cost(weights, bias, X, Y), acc\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Yte = np.array(y_test.values[:,0] * 2 - np.ones(len(y_test.values[:,0])), requires_grad = False)\n",
+    "Xte = np.array(normalize(x_test_lda_n), requires_grad=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost: 0.5195109293724983, Accuracy: 86.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = [np.sign(variational_classifier(wbest, bbest, x)) for x in Xte]\n",
+    "pred = [np.sign(variational_classifier(wbest, bbest, x)) for x in X]\n",
+    "acc = accuracy(Yte, predictions)\n",
+    "\n",
+    "print(f'Cost: {cost(wbest, bbest, Xte, Yte)}, Accuracy: {np.round(acc, 2) * 100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predictions</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Predictions  Test\n",
+       "0          -1.0  -1.0\n",
+       "1          -1.0  -1.0\n",
+       "2           1.0   1.0\n",
+       "3          -1.0  -1.0\n",
+       "4          -1.0  -1.0\n",
+       "..          ...   ...\n",
+       "595        -1.0  -1.0\n",
+       "596        -1.0  -1.0\n",
+       "597        -1.0  -1.0\n",
+       "598        -1.0   1.0\n",
+       "599        -1.0  -1.0\n",
+       "\n",
+       "[600 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame((predictions, Yte), ('Predictions', 'Test')).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "        -1.0       0.97      0.85      0.91       493\n",
+      "         1.0       0.56      0.90      0.69       107\n",
+      "\n",
+      "    accuracy                           0.85       600\n",
+      "   macro avg       0.77      0.87      0.80       600\n",
+      "weighted avg       0.90      0.85      0.87       600\n",
+      "\n",
+      "precisioin score: 0.5581395348837209\n",
+      "recall score: 0.897196261682243\n",
+      "f1 score: 0.6881720430107527\n",
+      "balanced accuracy: 0.8715190233360505\n",
+      "confusion matrix: [[417  76]\n",
+      " [ 11  96]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the classification report and important metrics\n",
+    "\n",
+    "print(metrics.classification_report(predictions,Yte))\n",
+    "print(f\"precisioin score: {metrics.precision_score(predictions,Yte)}\")\n",
+    "print(f\"recall score: {metrics.recall_score(predictions,Yte)}\")\n",
+    "print(f\"f1 score: {metrics.f1_score(predictions,Yte)}\")\n",
+    "print(f\"balanced accuracy: {metrics.balanced_accuracy_score(predictions,Yte)}\")\n",
+    "print(f\"confusion matrix: {metrics.confusion_matrix(predictions,Yte)}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "QC",
+   "language": "python",
+   "name": "qc"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5d83d7e0c60eaba1e78b3a460636a8f0ffa6eaee266f7bf3ff4e970676c5e4a4"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/QML4FRAUD/EDA.ipynb b/QML4FRAUD/EDA.ipynb
index 2e75d21..81ccf82 100644
--- a/QML4FRAUD/EDA.ipynb
+++ b/QML4FRAUD/EDA.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +342,7 @@
        "[8 rows x 113 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -664,7 +664,7 @@
        "[8 rows x 98 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -683,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,13 +692,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# save the cleaned data\n",
     "\n",
-    "df_clean.to_csv(\"dataset_nonzero.csv\")"
+    "df_clean.to_csv(\"dataset_cleaned.csv\")"
    ]
   },
   {
@@ -710,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -739,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -747,7 +747,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 1400 entries, 17785 to 17942\n",
+      "Int64Index: 1400 entries, 16246 to 5393\n",
       "Data columns (total 49 columns):\n",
       " #   Column  Non-Null Count  Dtype  \n",
       "---  ------  --------------  -----  \n",
@@ -826,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -834,15 +834,15 @@
     "\n",
     "lda = LDA(n_components=1)\n",
     "features_lda_1 = lda.fit_transform(features_a, y_train.values.ravel())\n",
-    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "features_lda_1_test = lda.transform(features_a_test)\n",
     "\n",
-    "features_lda_1_test = lda.fit_transform(features_a_test, y_test.values.ravel())\n",
-    "features_lda_2_test = lda.fit_transform(features_b_test, y_test.values.ravel())"
+    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "features_lda_2_test = lda.transform(features_b_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -855,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -885,23 +885,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.233199</td>\n",
+       "      <td>-1.647234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.517182</td>\n",
+       "      <td>-0.315169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.405583</td>\n",
+       "      <td>-1.624434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.811352</td>\n",
+       "      <td>0.647323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.126657</td>\n",
+       "      <td>-0.890059</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -909,14 +909,14 @@
       ],
       "text/plain": [
        "          0\n",
-       "0 -1.233199\n",
-       "1  0.517182\n",
-       "2  0.405583\n",
-       "3  0.811352\n",
-       "4 -1.126657"
+       "0 -1.647234\n",
+       "1 -0.315169\n",
+       "2 -1.624434\n",
+       "3  0.647323\n",
+       "4 -0.890059"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -927,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -970,28 +970,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-1.233199</td>\n",
-       "      <td>0.185201</td>\n",
+       "      <td>-1.647234</td>\n",
+       "      <td>-1.062756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.517182</td>\n",
-       "      <td>-0.051898</td>\n",
+       "      <td>-0.315169</td>\n",
+       "      <td>-0.704260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.405583</td>\n",
-       "      <td>-0.051898</td>\n",
+       "      <td>-1.624434</td>\n",
+       "      <td>-0.486132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.811352</td>\n",
-       "      <td>-1.167328</td>\n",
+       "      <td>0.647323</td>\n",
+       "      <td>-0.111503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.126657</td>\n",
-       "      <td>-0.696000</td>\n",
+       "      <td>-0.890059</td>\n",
+       "      <td>-0.912891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1000,28 +1000,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1395</th>\n",
-       "      <td>0.590027</td>\n",
-       "      <td>-0.962164</td>\n",
+       "      <td>-0.865866</td>\n",
+       "      <td>-0.531186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1396</th>\n",
-       "      <td>-2.931781</td>\n",
-       "      <td>-1.345132</td>\n",
+       "      <td>0.207545</td>\n",
+       "      <td>-0.111503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1397</th>\n",
-       "      <td>0.410028</td>\n",
-       "      <td>0.269090</td>\n",
+       "      <td>0.955061</td>\n",
+       "      <td>3.510328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1398</th>\n",
-       "      <td>-2.902701</td>\n",
-       "      <td>-0.953910</td>\n",
+       "      <td>0.683500</td>\n",
+       "      <td>0.411537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
-       "      <td>0.387806</td>\n",
-       "      <td>-0.886261</td>\n",
+       "      <td>0.840189</td>\n",
+       "      <td>3.468856</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1030,22 +1030,22 @@
       ],
       "text/plain": [
        "        0_left   0_right\n",
-       "0    -1.233199  0.185201\n",
-       "1     0.517182 -0.051898\n",
-       "2     0.405583 -0.051898\n",
-       "3     0.811352 -1.167328\n",
-       "4    -1.126657 -0.696000\n",
+       "0    -1.647234 -1.062756\n",
+       "1    -0.315169 -0.704260\n",
+       "2    -1.624434 -0.486132\n",
+       "3     0.647323 -0.111503\n",
+       "4    -0.890059 -0.912891\n",
        "...        ...       ...\n",
-       "1395  0.590027 -0.962164\n",
-       "1396 -2.931781 -1.345132\n",
-       "1397  0.410028  0.269090\n",
-       "1398 -2.902701 -0.953910\n",
-       "1399  0.387806 -0.886261\n",
+       "1395 -0.865866 -0.531186\n",
+       "1396  0.207545 -0.111503\n",
+       "1397  0.955061  3.510328\n",
+       "1398  0.683500  0.411537\n",
+       "1399  0.840189  3.468856\n",
        "\n",
        "[1400 rows x 2 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1087,28 +1087,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.516931</td>\n",
-       "      <td>1.476801</td>\n",
+       "      <td>0.979135</td>\n",
+       "      <td>0.411537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.038029</td>\n",
-       "      <td>-0.384427</td>\n",
+       "      <td>0.429368</td>\n",
+       "      <td>1.964625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.516965</td>\n",
-       "      <td>1.708365</td>\n",
+       "      <td>-0.920524</td>\n",
+       "      <td>-1.649966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.710725</td>\n",
-       "      <td>-0.208194</td>\n",
+       "      <td>1.350660</td>\n",
+       "      <td>-0.111503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.889208</td>\n",
-       "      <td>-1.515469</td>\n",
+       "      <td>0.593554</td>\n",
+       "      <td>0.604855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1117,28 +1117,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>595</th>\n",
-       "      <td>-0.540662</td>\n",
-       "      <td>-0.108684</td>\n",
+       "      <td>0.811924</td>\n",
+       "      <td>-0.524621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>0.769335</td>\n",
-       "      <td>2.802327</td>\n",
+       "      <td>-0.283575</td>\n",
+       "      <td>-0.893253</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
-       "      <td>0.651577</td>\n",
-       "      <td>-0.117112</td>\n",
+       "      <td>0.790146</td>\n",
+       "      <td>1.775425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
-       "      <td>-0.394235</td>\n",
-       "      <td>2.164792</td>\n",
+       "      <td>-1.128501</td>\n",
+       "      <td>-0.131876</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
-       "      <td>0.861443</td>\n",
-       "      <td>-0.110812</td>\n",
+       "      <td>0.557590</td>\n",
+       "      <td>-0.766294</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1147,22 +1147,22 @@
       ],
       "text/plain": [
        "       0_left   0_right\n",
-       "0    1.516931  1.476801\n",
-       "1    0.038029 -0.384427\n",
-       "2    0.516965  1.708365\n",
-       "3    1.710725 -0.208194\n",
-       "4    0.889208 -1.515469\n",
+       "0    0.979135  0.411537\n",
+       "1    0.429368  1.964625\n",
+       "2   -0.920524 -1.649966\n",
+       "3    1.350660 -0.111503\n",
+       "4    0.593554  0.604855\n",
        "..        ...       ...\n",
-       "595 -0.540662 -0.108684\n",
-       "596  0.769335  2.802327\n",
-       "597  0.651577 -0.117112\n",
-       "598 -0.394235  2.164792\n",
-       "599  0.861443 -0.110812\n",
+       "595  0.811924 -0.524621\n",
+       "596 -0.283575 -0.893253\n",
+       "597  0.790146  1.775425\n",
+       "598 -1.128501 -0.131876\n",
+       "599  0.557590 -0.766294\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1173,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,22 +1188,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.32047703,  1.02866565],\n",
-       "       [ 0.03310356, -0.2677724 ],\n",
-       "       [ 0.45001448,  1.18996136],\n",
+       "array([[ 0.76170468,  0.31532716],\n",
+       "       [ 0.31445428,  1.48863532],\n",
+       "       [-0.78372021, -1.24207227],\n",
        "       ...,\n",
-       "       [ 0.56719272, -0.08157415],\n",
-       "       [-0.34317897,  1.50788522],\n",
-       "       [ 0.74987996, -0.07718603]])"
+       "       [ 0.60795684,  1.34570102],\n",
+       "       [-0.95291501, -0.0952042 ],\n",
+       "       [ 0.41876601, -0.57448618]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1223,19 +1223,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        73.071429\n",
-       "1.0        26.928571\n",
+       "0.0        73.142857\n",
+       "1.0        26.857143\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1248,19 +1248,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        72.0\n",
-       "1.0        28.0\n",
+       "0.0        71.833333\n",
+       "1.0        28.166667\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1309,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1343,16 +1343,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(-0.09765625, requires_grad=True)"
+       "tensor(0.70507812, requires_grad=True)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1363,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1373,7 +1373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1388,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1405,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1416,18 +1416,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X = [tensor(-1.10428902, requires_grad=False), tensor(0.13841145, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.46311928, requires_grad=False), tensor(-0.03878681, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.36318635, requires_grad=False), tensor(-0.03878681, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.72653848, requires_grad=False), tensor(-0.87241389, requires_grad=False)], Y = -1\n",
-      "X = [tensor(-1.0088843, requires_grad=False), tensor(-0.52016244, requires_grad=False)], Y = -1\n"
+      "X = [tensor(-1.5025838, requires_grad=False), tensor(-0.79914964, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.28749305, requires_grad=False), tensor(-0.52957516, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-1.48178608, requires_grad=False), tensor(-0.36555189, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.59047896, requires_grad=False), tensor(-0.08384601, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.81189893, requires_grad=False), tensor(-0.686457, requires_grad=False)], Y = -1\n"
      ]
     }
    ],
@@ -1441,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1451,7 +1451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1459,73 +1459,71 @@
      "output_type": "stream",
      "text": [
       "New best\n",
-      "Iter:     1 | Cost: 0.8783847 | Accuracy: 0.6642857 \n",
-      "New best\n",
-      "Iter:     2 | Cost: 0.7234059 | Accuracy: 0.7392857 \n",
-      "New best\n",
-      "Iter:     3 | Cost: 0.6404744 | Accuracy: 0.7871429 \n",
+      "Iter:     1 | Cost: 0.7757074 | Accuracy: 0.7328571 \n",
       "New best\n",
-      "Iter:     4 | Cost: 0.6202939 | Accuracy: 0.8021429 \n",
+      "Iter:     2 | Cost: 0.6310626 | Accuracy: 0.7764286 \n",
       "New best\n",
-      "Iter:     5 | Cost: 0.5521156 | Accuracy: 0.8164286 \n",
+      "Iter:     3 | Cost: 0.5791569 | Accuracy: 0.7942857 \n",
       "New best\n",
-      "Iter:     6 | Cost: 0.4705954 | Accuracy: 0.8378571 \n",
-      "Iter:     7 | Cost: 0.4882907 | Accuracy: 0.8350000 \n",
-      "Iter:     8 | Cost: 0.4920552 | Accuracy: 0.8378571 \n",
+      "Iter:     4 | Cost: 0.5505772 | Accuracy: 0.8100000 \n",
       "New best\n",
-      "Iter:     9 | Cost: 0.4743783 | Accuracy: 0.8414286 \n",
+      "Iter:     5 | Cost: 0.5335625 | Accuracy: 0.8121429 \n",
       "New best\n",
-      "Iter:    10 | Cost: 0.4615172 | Accuracy: 0.8485714 \n",
-      "Iter:    11 | Cost: 0.4604754 | Accuracy: 0.8457143 \n",
+      "Iter:     6 | Cost: 0.5129484 | Accuracy: 0.8178571 \n",
       "New best\n",
-      "Iter:    12 | Cost: 0.4666229 | Accuracy: 0.8507143 \n",
-      "Iter:    13 | Cost: 0.4739211 | Accuracy: 0.8485714 \n",
-      "Iter:    14 | Cost: 0.4938755 | Accuracy: 0.8478571 \n",
-      "Iter:    15 | Cost: 0.4932492 | Accuracy: 0.8507143 \n",
+      "Iter:     7 | Cost: 0.5097865 | Accuracy: 0.8264286 \n",
       "New best\n",
-      "Iter:    16 | Cost: 0.4804728 | Accuracy: 0.8557143 \n",
+      "Iter:     8 | Cost: 0.5102069 | Accuracy: 0.8307143 \n",
+      "Iter:     9 | Cost: 0.4956267 | Accuracy: 0.8292857 \n",
       "New best\n",
-      "Iter:    17 | Cost: 0.4636001 | Accuracy: 0.8564286 \n",
+      "Iter:    10 | Cost: 0.4752182 | Accuracy: 0.8371429 \n",
+      "Iter:    11 | Cost: 0.5085655 | Accuracy: 0.8300000 \n",
+      "Iter:    12 | Cost: 0.5449851 | Accuracy: 0.8092857 \n",
+      "Iter:    13 | Cost: 0.4992988 | Accuracy: 0.8292857 \n",
       "New best\n",
-      "Iter:    18 | Cost: 0.4547634 | Accuracy: 0.8592857 \n",
+      "Iter:    14 | Cost: 0.4698252 | Accuracy: 0.8421429 \n",
       "New best\n",
-      "Iter:    19 | Cost: 0.4482752 | Accuracy: 0.8635714 \n",
+      "Iter:    15 | Cost: 0.4603315 | Accuracy: 0.8457143 \n",
+      "Iter:    16 | Cost: 0.4665378 | Accuracy: 0.8435714 \n",
+      "Iter:    17 | Cost: 0.4925975 | Accuracy: 0.8371429 \n",
+      "Iter:    18 | Cost: 0.5147517 | Accuracy: 0.8392857 \n",
+      "Iter:    19 | Cost: 0.5002796 | Accuracy: 0.8457143 \n",
       "New best\n",
-      "Iter:    20 | Cost: 0.4517178 | Accuracy: 0.8657143 \n",
+      "Iter:    20 | Cost: 0.4813663 | Accuracy: 0.8521429 \n",
       "New best\n",
-      "Iter:    21 | Cost: 0.4581175 | Accuracy: 0.8742857 \n",
+      "Iter:    21 | Cost: 0.4497377 | Accuracy: 0.8528571 \n",
       "New best\n",
-      "Iter:    22 | Cost: 0.4630016 | Accuracy: 0.8750000 \n",
+      "Iter:    22 | Cost: 0.4209442 | Accuracy: 0.8628571 \n",
       "New best\n",
-      "Iter:    23 | Cost: 0.4418877 | Accuracy: 0.8764286 \n",
-      "Iter:    24 | Cost: 0.4285966 | Accuracy: 0.8757143 \n",
-      "Iter:    25 | Cost: 0.4181510 | Accuracy: 0.8614286 \n",
-      "Iter:    26 | Cost: 0.4227318 | Accuracy: 0.8500000 \n",
-      "Iter:    27 | Cost: 0.4215839 | Accuracy: 0.8507143 \n",
-      "Iter:    28 | Cost: 0.4172597 | Accuracy: 0.8500000 \n",
-      "Iter:    29 | Cost: 0.4096631 | Accuracy: 0.8571429 \n",
+      "Iter:    23 | Cost: 0.4072826 | Accuracy: 0.8642857 \n",
+      "Iter:    24 | Cost: 0.4104807 | Accuracy: 0.8592857 \n",
+      "Iter:    25 | Cost: 0.4147440 | Accuracy: 0.8600000 \n",
+      "Iter:    26 | Cost: 0.4236694 | Accuracy: 0.8571429 \n",
+      "Iter:    27 | Cost: 0.4308793 | Accuracy: 0.8557143 \n",
+      "Iter:    28 | Cost: 0.4361225 | Accuracy: 0.8550000 \n",
+      "Iter:    29 | Cost: 0.4544455 | Accuracy: 0.8507143 \n",
+      "Iter:    30 | Cost: 0.4720752 | Accuracy: 0.8500000 \n",
+      "Iter:    31 | Cost: 0.4888478 | Accuracy: 0.8500000 \n",
+      "Iter:    32 | Cost: 0.4792126 | Accuracy: 0.8507143 \n",
+      "Iter:    33 | Cost: 0.4466107 | Accuracy: 0.8528571 \n",
+      "Iter:    34 | Cost: 0.4336227 | Accuracy: 0.8564286 \n",
+      "Iter:    35 | Cost: 0.4236255 | Accuracy: 0.8571429 \n",
+      "Iter:    36 | Cost: 0.4263112 | Accuracy: 0.8621429 \n",
       "New best\n",
-      "Iter:    30 | Cost: 0.4321907 | Accuracy: 0.8792857 \n",
-      "Iter:    31 | Cost: 0.4978105 | Accuracy: 0.8478571 \n",
-      "Iter:    32 | Cost: 0.6312185 | Accuracy: 0.7442857 \n",
-      "Iter:    33 | Cost: 0.7515308 | Accuracy: 0.6700000 \n",
-      "Iter:    34 | Cost: 0.6126481 | Accuracy: 0.7542857 \n",
-      "Iter:    35 | Cost: 0.4772270 | Accuracy: 0.8600000 \n",
-      "Iter:    36 | Cost: 0.4138549 | Accuracy: 0.8692857 \n",
-      "Iter:    37 | Cost: 0.3972903 | Accuracy: 0.8692857 \n",
-      "Iter:    38 | Cost: 0.3988931 | Accuracy: 0.8650000 \n",
-      "Iter:    39 | Cost: 0.4060796 | Accuracy: 0.8650000 \n",
-      "Iter:    40 | Cost: 0.4150995 | Accuracy: 0.8600000 \n",
-      "Iter:    41 | Cost: 0.4265430 | Accuracy: 0.8564286 \n",
-      "Iter:    42 | Cost: 0.4303090 | Accuracy: 0.8535714 \n",
-      "Iter:    43 | Cost: 0.4375184 | Accuracy: 0.8571429 \n",
-      "Iter:    44 | Cost: 0.4494775 | Accuracy: 0.8535714 \n",
-      "Iter:    45 | Cost: 0.4971204 | Accuracy: 0.8378571 \n",
-      "Iter:    46 | Cost: 0.4675429 | Accuracy: 0.8521429 \n",
-      "Iter:    47 | Cost: 0.4459844 | Accuracy: 0.8542857 \n",
-      "Iter:    48 | Cost: 0.4309135 | Accuracy: 0.8521429 \n",
-      "Iter:    49 | Cost: 0.4408698 | Accuracy: 0.8492857 \n",
-      "Iter:    50 | Cost: 0.4509176 | Accuracy: 0.8471429 \n"
+      "Iter:    37 | Cost: 0.4653883 | Accuracy: 0.8671429 \n",
+      "Iter:    38 | Cost: 0.4934635 | Accuracy: 0.8578571 \n",
+      "Iter:    39 | Cost: 0.4588661 | Accuracy: 0.8642857 \n",
+      "Iter:    40 | Cost: 0.4345780 | Accuracy: 0.8600000 \n",
+      "Iter:    41 | Cost: 0.4107692 | Accuracy: 0.8607143 \n",
+      "Iter:    42 | Cost: 0.4134072 | Accuracy: 0.8607143 \n",
+      "Iter:    43 | Cost: 0.4174631 | Accuracy: 0.8600000 \n",
+      "Iter:    44 | Cost: 0.4161263 | Accuracy: 0.8621429 \n",
+      "Iter:    45 | Cost: 0.4131321 | Accuracy: 0.8600000 \n",
+      "Iter:    46 | Cost: 0.4167386 | Accuracy: 0.8650000 \n",
+      "Iter:    47 | Cost: 0.4249988 | Accuracy: 0.8635714 \n",
+      "Iter:    48 | Cost: 0.4351619 | Accuracy: 0.8592857 \n",
+      "Iter:    49 | Cost: 0.4428742 | Accuracy: 0.8600000 \n",
+      "Iter:    50 | Cost: 0.4408459 | Accuracy: 0.8571429 \n"
      ]
     }
    ],
@@ -1566,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1576,14 +1574,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cost: 0.46517865137593795, Accuracy: 84.0%\n"
+      "Cost: 0.5805768328807487, Accuracy: 82.0%\n"
      ]
     }
    ],
@@ -1597,7 +1595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1633,22 +1631,22 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1663,13 +1661,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
@@ -1678,8 +1676,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1689,21 +1687,21 @@
       "text/plain": [
        "    Predictions  Test\n",
        "0           1.0   1.0\n",
-       "1          -1.0  -1.0\n",
-       "2           1.0   1.0\n",
-       "3           1.0  -1.0\n",
-       "4          -1.0  -1.0\n",
+       "1           1.0   1.0\n",
+       "2          -1.0  -1.0\n",
+       "3           1.0   1.0\n",
+       "4           1.0  -1.0\n",
        "..          ...   ...\n",
        "595        -1.0  -1.0\n",
-       "596         1.0   1.0\n",
-       "597         1.0  -1.0\n",
+       "596        -1.0  -1.0\n",
+       "597         1.0   1.0\n",
        "598        -1.0   1.0\n",
-       "599         1.0   1.0\n",
+       "599        -1.0  -1.0\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1714,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1723,19 +1721,19 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.86      0.92      0.89       405\n",
-      "         1.0       0.80      0.69      0.74       195\n",
+      "        -1.0       0.87      0.87      0.87       432\n",
+      "         1.0       0.67      0.68      0.68       168\n",
       "\n",
-      "    accuracy                           0.84       600\n",
-      "   macro avg       0.83      0.81      0.82       600\n",
-      "weighted avg       0.84      0.84      0.84       600\n",
+      "    accuracy                           0.82       600\n",
+      "   macro avg       0.77      0.78      0.78       600\n",
+      "weighted avg       0.82      0.82      0.82       600\n",
       "\n",
-      "precisioin score: 0.8035714285714286\n",
-      "recall score: 0.6923076923076923\n",
-      "f1 score: 0.7438016528925621\n",
-      "balanced accuracy: 0.8054131054131054\n",
-      "confusion matrix: [[372  33]\n",
-      " [ 60 135]]\n"
+      "precisioin score: 0.6745562130177515\n",
+      "recall score: 0.6785714285714286\n",
+      "f1 score: 0.6765578635014836\n",
+      "balanced accuracy: 0.775628306878307\n",
+      "confusion matrix: [[377  55]\n",
+      " [ 54 114]]\n"
      ]
     }
    ],
@@ -1761,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1793,7 +1791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1991,7 +1989,7 @@
        "[5 rows x 97 columns]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2003,7 +2001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2026,7 +2024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -2056,7 +2054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2069,7 +2067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2118,7 +2116,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7808</th>\n",
+       "      <th>2709</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2133,16 +2131,16 @@
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7908</th>\n",
+       "      <th>3264</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2162,11 +2160,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14342</th>\n",
+       "      <th>11955</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2187,19 +2185,19 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9080</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>14827</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>1.0</td>\n",
@@ -2214,16 +2212,16 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14057</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>6081</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>32.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>0.0</td>\n",
@@ -2238,9 +2236,7 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12761</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>14605</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2249,9 +2245,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2260,20 +2257,22 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15277</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>12.0</td>\n",
+       "      <th>15376</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>43.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2283,10 +2282,9 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6187</th>\n",
+       "      <th>3936</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2298,7 +2296,6 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2307,13 +2304,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15217</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>4703</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2321,20 +2316,23 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4801</th>\n",
+       "      <th>271</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2353,9 +2351,9 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2364,45 +2362,45 @@
       ],
       "text/plain": [
        "       col_0  col_22  col_47  col_66  col_63  col_57  col_50  col_36  col_55  \\\n",
-       "7808     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "7908     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "14342    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "9080     1.0     1.0     2.0     0.0     0.0     0.0     0.0     4.0     0.0   \n",
-       "14057    0.0     0.0     0.0     0.0     0.0     0.0     0.0     2.0     1.0   \n",
-       "12761    2.0     2.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "15277    2.0     3.0    12.0     0.0     0.0     0.0     5.0    43.0     0.0   \n",
-       "6187     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "15217    3.0     3.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "4801     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "2709     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "3264     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "11955    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14827    0.0     0.0     2.0     3.0     2.0     1.0     1.0     4.0     2.0   \n",
+       "6081     1.0     1.0    35.0     0.0     0.0     0.0    27.0    32.0     0.0   \n",
+       "14605    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15376    0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "3936     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4703     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "271      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
        "\n",
        "       col_40  ...  col_104  col_74  col_73  col_84  col_87  col_89  col_86  \\\n",
-       "7808      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "7908      0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "14342     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "9080      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "14057     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "12761     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "15277     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "6187      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "15217     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
-       "4801      0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "2709      0.0  ...      1.0     0.0     0.0     1.0     1.0     1.0     1.0   \n",
+       "3264      0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "11955     0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14827     0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "6081      0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "14605     0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "15376     0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "3936      0.0  ...      0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+       "4703      0.0  ...      1.0     0.0     0.0     0.0     0.0     1.0     1.0   \n",
+       "271       0.0  ...      1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
        "\n",
        "       col_83  targets  col_15  \n",
-       "7808      1.0      0.0     1.0  \n",
-       "7908      0.0      0.0     1.0  \n",
-       "14342     0.0      0.0     0.0  \n",
-       "9080      0.0      0.0     0.0  \n",
-       "14057     0.0      0.0     0.0  \n",
-       "12761     0.0      0.0     0.0  \n",
-       "15277     0.0      0.0     1.0  \n",
-       "6187      0.0      0.0     0.0  \n",
-       "15217     0.0      0.0     0.0  \n",
-       "4801      0.0      1.0     0.0  \n",
+       "2709      1.0      1.0     0.0  \n",
+       "3264      0.0      1.0     0.0  \n",
+       "11955     0.0      0.0     1.0  \n",
+       "14827     0.0      0.0     0.0  \n",
+       "6081      0.0      0.0     0.0  \n",
+       "14605     0.0      0.0     1.0  \n",
+       "15376     0.0      0.0     0.0  \n",
+       "3936      0.0      1.0     0.0  \n",
+       "4703      1.0      1.0     1.0  \n",
+       "271       1.0      1.0     1.0  \n",
        "\n",
        "[10 rows x 97 columns]"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2413,7 +2411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2429,7 +2427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -2438,7 +2436,7 @@
        "((1400, 21), (1400, 75))"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2456,7 +2454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2468,7 +2466,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2486,7 +2484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2494,15 +2492,15 @@
     "\n",
     "lda = LDA(n_components=1)\n",
     "features_lda_1 = lda.fit_transform(features_a, y_train.values.ravel())\n",
-    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "features_lda_1_test = lda.transform(features_a_test)\n",
     "\n",
-    "features_lda_1_test = lda.fit_transform(features_a_test, y_test.values.ravel())\n",
-    "features_lda_2_test = lda.fit_transform(features_b_test, y_test.values.ravel())"
+    "features_lda_2 = lda.fit_transform(features_b, y_train.values.ravel())\n",
+    "features_lda_2_test = lda.transform(features_b_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2515,7 +2513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -2545,23 +2543,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.029022</td>\n",
+       "      <td>-0.337612</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.368815</td>\n",
+       "      <td>0.424144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.364754</td>\n",
+       "      <td>-1.260492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.380754</td>\n",
+       "      <td>0.454699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.348290</td>\n",
+       "      <td>-0.008053</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2569,14 +2567,14 @@
       ],
       "text/plain": [
        "          0\n",
-       "0  0.029022\n",
-       "1  0.368815\n",
-       "2  0.364754\n",
-       "3  0.380754\n",
-       "4  0.348290"
+       "0 -0.337612\n",
+       "1  0.424144\n",
+       "2 -1.260492\n",
+       "3  0.454699\n",
+       "4 -0.008053"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2587,7 +2585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2599,7 +2597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -2630,28 +2628,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.029022</td>\n",
-       "      <td>-0.293507</td>\n",
+       "      <td>-0.337612</td>\n",
+       "      <td>-1.688559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.368815</td>\n",
-       "      <td>0.154781</td>\n",
+       "      <td>0.424144</td>\n",
+       "      <td>-0.779975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.364754</td>\n",
-       "      <td>-0.136371</td>\n",
+       "      <td>-1.260492</td>\n",
+       "      <td>-1.135964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.380754</td>\n",
-       "      <td>-0.466693</td>\n",
+       "      <td>0.454699</td>\n",
+       "      <td>-0.091676</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.348290</td>\n",
-       "      <td>-1.429328</td>\n",
+       "      <td>-0.008053</td>\n",
+       "      <td>-1.018340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2660,28 +2658,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1395</th>\n",
-       "      <td>0.239483</td>\n",
-       "      <td>-0.752545</td>\n",
+       "      <td>0.369068</td>\n",
+       "      <td>-0.727799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1396</th>\n",
-       "      <td>-9.149074</td>\n",
-       "      <td>-1.870988</td>\n",
+       "      <td>0.414402</td>\n",
+       "      <td>0.031835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1397</th>\n",
-       "      <td>0.298241</td>\n",
-       "      <td>0.392145</td>\n",
+       "      <td>0.431892</td>\n",
+       "      <td>3.308423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1398</th>\n",
-       "      <td>-0.939658</td>\n",
-       "      <td>-1.695210</td>\n",
+       "      <td>0.449680</td>\n",
+       "      <td>0.387571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
-       "      <td>0.301167</td>\n",
-       "      <td>-0.701224</td>\n",
+       "      <td>0.450345</td>\n",
+       "      <td>3.256379</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2690,22 +2688,22 @@
       ],
       "text/plain": [
        "        0_left   0_right\n",
-       "0     0.029022 -0.293507\n",
-       "1     0.368815  0.154781\n",
-       "2     0.364754 -0.136371\n",
-       "3     0.380754 -0.466693\n",
-       "4     0.348290 -1.429328\n",
+       "0    -0.337612 -1.688559\n",
+       "1     0.424144 -0.779975\n",
+       "2    -1.260492 -1.135964\n",
+       "3     0.454699 -0.091676\n",
+       "4    -0.008053 -1.018340\n",
        "...        ...       ...\n",
-       "1395  0.239483 -0.752545\n",
-       "1396 -9.149074 -1.870988\n",
-       "1397  0.298241  0.392145\n",
-       "1398 -0.939658 -1.695210\n",
-       "1399  0.301167 -0.701224\n",
+       "1395  0.369068 -0.727799\n",
+       "1396  0.414402  0.031835\n",
+       "1397  0.431892  3.308423\n",
+       "1398  0.449680  0.387571\n",
+       "1399  0.450345  3.256379\n",
        "\n",
        "[1400 rows x 2 columns]"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2716,7 +2714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2747,28 +2745,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.552360</td>\n",
-       "      <td>2.152633</td>\n",
+       "      <td>0.450640</td>\n",
+       "      <td>0.926243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.387320</td>\n",
-       "      <td>-0.313518</td>\n",
+       "      <td>0.482912</td>\n",
+       "      <td>1.736359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.547085</td>\n",
-       "      <td>1.668552</td>\n",
+       "      <td>-0.878407</td>\n",
+       "      <td>-1.201244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.545153</td>\n",
-       "      <td>0.298175</td>\n",
+       "      <td>0.450493</td>\n",
+       "      <td>0.578571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.148260</td>\n",
-       "      <td>-1.430355</td>\n",
+       "      <td>-0.162323</td>\n",
+       "      <td>0.700400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2777,28 +2775,28 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>595</th>\n",
-       "      <td>0.510857</td>\n",
-       "      <td>-0.539991</td>\n",
+       "      <td>0.390797</td>\n",
+       "      <td>-0.101607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>0.543589</td>\n",
-       "      <td>2.828810</td>\n",
+       "      <td>0.400229</td>\n",
+       "      <td>-1.225276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
-       "      <td>0.198730</td>\n",
-       "      <td>0.010667</td>\n",
+       "      <td>0.439348</td>\n",
+       "      <td>1.836483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
-       "      <td>0.339441</td>\n",
-       "      <td>1.651147</td>\n",
+       "      <td>0.343268</td>\n",
+       "      <td>-0.491754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
-       "      <td>0.549595</td>\n",
-       "      <td>0.094715</td>\n",
+       "      <td>0.450197</td>\n",
+       "      <td>-0.499059</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2807,22 +2805,22 @@
       ],
       "text/plain": [
        "       0_left   0_right\n",
-       "0    0.552360  2.152633\n",
-       "1    0.387320 -0.313518\n",
-       "2    0.547085  1.668552\n",
-       "3    0.545153  0.298175\n",
-       "4    0.148260 -1.430355\n",
+       "0    0.450640  0.926243\n",
+       "1    0.482912  1.736359\n",
+       "2   -0.878407 -1.201244\n",
+       "3    0.450493  0.578571\n",
+       "4   -0.162323  0.700400\n",
        "..        ...       ...\n",
-       "595  0.510857 -0.539991\n",
-       "596  0.543589  2.828810\n",
-       "597  0.198730  0.010667\n",
-       "598  0.339441  1.651147\n",
-       "599  0.549595  0.094715\n",
+       "595  0.390797 -0.101607\n",
+       "596  0.400229 -1.225276\n",
+       "597  0.439348  1.836483\n",
+       "598  0.343268 -0.491754\n",
+       "599  0.450197 -0.499059\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2833,7 +2831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2848,22 +2846,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.53286889,  1.4488161 ],\n",
-       "       [ 0.37365254, -0.2110115 ],\n",
-       "       [ 0.52778047,  1.1230087 ],\n",
+       "array([[ 0.34175609,  0.66880864],\n",
+       "       [ 0.36658269,  1.26556677],\n",
+       "       [-0.68066633, -0.89836773],\n",
        "       ...,\n",
-       "       [ 0.191717  ,  0.00717903],\n",
-       "       [ 0.32746337,  1.1112944 ],\n",
-       "       [ 0.53020126,  0.06374722]])"
+       "       [ 0.33306869,  1.33932162],\n",
+       "       [ 0.25915566, -0.37573438],\n",
+       "       [ 0.34141525, -0.38111563]])"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2874,7 +2872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2883,19 +2881,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        73.071429\n",
-       "1.0        26.928571\n",
+       "0.0        73.142857\n",
+       "1.0        26.857143\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2908,19 +2906,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "targets\n",
-       "0.0        72.0\n",
-       "1.0        28.0\n",
+       "0.0        71.833333\n",
+       "1.0        28.166667\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2942,7 +2940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2969,7 +2967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -3003,16 +3001,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0.01757812, requires_grad=True)"
+       "tensor(0.35351562, requires_grad=True)"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3023,18 +3021,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X = [tensor(0.02854756, requires_grad=False), tensor(-0.20927842, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.36279179, requires_grad=False), tensor(0.11036303, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.35879728, requires_grad=False), tensor(-0.09723657, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.37453586, requires_grad=False), tensor(-0.33276499, requires_grad=False)], Y = -1\n",
-      "X = [tensor(0.34260141, requires_grad=False), tensor(-1.01914988, requires_grad=False)], Y = -1\n"
+      "X = [tensor(-0.33232882, requires_grad=False), tensor(-1.23501011, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.41750687, requires_grad=False), tensor(-0.57047307, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-1.24076777, requires_grad=False), tensor(-0.83084238, requires_grad=False)], Y = -1\n",
+      "X = [tensor(0.44758408, requires_grad=False), tensor(-0.06705202, requires_grad=False)], Y = -1\n",
+      "X = [tensor(-0.00792708, requires_grad=False), tensor(-0.74481253, requires_grad=False)], Y = -1\n"
      ]
     }
    ],
@@ -3048,7 +3046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3058,7 +3056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -3066,64 +3064,69 @@
      "output_type": "stream",
      "text": [
       "New best\n",
-      "Iter:     1 | Cost: 0.6805348 | Accuracy: 0.7614286 \n",
+      "Iter:     1 | Cost: 0.6840120 | Accuracy: 0.7707143 \n",
+      "New best\n",
+      "Iter:     2 | Cost: 0.6884128 | Accuracy: 0.7721429 \n",
+      "New best\n",
+      "Iter:     3 | Cost: 0.6497199 | Accuracy: 0.7907143 \n",
       "New best\n",
-      "Iter:     2 | Cost: 0.6148319 | Accuracy: 0.7678571 \n",
-      "Iter:     3 | Cost: 0.6655624 | Accuracy: 0.7585714 \n",
+      "Iter:     4 | Cost: 0.5526323 | Accuracy: 0.8092857 \n",
       "New best\n",
-      "Iter:     4 | Cost: 0.6156780 | Accuracy: 0.7814286 \n",
+      "Iter:     5 | Cost: 0.5106892 | Accuracy: 0.8278571 \n",
       "New best\n",
-      "Iter:     5 | Cost: 0.5232250 | Accuracy: 0.8035714 \n",
+      "Iter:     6 | Cost: 0.4907973 | Accuracy: 0.8342857 \n",
       "New best\n",
-      "Iter:     6 | Cost: 0.4934695 | Accuracy: 0.8407143 \n",
+      "Iter:     7 | Cost: 0.4633183 | Accuracy: 0.8385714 \n",
       "New best\n",
-      "Iter:     7 | Cost: 0.5084396 | Accuracy: 0.8671429 \n",
-      "Iter:     8 | Cost: 0.4678253 | Accuracy: 0.8592857 \n",
-      "Iter:     9 | Cost: 0.4462772 | Accuracy: 0.8428571 \n",
-      "Iter:    10 | Cost: 0.4559256 | Accuracy: 0.8257143 \n",
-      "Iter:    11 | Cost: 0.4617289 | Accuracy: 0.8221429 \n",
-      "Iter:    12 | Cost: 0.4551549 | Accuracy: 0.8371429 \n",
-      "Iter:    13 | Cost: 0.4708027 | Accuracy: 0.8457143 \n",
-      "Iter:    14 | Cost: 0.4739704 | Accuracy: 0.8542857 \n",
-      "Iter:    15 | Cost: 0.4484922 | Accuracy: 0.8557143 \n",
-      "Iter:    16 | Cost: 0.4309202 | Accuracy: 0.8571429 \n",
-      "Iter:    17 | Cost: 0.4304361 | Accuracy: 0.8421429 \n",
-      "Iter:    18 | Cost: 0.4380497 | Accuracy: 0.8400000 \n",
-      "Iter:    19 | Cost: 0.4884551 | Accuracy: 0.8492857 \n",
-      "Iter:    20 | Cost: 0.5610199 | Accuracy: 0.8435714 \n",
-      "Iter:    21 | Cost: 0.5085669 | Accuracy: 0.8535714 \n",
-      "Iter:    22 | Cost: 0.4507345 | Accuracy: 0.8635714 \n",
-      "Iter:    23 | Cost: 0.4116641 | Accuracy: 0.8671429 \n",
-      "Iter:    24 | Cost: 0.3996521 | Accuracy: 0.8650000 \n",
-      "Iter:    25 | Cost: 0.3943720 | Accuracy: 0.8621429 \n",
-      "Iter:    26 | Cost: 0.3956733 | Accuracy: 0.8635714 \n",
-      "Iter:    27 | Cost: 0.3997569 | Accuracy: 0.8642857 \n",
-      "Iter:    28 | Cost: 0.4042525 | Accuracy: 0.8592857 \n",
-      "Iter:    29 | Cost: 0.4165210 | Accuracy: 0.8650000 \n",
-      "Iter:    30 | Cost: 0.4319129 | Accuracy: 0.8657143 \n",
+      "Iter:     8 | Cost: 0.4659939 | Accuracy: 0.8400000 \n",
       "New best\n",
-      "Iter:    31 | Cost: 0.4441879 | Accuracy: 0.8678571 \n",
+      "Iter:     9 | Cost: 0.4767518 | Accuracy: 0.8442857 \n",
       "New best\n",
-      "Iter:    32 | Cost: 0.4851312 | Accuracy: 0.8692857 \n",
+      "Iter:    10 | Cost: 0.4574803 | Accuracy: 0.8492857 \n",
+      "Iter:    11 | Cost: 0.4465304 | Accuracy: 0.8492857 \n",
+      "Iter:    12 | Cost: 0.5129355 | Accuracy: 0.8428571 \n",
       "New best\n",
-      "Iter:    33 | Cost: 0.4647126 | Accuracy: 0.8757143 \n",
-      "Iter:    34 | Cost: 0.4018195 | Accuracy: 0.8700000 \n",
-      "Iter:    35 | Cost: 0.3970764 | Accuracy: 0.8614286 \n",
-      "Iter:    36 | Cost: 0.4077688 | Accuracy: 0.8557143 \n",
-      "Iter:    37 | Cost: 0.4022449 | Accuracy: 0.8571429 \n",
-      "Iter:    38 | Cost: 0.4065742 | Accuracy: 0.8557143 \n",
-      "Iter:    39 | Cost: 0.3877503 | Accuracy: 0.8585714 \n",
-      "Iter:    40 | Cost: 0.3806838 | Accuracy: 0.8607143 \n",
-      "Iter:    41 | Cost: 0.3807323 | Accuracy: 0.8607143 \n",
-      "Iter:    42 | Cost: 0.3761005 | Accuracy: 0.8657143 \n",
-      "Iter:    43 | Cost: 0.3762085 | Accuracy: 0.8742857 \n",
-      "Iter:    44 | Cost: 0.3792349 | Accuracy: 0.8735714 \n",
-      "Iter:    45 | Cost: 0.3946513 | Accuracy: 0.8742857 \n",
-      "Iter:    46 | Cost: 0.3932549 | Accuracy: 0.8757143 \n",
-      "Iter:    47 | Cost: 0.3902515 | Accuracy: 0.8742857 \n",
-      "Iter:    48 | Cost: 0.3778917 | Accuracy: 0.8750000 \n",
-      "Iter:    49 | Cost: 0.3754754 | Accuracy: 0.8678571 \n",
-      "Iter:    50 | Cost: 0.3804304 | Accuracy: 0.8585714 \n"
+      "Iter:    13 | Cost: 0.4968205 | Accuracy: 0.8507143 \n",
+      "New best\n",
+      "Iter:    14 | Cost: 0.4471275 | Accuracy: 0.8550000 \n",
+      "Iter:    15 | Cost: 0.4336792 | Accuracy: 0.8528571 \n",
+      "New best\n",
+      "Iter:    16 | Cost: 0.4278970 | Accuracy: 0.8578571 \n",
+      "Iter:    17 | Cost: 0.4443043 | Accuracy: 0.8535714 \n",
+      "Iter:    18 | Cost: 0.4491831 | Accuracy: 0.8578571 \n",
+      "Iter:    19 | Cost: 0.4371018 | Accuracy: 0.8535714 \n",
+      "Iter:    20 | Cost: 0.4388587 | Accuracy: 0.8557143 \n",
+      "Iter:    21 | Cost: 0.4400532 | Accuracy: 0.8514286 \n",
+      "Iter:    22 | Cost: 0.4324835 | Accuracy: 0.8542857 \n",
+      "Iter:    23 | Cost: 0.4225311 | Accuracy: 0.8571429 \n",
+      "New best\n",
+      "Iter:    24 | Cost: 0.4173394 | Accuracy: 0.8600000 \n",
+      "Iter:    25 | Cost: 0.4114748 | Accuracy: 0.8585714 \n",
+      "Iter:    26 | Cost: 0.4122233 | Accuracy: 0.8585714 \n",
+      "Iter:    27 | Cost: 0.4174783 | Accuracy: 0.8535714 \n",
+      "Iter:    28 | Cost: 0.4284635 | Accuracy: 0.8500000 \n",
+      "Iter:    29 | Cost: 0.4492922 | Accuracy: 0.8500000 \n",
+      "Iter:    30 | Cost: 0.4737290 | Accuracy: 0.8471429 \n",
+      "Iter:    31 | Cost: 0.4927850 | Accuracy: 0.8471429 \n",
+      "Iter:    32 | Cost: 0.4791307 | Accuracy: 0.8492857 \n",
+      "Iter:    33 | Cost: 0.4331199 | Accuracy: 0.8521429 \n",
+      "Iter:    34 | Cost: 0.4043683 | Accuracy: 0.8585714 \n",
+      "Iter:    35 | Cost: 0.3997276 | Accuracy: 0.8535714 \n",
+      "Iter:    36 | Cost: 0.4114119 | Accuracy: 0.8542857 \n",
+      "Iter:    37 | Cost: 0.4548296 | Accuracy: 0.8585714 \n",
+      "Iter:    38 | Cost: 0.4957383 | Accuracy: 0.8507143 \n",
+      "Iter:    39 | Cost: 0.4792713 | Accuracy: 0.8507143 \n",
+      "Iter:    40 | Cost: 0.4591796 | Accuracy: 0.8557143 \n",
+      "Iter:    41 | Cost: 0.4242457 | Accuracy: 0.8514286 \n",
+      "Iter:    42 | Cost: 0.4181950 | Accuracy: 0.8535714 \n",
+      "Iter:    43 | Cost: 0.4138558 | Accuracy: 0.8514286 \n",
+      "Iter:    44 | Cost: 0.4046652 | Accuracy: 0.8542857 \n",
+      "Iter:    45 | Cost: 0.3959494 | Accuracy: 0.8542857 \n",
+      "Iter:    46 | Cost: 0.3999658 | Accuracy: 0.8578571 \n",
+      "Iter:    47 | Cost: 0.4110340 | Accuracy: 0.8535714 \n",
+      "Iter:    48 | Cost: 0.4199579 | Accuracy: 0.8521429 \n",
+      "Iter:    49 | Cost: 0.4267979 | Accuracy: 0.8500000 \n",
+      "Iter:    50 | Cost: 0.4167003 | Accuracy: 0.8542857 \n"
      ]
     }
    ],
@@ -3164,7 +3167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3174,14 +3177,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cost: 0.42114813175434823, Accuracy: 88.0%\n"
+      "Cost: 0.47292335627528304, Accuracy: 84.0%\n"
      ]
     }
    ],
@@ -3195,7 +3198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -3231,22 +3234,22 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3261,23 +3264,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>596</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>597</th>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
        "      <td>-1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3287,21 +3290,21 @@
       "text/plain": [
        "    Predictions  Test\n",
        "0           1.0   1.0\n",
-       "1          -1.0  -1.0\n",
-       "2           1.0   1.0\n",
-       "3           1.0  -1.0\n",
-       "4          -1.0  -1.0\n",
+       "1           1.0   1.0\n",
+       "2          -1.0  -1.0\n",
+       "3           1.0   1.0\n",
+       "4           1.0  -1.0\n",
        "..          ...   ...\n",
        "595        -1.0  -1.0\n",
-       "596         1.0   1.0\n",
-       "597        -1.0  -1.0\n",
-       "598         1.0   1.0\n",
-       "599        -1.0   1.0\n",
+       "596        -1.0  -1.0\n",
+       "597         1.0   1.0\n",
+       "598        -1.0   1.0\n",
+       "599        -1.0  -1.0\n",
        "\n",
        "[600 rows x 2 columns]"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3312,7 +3315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -3321,19 +3324,19 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.93      0.91      0.92       443\n",
-      "         1.0       0.75      0.80      0.78       157\n",
+      "        -1.0       0.91      0.86      0.89       456\n",
+      "         1.0       0.63      0.74      0.68       144\n",
       "\n",
-      "    accuracy                           0.88       600\n",
-      "   macro avg       0.84      0.85      0.85       600\n",
-      "weighted avg       0.88      0.88      0.88       600\n",
+      "    accuracy                           0.83       600\n",
+      "   macro avg       0.77      0.80      0.79       600\n",
+      "weighted avg       0.85      0.83      0.84       600\n",
       "\n",
-      "precisioin score: 0.75\n",
-      "recall score: 0.802547770700637\n",
-      "f1 score: 0.7753846153846153\n",
-      "balanced accuracy: 0.8538698221449008\n",
-      "confusion matrix: [[401  42]\n",
-      " [ 31 126]]\n"
+      "precisioin score: 0.6331360946745562\n",
+      "recall score: 0.7430555555555556\n",
+      "f1 score: 0.6837060702875398\n",
+      "balanced accuracy: 0.8035453216374269\n",
+      "confusion matrix: [[394  62]\n",
+      " [ 37 107]]\n"
      ]
     }
    ],
@@ -3352,8 +3355,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that the results are overall better than the half&half split."
+    "It performs better than a half & half split overall."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

From 2675bcf42a50eb35c50a936e2d95417d35f77edf Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 20:58:08 +0800
Subject: [PATCH 14/25] Update Solution via architecture.ipynb

---
 QML4FRAUD/Solution via architecture.ipynb | 82 +++++++++++------------
 1 file changed, 41 insertions(+), 41 deletions(-)

diff --git a/QML4FRAUD/Solution via architecture.ipynb b/QML4FRAUD/Solution via architecture.ipynb
index 10d2735..6b49b30 100644
--- a/QML4FRAUD/Solution via architecture.ipynb	
+++ b/QML4FRAUD/Solution via architecture.ipynb	
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "012381cc",
+   "id": "cfb08b02",
    "metadata": {},
    "source": [
     "Load data in"
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0ba1e89b",
+   "id": "11f9921b",
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5fbafeb",
+   "id": "9c900922",
    "metadata": {},
    "source": [
     "Clean data"
@@ -434,7 +434,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8ea31c16",
+   "id": "e49bab17",
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bbf654a0",
+   "id": "aaa4013e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -756,7 +756,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3382d431",
+   "id": "4fd51c43",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +766,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "cdb80e54",
+   "id": "9c105230",
    "metadata": {},
    "outputs": [
     {
@@ -794,7 +794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f58654b",
+   "id": "ffc13712",
    "metadata": {},
    "source": [
     "Prepare data"
@@ -1236,7 +1236,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ac54ca1f",
+   "id": "d02cf3ec",
    "metadata": {},
    "outputs": [
     {
@@ -1825,7 +1825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "id": "ce01db5f",
    "metadata": {},
    "outputs": [
@@ -1835,29 +1835,29 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.97      0.86      0.91       991\n",
-      "         1.0       0.57      0.86      0.68       209\n",
+      "        -1.0       0.86      0.97      0.91       885\n",
+      "         1.0       0.86      0.57      0.68       315\n",
       "\n",
       "    accuracy                           0.86      1200\n",
-      "   macro avg       0.77      0.86      0.80      1200\n",
-      "weighted avg       0.90      0.86      0.87      1200\n",
+      "   macro avg       0.86      0.77      0.80      1200\n",
+      "weighted avg       0.86      0.86      0.85      1200\n",
       "\n",
-      "0.5682539682539682\n",
       "0.8564593301435407\n",
+      "0.5682539682539682\n",
       "0.683206106870229\n",
-      "0.8596121070495706\n",
-      "[[855 136]\n",
-      " [ 30 179]]\n"
+      "0.7671778315846112\n",
+      "[[855  30]\n",
+      " [136 179]]\n"
      ]
     }
    ],
    "source": [
-    "print(metrics.classification_report(predictions_LDA,Y_test))\n",
-    "print(metrics.precision_score(predictions_LDA,Y_test))\n",
-    "print(metrics.recall_score(predictions_LDA,Y_test))\n",
-    "print(metrics.f1_score(predictions_LDA,Y_test))\n",
-    "print(metrics.balanced_accuracy_score(predictions_LDA,Y_test))\n",
-    "print(metrics.confusion_matrix(predictions_LDA,Y_test))"
+    "print(metrics.classification_report(Y_test, predictions_LDA))\n",
+    "print(metrics.precision_score(Y_test, predictions_LDA))\n",
+    "print(metrics.recall_score(Y_test, predictions_LDA))\n",
+    "print(metrics.f1_score(Y_test, predictions_LDA))\n",
+    "print(metrics.balanced_accuracy_score(Y_test, predictions_LDA))\n",
+    "print(metrics.confusion_matrix(Y_test, predictions_LDA))"
    ]
   },
   {
@@ -1879,7 +1879,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "00669c65",
+   "id": "eaaf6293",
    "metadata": {},
    "outputs": [
     {
@@ -2251,7 +2251,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "63471930",
+   "id": "a4a648d1",
    "metadata": {},
    "outputs": [
     {
@@ -3153,7 +3153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "0a3f0a5a",
    "metadata": {},
    "outputs": [
@@ -3163,29 +3163,29 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        -1.0       0.86      0.83      0.84       920\n",
-      "         1.0       0.49      0.55      0.52       280\n",
+      "        -1.0       0.83      0.86      0.84       885\n",
+      "         1.0       0.55      0.49      0.52       315\n",
       "\n",
       "    accuracy                           0.76      1200\n",
-      "   macro avg       0.68      0.69      0.68      1200\n",
-      "weighted avg       0.77      0.76      0.77      1200\n",
+      "   macro avg       0.69      0.68      0.68      1200\n",
+      "weighted avg       0.75      0.76      0.76      1200\n",
       "\n",
-      "0.49206349206349204\n",
       "0.5535714285714286\n",
+      "0.49206349206349204\n",
       "0.5210084033613446\n",
-      "0.6898291925465838\n",
-      "[[760 160]\n",
-      " [125 155]]\n"
+      "0.6754102771051923\n",
+      "[[760 125]\n",
+      " [160 155]]\n"
      ]
     }
    ],
    "source": [
-    "print(metrics.classification_report(predictions_PCA,Y_test))\n",
-    "print(metrics.precision_score(predictions_PCA,Y_test))\n",
-    "print(metrics.recall_score(predictions_PCA,Y_test))\n",
-    "print(metrics.f1_score(predictions_PCA,Y_test))\n",
-    "print(metrics.balanced_accuracy_score(predictions_PCA,Y_test))\n",
-    "print(metrics.confusion_matrix(predictions_PCA,Y_test))"
+    "print(metrics.classification_report(Y_test, predictions_PCA))\n",
+    "print(metrics.precision_score(Y_test, predictions_PCA))\n",
+    "print(metrics.recall_score(Y_test, predictions_PCA))\n",
+    "print(metrics.f1_score(Y_test, predictions_PCA))\n",
+    "print(metrics.balanced_accuracy_score(Y_test, predictions_PCA))\n",
+    "print(metrics.confusion_matrix(Y_test, predictions_PCA))"
    ]
   },
   {

From 2bd87c0f166925ba457eca889ddf88a39fb4f4ec Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Wed, 27 Jul 2022 09:03:53 -0400
Subject: [PATCH 15/25] Update Business_application.md

---
 QML4FRAUD/Business_application.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/QML4FRAUD/Business_application.md b/QML4FRAUD/Business_application.md
index 8b13789..1646467 100644
--- a/QML4FRAUD/Business_application.md
+++ b/QML4FRAUD/Business_application.md
@@ -1 +1,18 @@
+# Business Application
+
+Machine learning is ubiquitous in many of the best solutions to industrial problems. Quantum computing is proposed to enhance machine learning. We have used the case of fraud detection as a demonstration to show that using LDA as a preprocessing method with a variational classifier could enhance the QML model's performance and allow it to process a lot more features. And we believe that this could be further improved in the future. 
+
+Since this is an improvement based on existing machine learning models, it has direct application in real cases. In principle, this could eventually be generalized for any multi-class classification problem. Some examples [[1]](#1)  from finance include:
+
+- Customer Identification and Scoring
+- Credit Scoring
+- Customer Retention (Churn)
+- Monitor Transactions
+
+
+
+
+## References
+<a id="1">[1]</a> 
+D. J. Egger, C. Gambella, J. Marecek, S. McFaddin, M. Mevissen, R. Raymond, A. Simonetto, S. Woerner, and E. Yndurain, Quantum Computing for Finance: State of the Art and Future Prospects, IEEE Trans. Quantum Eng. 1, 1 (2020).
 

From 5f2c9577b7848784f9ecaae02175576fd107543a Mon Sep 17 00:00:00 2001
From: FoggyBrain <FoggyBrain@users.noreply.github.com>
Date: Wed, 27 Jul 2022 09:22:15 -0400
Subject: [PATCH 16/25] Update Business_application.md

---
 QML4FRAUD/Business_application.md | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/QML4FRAUD/Business_application.md b/QML4FRAUD/Business_application.md
index 1646467..7192014 100644
--- a/QML4FRAUD/Business_application.md
+++ b/QML4FRAUD/Business_application.md
@@ -2,17 +2,22 @@
 
 Machine learning is ubiquitous in many of the best solutions to industrial problems. Quantum computing is proposed to enhance machine learning. We have used the case of fraud detection as a demonstration to show that using LDA as a preprocessing method with a variational classifier could enhance the QML model's performance and allow it to process a lot more features. And we believe that this could be further improved in the future. 
 
-Since this is an improvement based on existing machine learning models, it has direct application in real cases. In principle, this could eventually be generalized for any multi-class classification problem. Some examples [[1]](#1)  from finance include:
+Since this is an improvement based on existing machine learning models, it has direct application in real cases. In principle, this could eventually be generalized for any multi-class classification problem. 
 
-- Customer Identification and Scoring
-- Credit Scoring
-- Customer Retention (Churn)
-- Monitor Transactions
+We are seeing that the finance sector is always within the top 3 industries related to quantum computing use cases and benefits. Example problems that our approach could be applied to include: 
 
+- Credit scoring
+- Fraud detection
+- Default forecasting
+- Churn prediction
+- Loan approval
 
+and many more. 
 
+In other industries, improvement of classification also has many benefits. A quick but not complete list would include:
 
-## References
-<a id="1">[1]</a> 
-D. J. Egger, C. Gambella, J. Marecek, S. McFaddin, M. Mevissen, R. Raymond, A. Simonetto, S. Woerner, and E. Yndurain, Quantum Computing for Finance: State of the Art and Future Prospects, IEEE Trans. Quantum Eng. 1, 1 (2020).
+- Energy: energy loss detection.
+- E-commerce: recommendation system.
+- Several industries: forecasting.
+- Healthcare: diagnosis.
 

From ec03bce7b873ea123efd054a7ddf0c9653e785e1 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:28:42 +0800
Subject: [PATCH 17/25] Update README.md

---
 QML4FRAUD/README.md | 81 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index 8b13789..e2990de 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -1 +1,82 @@
+#Hackathon 2022: QML4FRAUD
+
+This project is QML4FRAUD's submission for CDL Quantum Bootcamp Hackathon 2022, and will guide you through our implementation of using LDA to improve the encoding process of Variation Quantum Circuits in Quantum Machine Learning.
+
+## Our project: Novel approach about classical data preprocessing for classification problems using QML
+
+Quantum Machine Learning (QML) involves the integration of quantum algorithms into machine learning techniques, and has the potential to be one of the more versatile uses of quantum computing, simply because of how far-reaching machine learning is. Furthermore, many QML techniques are robust and are touted to be able to be used in the NISQ-era. However, one of the main problems of implementing quantum algorithms into machine learning is the problem of encoding, which is basically how to encode classical data for hybrid quantum algorithms (in our case VQC). There has been many studies done with interesting results, but those usually have a small number of data pooints, and more importantly, a small number of features. This is due to the fact that current (and for the foreseeable NISQ-era) quantum computers and simulators have a small number of qubits that we can work with, and so encoding a large number of features into a small number of qubits might cause us to lose important information from the features.
+
+Focusing on classification models, there have been several approaches discussed to solve this issue, and we believe that classical pre-processing is key to enabling QML to work for big datasets in the foreseeable future. Most current approaches for classification problems utilizes Principal Component Analysis (PCA) for the dimensionality reduction problem, but rather than getting component axes that maximize variance, it makes more sense to maximize axes for class separation for classification problems, hence we investigate Linear Discriminany Analysis (LDA) as a dimensionality reduction technique for the classical preprocessing.
+
+![PCA LDA](img/pca_lda_explanation.JPG)
+
+## Investigation
+
+### Data Set
+
+We used a dataset from Kaggle related to fraud detection. The objective is to demonstrate the benefits of the preprocessing with LDA in conjunction with a variational classifier algorithm. This are the characteristics of the dataset:
+
+- 20,000+ records.
+- 112 features and a binary target variable.
+- All numerical (perfect for the purpose of the hackathon and time constraints).
+
+And it was taken from this link: https://www.kaggle.com/datasets/volodymyrgavrysh/fraud-detection-bank-dataset-20k-records-binary
+
+For the purposes of our experiments and proof-of-concepts (as well as time constraints), we first cleaned the dataset as we noticed that there were some duplicated features and some features that did not deviate at all, then we sampled 4000 from the data set and did a 70:30 split for training and test set. All in all, we were left with:
+
+- 4000 records
+- 97 features and a binary target variable.
+
+Note: To see how we perform the LDA and PCA, please look at [Solution via architecture.ipynb](./Solution via architecture.ipynb).
+
+### Performing the LDA
+
+Based on how many qubits we were planning to use, we needed to reduce the dimensionality of the features from 97 to N (supposing N was the number of qubits). For LDA, we needed to split the features into N groups. We did so via checking the correlations, and choosing the groups in such a way that features that were more strongly correlated with each other were in the same group, while features with less correlation where in separate groups. Beyond that, we then performed the dimensionlity reduction technique and proceeded to use the results in a simpled VQC tailored for binary classification. 
+
+With 2 qubits (hence reduced to two dimensions), and 5 entangling layers, we achieved the following results:
+
+![LDA results](img/lda_results.png)
+
+### Performing the PCA
+
+Since we stated at the outset that we were using LDA because it made more sense than PCA for encoding, we thought it prudent to show the results of performing PCA as a dimensionality reduction technique on the same dataset. Hence, we did the PCA on the data, and likewise used those results for encoding in a simpled VQC tailored for binary classification. 
+
+With 2 qubits, and 5 entangling layers (same VQC structure as above), we achieved the following results:
+
+![LDA results](img/pca_results.png)
+
+## Results and Discussion
+
+### Results
+
+We can compare the results of the metrics in the graph below:
+
+![PCA LDA comparison](img/comparison_results.png)
+
+With these results, we can see that using LDA for encoding surpasses PCA for VQC preprocessing encoding. Furthermore, we also looked at other classical ML approaches (not in this repo) like Linear Regression, SVM, and KNN, and the LDA encoding for VQC binary classification technique is better. It however does worse than techniques like Decision Tree and Random Forest, but the results are extremely encouraging considering that we have only used 2 qubits for 2 dimensions.
+
+### Future
+
+We have shown a proof-of-concept that using LDA for encoding surpasses PCA for VQC encoding for classification purposes, or binary classification purposes at least. There are several more things that we hope to test in the future, for example, the most pressing items are:
+
+- Extend this into multi-class classification techniques. This would be extremely useful and applicable to a wide range of problems
+- Further experimentation with different types of encoding and VQC structures. We picked one and stuck with it, but we hope to improve on it by looking at others
+- Academic studies. In this example, we have shown experimentally that LDA is superior, but an academic study into the mathematics and theory why would be enlightening.
+
+This is our pipeline of what we did:
+
+![code_architecture](img/code_architecture.JPG)
+
+And we basically hope to experiment with every single part more.
+
+## Business Application
+
+## What is in this repository?
+
+- [Solution via architecture.ipynb](./Solution via architecture.ipynb): This notebook contains the proof-of-concept and experiment for the LDA vs PCA, and basically runs through how we run the code.
+
+- [Factory](./Factory): This folder contains two files: data.py and model.py. These two files are the basis for the unified architecture to prepare the data and run the VQC model for both LDA and PCA.
+- [data.py](Factory/data.py): This file contains the class for preparing the data for both LDA and PCA within the same class PrepareData.
+- [model.py](Factory/model.py): This file contains the class for using LDA and PCA to run the VQC.
+
 

From f46ead487160c8acae2035b15100cdca724d2766 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:29:24 +0800
Subject: [PATCH 18/25] add pics

---
 QML4FRAUD/Solution via architecture.ipynb | 792 +---------------------
 QML4FRAUD/img/code_architecture.JPG       | Bin 0 -> 60280 bytes
 QML4FRAUD/img/comparison_results.png      | Bin 0 -> 109701 bytes
 QML4FRAUD/img/lda_results.png             | Bin 0 -> 34307 bytes
 QML4FRAUD/img/pca_lda_explanation.JPG     | Bin 0 -> 36350 bytes
 QML4FRAUD/img/pca_results.png             | Bin 0 -> 34340 bytes
 6 files changed, 8 insertions(+), 784 deletions(-)
 create mode 100644 QML4FRAUD/img/code_architecture.JPG
 create mode 100644 QML4FRAUD/img/comparison_results.png
 create mode 100644 QML4FRAUD/img/lda_results.png
 create mode 100644 QML4FRAUD/img/pca_lda_explanation.JPG
 create mode 100644 QML4FRAUD/img/pca_results.png

diff --git a/QML4FRAUD/Solution via architecture.ipynb b/QML4FRAUD/Solution via architecture.ipynb
index 6b49b30..6834cd2 100644
--- a/QML4FRAUD/Solution via architecture.ipynb	
+++ b/QML4FRAUD/Solution via architecture.ipynb	
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfb08b02",
+   "id": "4d2f7283",
    "metadata": {},
    "source": [
     "Load data in"
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "11f9921b",
+   "id": "1951105a",
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c900922",
+   "id": "b8554853",
    "metadata": {},
    "source": [
     "Clean data"
@@ -434,7 +434,7 @@
   {
    "cell_type": "code",
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-   "id": "e49bab17",
+   "id": "8f3cdf1e",
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@@ -746,7 +746,7 @@
   {
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-   "id": "aaa4013e",
+   "id": "f1d227b5",
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@@ -756,7 +756,7 @@
   {
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-   "id": "4fd51c43",
+   "id": "7ca4bee2",
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@@ -766,7 +766,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9c105230",
+   "id": "1e105ec9",
    "metadata": {},
    "outputs": [
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@@ -794,7 +794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffc13712",
+   "id": "919c9292",
    "metadata": {},
    "source": [
     "Prepare data"
@@ -1233,38 +1233,6 @@
     "Preprocess LDA"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d02cf3ec",
-   "metadata": {},
-   "outputs": [
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-     "data": {
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-     "execution_count": 10,
-     "metadata": {},
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-   "source": [
-    "data.x_train[\"col_69\"]"
-   ]
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   {
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    "execution_count": 11,
@@ -1876,750 +1844,6 @@
     "Preprocess PCA"
    ]
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-       "<p>2800 rows × 91 columns</p>\n",
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-       "[2800 rows x 91 columns]"
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-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
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-    "data.x_train"
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-   "cell_type": "code",
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-   "id": "a4a648d1",
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-       "9822      0.0  ...      0.0      1.0      0.0      0.0      0.0      1.0   \n",
-       "19204     1.0  ...      0.0      1.0      0.0      0.0      0.0      1.0   \n",
-       "12449     0.0  ...      0.0      0.0      0.0      0.0      0.0      0.0   \n",
-       "16268     0.0  ...      0.0      0.0      0.0      0.0      0.0      1.0   \n",
-       "8975      0.0  ...      0.0      0.0      0.0      0.0      0.0      1.0   \n",
-       "\n",
-       "       col_107  col_109  col_110  col_111  \n",
-       "4181       0.0      0.0      0.0      0.0  \n",
-       "7592       0.0      0.0      0.0      0.0  \n",
-       "33         0.0      0.0      1.0    181.0  \n",
-       "18507      1.0      0.0      0.0     22.0  \n",
-       "9106       0.0      0.0      0.0      7.0  \n",
-       "...        ...      ...      ...      ...  \n",
-       "9822       0.0      0.0      0.0     20.0  \n",
-       "19204      1.0      0.0      0.0     25.0  \n",
-       "12449      0.0      0.0      0.0      0.0  \n",
-       "16268      1.0      1.0      0.0    154.0  \n",
-       "8975       0.0      0.0      0.0      6.0  \n",
-       "\n",
-       "[1200 rows x 91 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.x_test"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
diff --git a/QML4FRAUD/img/code_architecture.JPG b/QML4FRAUD/img/code_architecture.JPG
new file mode 100644
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HcmV?d00001


From 5b93afddd2a3192389b9e57f79034ef416f424a0 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:31:06 +0800
Subject: [PATCH 19/25] put to backup

---
 QML4FRAUD/{ => testing}/3 qubit test.ipynb          | 0
 QML4FRAUD/{ => testing}/Architecture.ipynb          | 0
 QML4FRAUD/{ => testing}/EDA.ipynb                   | 0
 QML4FRAUD/{ => testing}/Pennylane_LDA_CDL_QML.ipynb | 0
 QML4FRAUD/{ => testing}/Pennylane_PCA_CDL_QML.ipynb | 0
 5 files changed, 0 insertions(+), 0 deletions(-)
 rename QML4FRAUD/{ => testing}/3 qubit test.ipynb (100%)
 rename QML4FRAUD/{ => testing}/Architecture.ipynb (100%)
 rename QML4FRAUD/{ => testing}/EDA.ipynb (100%)
 rename QML4FRAUD/{ => testing}/Pennylane_LDA_CDL_QML.ipynb (100%)
 rename QML4FRAUD/{ => testing}/Pennylane_PCA_CDL_QML.ipynb (100%)

diff --git a/QML4FRAUD/3 qubit test.ipynb b/QML4FRAUD/testing/3 qubit test.ipynb
similarity index 100%
rename from QML4FRAUD/3 qubit test.ipynb
rename to QML4FRAUD/testing/3 qubit test.ipynb
diff --git a/QML4FRAUD/Architecture.ipynb b/QML4FRAUD/testing/Architecture.ipynb
similarity index 100%
rename from QML4FRAUD/Architecture.ipynb
rename to QML4FRAUD/testing/Architecture.ipynb
diff --git a/QML4FRAUD/EDA.ipynb b/QML4FRAUD/testing/EDA.ipynb
similarity index 100%
rename from QML4FRAUD/EDA.ipynb
rename to QML4FRAUD/testing/EDA.ipynb
diff --git a/QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb b/QML4FRAUD/testing/Pennylane_LDA_CDL_QML.ipynb
similarity index 100%
rename from QML4FRAUD/Pennylane_LDA_CDL_QML.ipynb
rename to QML4FRAUD/testing/Pennylane_LDA_CDL_QML.ipynb
diff --git a/QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb b/QML4FRAUD/testing/Pennylane_PCA_CDL_QML.ipynb
similarity index 100%
rename from QML4FRAUD/Pennylane_PCA_CDL_QML.ipynb
rename to QML4FRAUD/testing/Pennylane_PCA_CDL_QML.ipynb

From aba1a5ea5c628d107c9ca99eb5529531654ed373 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:31:46 +0800
Subject: [PATCH 20/25] Update README.md

---
 QML4FRAUD/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index e2990de..2250b6d 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -1,4 +1,4 @@
-#Hackathon 2022: QML4FRAUD
+# Hackathon 2022: QML4FRAUD
 
 This project is QML4FRAUD's submission for CDL Quantum Bootcamp Hackathon 2022, and will guide you through our implementation of using LDA to improve the encoding process of Variation Quantum Circuits in Quantum Machine Learning.
 
@@ -27,7 +27,7 @@ For the purposes of our experiments and proof-of-concepts (as well as time const
 - 4000 records
 - 97 features and a binary target variable.
 
-Note: To see how we perform the LDA and PCA, please look at [Solution via architecture.ipynb](./Solution via architecture.ipynb).
+Note: To see how we perform the LDA and PCA, please look at [Solution via architecture.ipynb](./Solution%20via%20architecture.ipynb).
 
 ### Performing the LDA
 
@@ -73,7 +73,7 @@ And we basically hope to experiment with every single part more.
 
 ## What is in this repository?
 
-- [Solution via architecture.ipynb](./Solution via architecture.ipynb): This notebook contains the proof-of-concept and experiment for the LDA vs PCA, and basically runs through how we run the code.
+- [Solution via architecture.ipynb](./Solution%20via%20architecture.ipynb): This notebook contains the proof-of-concept and experiment for the LDA vs PCA, and basically runs through how we run the code.
 
 - [Factory](./Factory): This folder contains two files: data.py and model.py. These two files are the basis for the unified architecture to prepare the data and run the VQC model for both LDA and PCA.
 - [data.py](Factory/data.py): This file contains the class for preparing the data for both LDA and PCA within the same class PrepareData.

From b370a5b599cb36337d2447b1c9efbd88ca96560b Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:32:39 +0800
Subject: [PATCH 21/25] Update README.md

---
 QML4FRAUD/README.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index 2250b6d..c9324eb 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -71,6 +71,27 @@ And we basically hope to experiment with every single part more.
 
 ## Business Application
 
+Machine learning is ubiquitous in many of the best solutions to industrial problems. Quantum computing is proposed to enhance machine learning. We have used the case of fraud detection as a demonstration to show that using LDA as a preprocessing method with a variational classifier could enhance the QML model's performance and allow it to process a lot more features. And we believe that this could be further improved in the future. 
+
+Since this is an improvement based on existing machine learning models, it has direct application in real cases. In principle, this could eventually be generalized for any multi-class classification problem. 
+
+We are seeing that the finance sector is always within the top 3 industries related to quantum computing use cases and benefits. Example problems that our approach could be applied to include: 
+
+- Credit scoring
+- Fraud detection
+- Default forecasting
+- Churn prediction
+- Loan approval
+
+and many more. 
+
+In other industries, improvement of classification also has many benefits. A quick but not complete list would include:
+
+- Energy: energy loss detection.
+- E-commerce: recommendation system.
+- Several industries: forecasting.
+- Healthcare: diagnosis.
+
 ## What is in this repository?
 
 - [Solution via architecture.ipynb](./Solution%20via%20architecture.ipynb): This notebook contains the proof-of-concept and experiment for the LDA vs PCA, and basically runs through how we run the code.

From 49be3f9d8096bc1fb94fd26ee5184dc3005cb6f4 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+he11fire2@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:36:45 +0800
Subject: [PATCH 22/25] update pic

---
 QML4FRAUD/img/comparison_results.png | Bin 109701 -> 0 bytes
 QML4FRAUD/img/pca_lda_comparison.JPG | Bin 0 -> 62615 bytes
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 delete mode 100644 QML4FRAUD/img/comparison_results.png
 create mode 100644 QML4FRAUD/img/pca_lda_comparison.JPG

diff --git a/QML4FRAUD/img/comparison_results.png b/QML4FRAUD/img/comparison_results.png
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From 5f9f1fac2d213252a6b348ee23a318d874f09c26 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:37:50 +0800
Subject: [PATCH 23/25] Update README.md

---
 QML4FRAUD/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index c9324eb..feac140 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -51,7 +51,7 @@ With 2 qubits, and 5 entangling layers (same VQC structure as above), we achieve
 
 We can compare the results of the metrics in the graph below:
 
-![PCA LDA comparison](img/comparison_results.png)
+![PCA LDA comparison](img/pca_lda_comparison.JPG)
 
 With these results, we can see that using LDA for encoding surpasses PCA for VQC preprocessing encoding. Furthermore, we also looked at other classical ML approaches (not in this repo) like Linear Regression, SVM, and KNN, and the LDA encoding for VQC binary classification technique is better. It however does worse than techniques like Decision Tree and Random Forest, but the results are extremely encouraging considering that we have only used 2 qubits for 2 dimensions.
 

From a77f75331bdeda15699e031d7f9c9fe6a17e1860 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:39:07 +0800
Subject: [PATCH 24/25] Update README.md

---
 QML4FRAUD/README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index feac140..5b52e97 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -2,6 +2,8 @@
 
 This project is QML4FRAUD's submission for CDL Quantum Bootcamp Hackathon 2022, and will guide you through our implementation of using LDA to improve the encoding process of Variation Quantum Circuits in Quantum Machine Learning.
 
+Team Members: Yaqi Han, Jonas Tan, Mert Esencan, Javier Mancilla
+
 ## Our project: Novel approach about classical data preprocessing for classification problems using QML
 
 Quantum Machine Learning (QML) involves the integration of quantum algorithms into machine learning techniques, and has the potential to be one of the more versatile uses of quantum computing, simply because of how far-reaching machine learning is. Furthermore, many QML techniques are robust and are touted to be able to be used in the NISQ-era. However, one of the main problems of implementing quantum algorithms into machine learning is the problem of encoding, which is basically how to encode classical data for hybrid quantum algorithms (in our case VQC). There has been many studies done with interesting results, but those usually have a small number of data pooints, and more importantly, a small number of features. This is due to the fact that current (and for the foreseeable NISQ-era) quantum computers and simulators have a small number of qubits that we can work with, and so encoding a large number of features into a small number of qubits might cause us to lose important information from the features.

From 04a22325903638df9af02c6cb6752d6d510c6061 Mon Sep 17 00:00:00 2001
From: Jonas Tan <50507439+JonasTYW@users.noreply.github.com>
Date: Wed, 27 Jul 2022 21:39:20 +0800
Subject: [PATCH 25/25] Update README.md

---
 QML4FRAUD/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/QML4FRAUD/README.md b/QML4FRAUD/README.md
index 5b52e97..ba2a70b 100644
--- a/QML4FRAUD/README.md
+++ b/QML4FRAUD/README.md
@@ -2,7 +2,7 @@
 
 This project is QML4FRAUD's submission for CDL Quantum Bootcamp Hackathon 2022, and will guide you through our implementation of using LDA to improve the encoding process of Variation Quantum Circuits in Quantum Machine Learning.
 
-Team Members: Yaqi Han, Jonas Tan, Mert Esencan, Javier Mancilla
+Team 9 Members: Yaqi Han, Jonas Tan, Mert Esencan, Javier Mancilla
 
 ## Our project: Novel approach about classical data preprocessing for classification problems using QML