Epilepsy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cZ5tuLfDCT0Q"
   },
   "outputs": [],
   "source": [
    "#importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import ConfusionMatrixDisplay,confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "executionInfo": {
     "elapsed": 428,
     "status": "ok",
     "timestamp": 1689602553163,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "vv5y9CQQClkn",
    "outputId": "0e0e58d2-e9ec-454d-e1fe-a122eea90d99"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_1</th>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>0.426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_2</th>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_3</th>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_4</th>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>0.517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_5</th>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "name                                                                      \n",
       "phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "                MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "name                                                               \n",
       "phon_R01_S01_1           0.00007   0.00370   0.00554     0.01109   \n",
       "phon_R01_S01_2           0.00008   0.00465   0.00696     0.01394   \n",
       "phon_R01_S01_3           0.00009   0.00544   0.00781     0.01633   \n",
       "phon_R01_S01_4           0.00009   0.00502   0.00698     0.01505   \n",
       "phon_R01_S01_5           0.00011   0.00655   0.00908     0.01966   \n",
       "\n",
       "                MDVP:Shimmer  MDVP:Shimmer(dB)  ...  Shimmer:DDA      NHR  \\\n",
       "name                                            ...                         \n",
       "phon_R01_S01_1       0.04374             0.426  ...      0.06545  0.02211   \n",
       "phon_R01_S01_2       0.06134             0.626  ...      0.09403  0.01929   \n",
       "phon_R01_S01_3       0.05233             0.482  ...      0.08270  0.01309   \n",
       "phon_R01_S01_4       0.05492             0.517  ...      0.08771  0.01353   \n",
       "phon_R01_S01_5       0.06425             0.584  ...      0.10470  0.01767   \n",
       "\n",
       "                   HNR  status      RPDE       DFA   spread1   spread2  \\\n",
       "name                                                                     \n",
       "phon_R01_S01_1  21.033       1  0.414783  0.815285 -4.813031  0.266482   \n",
       "phon_R01_S01_2  19.085       1  0.458359  0.819521 -4.075192  0.335590   \n",
       "phon_R01_S01_3  20.651       1  0.429895  0.825288 -4.443179  0.311173   \n",
       "phon_R01_S01_4  20.644       1  0.434969  0.819235 -4.117501  0.334147   \n",
       "phon_R01_S01_5  19.649       1  0.417356  0.823484 -3.747787  0.234513   \n",
       "\n",
       "                      D2       PPE  \n",
       "name                                \n",
       "phon_R01_S01_1  2.301442  0.284654  \n",
       "phon_R01_S01_2  2.486855  0.368674  \n",
       "phon_R01_S01_3  2.342259  0.332634  \n",
       "phon_R01_S01_4  2.405554  0.368975  \n",
       "phon_R01_S01_5  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('epilepsy.csv')\n",
    "df.set_index('name',inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1689499985681,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "OE40YvcjC9u7",
    "outputId": "0f176230-8f7e-4a0f-fb2c-204251669362"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 23)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 195 entries, phon_R01_S01_1 to phon_R01_S50_6\n",
      "Data columns (total 23 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 1   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 2   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 3   MDVP:Jitter(%)    195 non-null    float64\n",
      " 4   MDVP:Jitter(Abs)  195 non-null    float64\n",
      " 5   MDVP:RAP          195 non-null    float64\n",
      " 6   MDVP:PPQ          193 non-null    float64\n",
      " 7   Jitter:DDP        194 non-null    float64\n",
      " 8   MDVP:Shimmer      195 non-null    float64\n",
      " 9   MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 10  Shimmer:APQ3      195 non-null    float64\n",
      " 11  Shimmer:APQ5      195 non-null    float64\n",
      " 12  MDVP:APQ          195 non-null    float64\n",
      " 13  Shimmer:DDA       195 non-null    float64\n",
      " 14  NHR               195 non-null    float64\n",
      " 15  HNR               195 non-null    float64\n",
      " 16  status            195 non-null    int64  \n",
      " 17  RPDE              195 non-null    float64\n",
      " 18  DFA               195 non-null    float64\n",
      " 19  spread1           195 non-null    float64\n",
      " 20  spread2           195 non-null    float64\n",
      " 21  D2                195 non-null    float64\n",
      " 22  PPE               195 non-null    float64\n",
      "dtypes: float64(22), int64(1)\n",
      "memory usage: 36.6+ KB\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 393
    },
    "executionInfo": {
     "elapsed": 422,
     "status": "ok",
     "timestamp": 1689602559657,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "vI2E4BAbDBzg",
    "outputId": "72cd81a6-6995-4f3a-bf61-e4c138063674"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.228641</td>\n",
       "      <td>197.104918</td>\n",
       "      <td>116.324631</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.009932</td>\n",
       "      <td>0.029709</td>\n",
       "      <td>0.282251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>21.885974</td>\n",
       "      <td>0.753846</td>\n",
       "      <td>0.498536</td>\n",
       "      <td>0.718099</td>\n",
       "      <td>-5.684397</td>\n",
       "      <td>0.226510</td>\n",
       "      <td>2.381826</td>\n",
       "      <td>0.206552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.390065</td>\n",
       "      <td>91.491548</td>\n",
       "      <td>43.521413</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>0.002765</td>\n",
       "      <td>0.008925</td>\n",
       "      <td>0.018857</td>\n",
       "      <td>0.194877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030459</td>\n",
       "      <td>0.040418</td>\n",
       "      <td>4.425764</td>\n",
       "      <td>0.431878</td>\n",
       "      <td>0.103942</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>1.090208</td>\n",
       "      <td>0.083406</td>\n",
       "      <td>0.382799</td>\n",
       "      <td>0.090119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>88.333000</td>\n",
       "      <td>102.145000</td>\n",
       "      <td>65.476000</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.009540</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>8.441000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>0.574282</td>\n",
       "      <td>-7.964984</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>1.423287</td>\n",
       "      <td>0.044539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>117.572000</td>\n",
       "      <td>134.862500</td>\n",
       "      <td>84.291000</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.004982</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.148500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024735</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>19.198000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.421306</td>\n",
       "      <td>0.674758</td>\n",
       "      <td>-6.450096</td>\n",
       "      <td>0.174351</td>\n",
       "      <td>2.099125</td>\n",
       "      <td>0.137451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.790000</td>\n",
       "      <td>175.829000</td>\n",
       "      <td>104.315000</td>\n",
       "      <td>0.004940</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.007455</td>\n",
       "      <td>0.022970</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038360</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>22.085000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495954</td>\n",
       "      <td>0.722254</td>\n",
       "      <td>-5.720868</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>2.361532</td>\n",
       "      <td>0.194052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>182.769000</td>\n",
       "      <td>224.205500</td>\n",
       "      <td>140.018500</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.003960</td>\n",
       "      <td>0.011558</td>\n",
       "      <td>0.037885</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060795</td>\n",
       "      <td>0.025640</td>\n",
       "      <td>25.075500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587562</td>\n",
       "      <td>0.761881</td>\n",
       "      <td>-5.046192</td>\n",
       "      <td>0.279234</td>\n",
       "      <td>2.636456</td>\n",
       "      <td>0.252980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.105000</td>\n",
       "      <td>592.030000</td>\n",
       "      <td>239.170000</td>\n",
       "      <td>0.033160</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.019580</td>\n",
       "      <td>0.064330</td>\n",
       "      <td>0.119080</td>\n",
       "      <td>1.302000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169420</td>\n",
       "      <td>0.314820</td>\n",
       "      <td>33.047000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.685151</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-2.434031</td>\n",
       "      <td>0.450493</td>\n",
       "      <td>3.671155</td>\n",
       "      <td>0.527367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "count   195.000000    195.000000    195.000000      195.000000   \n",
       "mean    154.228641    197.104918    116.324631        0.006220   \n",
       "std      41.390065     91.491548     43.521413        0.004848   \n",
       "min      88.333000    102.145000     65.476000        0.001680   \n",
       "25%     117.572000    134.862500     84.291000        0.003460   \n",
       "50%     148.790000    175.829000    104.315000        0.004940   \n",
       "75%     182.769000    224.205500    140.018500        0.007365   \n",
       "max     260.105000    592.030000    239.170000        0.033160   \n",
       "\n",
       "       MDVP:Jitter(Abs)    MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
       "count        195.000000  195.000000  193.000000  194.000000    195.000000   \n",
       "mean           0.000044    0.003306    0.003468    0.009932      0.029709   \n",
       "std            0.000035    0.002968    0.002765    0.008925      0.018857   \n",
       "min            0.000007    0.000680    0.000920    0.002040      0.009540   \n",
       "25%            0.000020    0.001660    0.001880    0.004982      0.016505   \n",
       "50%            0.000030    0.002500    0.002700    0.007455      0.022970   \n",
       "75%            0.000060    0.003835    0.003960    0.011558      0.037885   \n",
       "max            0.000260    0.021440    0.019580    0.064330      0.119080   \n",
       "\n",
       "       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \\\n",
       "count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   \n",
       "mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   \n",
       "std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   \n",
       "min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   \n",
       "25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   \n",
       "50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   \n",
       "75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   \n",
       "max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   \n",
       "\n",
       "             RPDE         DFA     spread1     spread2          D2         PPE  \n",
       "count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
       "mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
       "std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n",
       "min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
       "25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
       "50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
       "75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
       "max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean and median are aroung the same range.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 659,
     "status": "ok",
     "timestamp": 1689500014523,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "cHsWA8q9DEru",
    "outputId": "c04a95d3-e3aa-4075-a35f-e856edc08feb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MDVP:Fo(Hz)         0.000000\n",
       "MDVP:Fhi(Hz)        0.000000\n",
       "MDVP:Flo(Hz)        0.000000\n",
       "MDVP:Jitter(%)      0.000000\n",
       "MDVP:Jitter(Abs)    0.000000\n",
       "MDVP:RAP            0.000000\n",
       "MDVP:PPQ            0.010256\n",
       "Jitter:DDP          0.005128\n",
       "MDVP:Shimmer        0.000000\n",
       "MDVP:Shimmer(dB)    0.000000\n",
       "Shimmer:APQ3        0.000000\n",
       "Shimmer:APQ5        0.000000\n",
       "MDVP:APQ            0.000000\n",
       "Shimmer:DDA         0.000000\n",
       "NHR                 0.000000\n",
       "HNR                 0.000000\n",
       "status              0.000000\n",
       "RPDE                0.000000\n",
       "DFA                 0.000000\n",
       "spread1             0.000000\n",
       "spread2             0.000000\n",
       "D2                  0.000000\n",
       "PPE                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kc4Vrbs3DHeG"
   },
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 832
    },
    "executionInfo": {
     "elapsed": 715,
     "status": "ok",
     "timestamp": 1689500033298,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "H-ltPwmPDIPO",
    "outputId": "11f74f80-5aef-4685-f97c-27693658f009"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "  <div id=\"df-121bb377-02d5-4e65-808b-b31b4040cb98\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400327</td>\n",
       "      <td>0.602803</td>\n",
       "      <td>-0.106765</td>\n",
       "      <td>-0.373000</td>\n",
       "      <td>-0.066314</td>\n",
       "      <td>-0.101633</td>\n",
       "      <td>-0.066332</td>\n",
       "      <td>-0.087435</td>\n",
       "      <td>-0.062556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066798</td>\n",
       "      <td>-0.083763</td>\n",
       "      <td>-0.013346</td>\n",
       "      <td>0.041347</td>\n",
       "      <td>-0.371439</td>\n",
       "      <td>-0.448063</td>\n",
       "      <td>-0.400615</td>\n",
       "      <td>-0.228277</td>\n",
       "      <td>0.201440</td>\n",
       "      <td>-0.359729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <td>0.400327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084839</td>\n",
       "      <td>0.105580</td>\n",
       "      <td>-0.025727</td>\n",
       "      <td>0.100042</td>\n",
       "      <td>0.094352</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.046498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007741</td>\n",
       "      <td>-0.000869</td>\n",
       "      <td>0.166263</td>\n",
       "      <td>-0.029451</td>\n",
       "      <td>-0.108345</td>\n",
       "      <td>-0.342681</td>\n",
       "      <td>-0.072252</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.182949</td>\n",
       "      <td>-0.065420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <td>0.602803</td>\n",
       "      <td>0.084839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136072</td>\n",
       "      <td>-0.275528</td>\n",
       "      <td>-0.096635</td>\n",
       "      <td>-0.092359</td>\n",
       "      <td>-0.096603</td>\n",
       "      <td>-0.141103</td>\n",
       "      <td>-0.115316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102637</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>-0.106473</td>\n",
       "      <td>0.201967</td>\n",
       "      <td>-0.410294</td>\n",
       "      <td>-0.054428</td>\n",
       "      <td>-0.394259</td>\n",
       "      <td>-0.246411</td>\n",
       "      <td>-0.079596</td>\n",
       "      <td>-0.339180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <td>-0.106765</td>\n",
       "      <td>0.105580</td>\n",
       "      <td>-0.136072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935258</td>\n",
       "      <td>0.990308</td>\n",
       "      <td>0.974104</td>\n",
       "      <td>0.990309</td>\n",
       "      <td>0.767399</td>\n",
       "      <td>0.802896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756514</td>\n",
       "      <td>0.744918</td>\n",
       "      <td>0.906811</td>\n",
       "      <td>-0.727884</td>\n",
       "      <td>0.355399</td>\n",
       "      <td>0.098116</td>\n",
       "      <td>0.690928</td>\n",
       "      <td>0.380236</td>\n",
       "      <td>0.429830</td>\n",
       "      <td>0.719109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <td>-0.373000</td>\n",
       "      <td>-0.025727</td>\n",
       "      <td>-0.275528</td>\n",
       "      <td>0.935258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.922751</td>\n",
       "      <td>0.897142</td>\n",
       "      <td>0.922752</td>\n",
       "      <td>0.700846</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.645927</td>\n",
       "      <td>0.694730</td>\n",
       "      <td>0.834742</td>\n",
       "      <td>-0.654049</td>\n",
       "      <td>0.436213</td>\n",
       "      <td>0.174904</td>\n",
       "      <td>0.732451</td>\n",
       "      <td>0.381336</td>\n",
       "      <td>0.303092</td>\n",
       "      <td>0.744991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <td>-0.066314</td>\n",
       "      <td>0.100042</td>\n",
       "      <td>-0.096635</td>\n",
       "      <td>0.990308</td>\n",
       "      <td>0.922751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758212</td>\n",
       "      <td>0.789464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735876</td>\n",
       "      <td>0.743606</td>\n",
       "      <td>0.919274</td>\n",
       "      <td>-0.722339</td>\n",
       "      <td>0.338044</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>0.646316</td>\n",
       "      <td>0.320029</td>\n",
       "      <td>0.424047</td>\n",
       "      <td>0.669057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <td>-0.101633</td>\n",
       "      <td>0.094352</td>\n",
       "      <td>-0.092359</td>\n",
       "      <td>0.974104</td>\n",
       "      <td>0.897142</td>\n",
       "      <td>0.957121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957123</td>\n",
       "      <td>0.796449</td>\n",
       "      <td>0.838157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.762046</td>\n",
       "      <td>0.844012</td>\n",
       "      <td>-0.731760</td>\n",
       "      <td>0.327855</td>\n",
       "      <td>0.196297</td>\n",
       "      <td>0.714687</td>\n",
       "      <td>0.404053</td>\n",
       "      <td>0.409698</td>\n",
       "      <td>0.768204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <td>-0.066332</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>-0.096603</td>\n",
       "      <td>0.990309</td>\n",
       "      <td>0.922752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758186</td>\n",
       "      <td>0.789433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735859</td>\n",
       "      <td>0.743587</td>\n",
       "      <td>0.919301</td>\n",
       "      <td>-0.722288</td>\n",
       "      <td>0.337982</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>0.646315</td>\n",
       "      <td>0.319997</td>\n",
       "      <td>0.423996</td>\n",
       "      <td>0.669063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <td>-0.087435</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>-0.141103</td>\n",
       "      <td>0.767399</td>\n",
       "      <td>0.700846</td>\n",
       "      <td>0.758212</td>\n",
       "      <td>0.796449</td>\n",
       "      <td>0.758186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.949841</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.720967</td>\n",
       "      <td>-0.835952</td>\n",
       "      <td>0.444032</td>\n",
       "      <td>0.160499</td>\n",
       "      <td>0.651883</td>\n",
       "      <td>0.450566</td>\n",
       "      <td>0.505347</td>\n",
       "      <td>0.690998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <td>-0.062556</td>\n",
       "      <td>0.046498</td>\n",
       "      <td>-0.115316</td>\n",
       "      <td>0.802896</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.789464</td>\n",
       "      <td>0.838157</td>\n",
       "      <td>0.789433</td>\n",
       "      <td>0.987164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960779</td>\n",
       "      <td>0.962987</td>\n",
       "      <td>0.743362</td>\n",
       "      <td>-0.828633</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>0.165608</td>\n",
       "      <td>0.649745</td>\n",
       "      <td>0.452798</td>\n",
       "      <td>0.510674</td>\n",
       "      <td>0.692418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <td>-0.083748</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>-0.148028</td>\n",
       "      <td>0.744907</td>\n",
       "      <td>0.694713</td>\n",
       "      <td>0.743598</td>\n",
       "      <td>0.762033</td>\n",
       "      <td>0.743579</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.962983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715017</td>\n",
       "      <td>-0.827330</td>\n",
       "      <td>0.431596</td>\n",
       "      <td>0.151840</td>\n",
       "      <td>0.607574</td>\n",
       "      <td>0.400194</td>\n",
       "      <td>0.465317</td>\n",
       "      <td>0.641892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <td>-0.061097</td>\n",
       "      <td>-0.007597</td>\n",
       "      <td>-0.097769</td>\n",
       "      <td>0.723923</td>\n",
       "      <td>0.646612</td>\n",
       "      <td>0.708445</td>\n",
       "      <td>0.785559</td>\n",
       "      <td>0.708424</td>\n",
       "      <td>0.982786</td>\n",
       "      <td>0.973634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948999</td>\n",
       "      <td>0.959891</td>\n",
       "      <td>0.656570</td>\n",
       "      <td>-0.814920</td>\n",
       "      <td>0.396942</td>\n",
       "      <td>0.214747</td>\n",
       "      <td>0.645012</td>\n",
       "      <td>0.457585</td>\n",
       "      <td>0.501556</td>\n",
       "      <td>0.700765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <td>-0.066798</td>\n",
       "      <td>0.007741</td>\n",
       "      <td>-0.102637</td>\n",
       "      <td>0.756514</td>\n",
       "      <td>0.645927</td>\n",
       "      <td>0.735876</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.735859</td>\n",
       "      <td>0.949841</td>\n",
       "      <td>0.960779</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896181</td>\n",
       "      <td>0.692664</td>\n",
       "      <td>-0.801422</td>\n",
       "      <td>0.448348</td>\n",
       "      <td>0.157430</td>\n",
       "      <td>0.670719</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>0.535212</td>\n",
       "      <td>0.719671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <td>-0.083763</td>\n",
       "      <td>-0.000869</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>0.744918</td>\n",
       "      <td>0.694730</td>\n",
       "      <td>0.743606</td>\n",
       "      <td>0.762046</td>\n",
       "      <td>0.743587</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.962987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715025</td>\n",
       "      <td>-0.827334</td>\n",
       "      <td>0.431593</td>\n",
       "      <td>0.151850</td>\n",
       "      <td>0.607578</td>\n",
       "      <td>0.400178</td>\n",
       "      <td>0.465312</td>\n",
       "      <td>0.641904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NHR</th>\n",
       "      <td>-0.013346</td>\n",
       "      <td>0.166263</td>\n",
       "      <td>-0.106473</td>\n",
       "      <td>0.906811</td>\n",
       "      <td>0.834742</td>\n",
       "      <td>0.919274</td>\n",
       "      <td>0.844012</td>\n",
       "      <td>0.919301</td>\n",
       "      <td>0.720967</td>\n",
       "      <td>0.743362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692664</td>\n",
       "      <td>0.715025</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.715837</td>\n",
       "      <td>0.368259</td>\n",
       "      <td>-0.132850</td>\n",
       "      <td>0.538909</td>\n",
       "      <td>0.315831</td>\n",
       "      <td>0.471325</td>\n",
       "      <td>0.550427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNR</th>\n",
       "      <td>0.041347</td>\n",
       "      <td>-0.029451</td>\n",
       "      <td>0.201967</td>\n",
       "      <td>-0.727884</td>\n",
       "      <td>-0.654049</td>\n",
       "      <td>-0.722339</td>\n",
       "      <td>-0.731760</td>\n",
       "      <td>-0.722288</td>\n",
       "      <td>-0.835952</td>\n",
       "      <td>-0.828633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.801422</td>\n",
       "      <td>-0.827334</td>\n",
       "      <td>-0.715837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.600515</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>-0.669196</td>\n",
       "      <td>-0.430786</td>\n",
       "      <td>-0.595321</td>\n",
       "      <td>-0.688676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPDE</th>\n",
       "      <td>-0.371439</td>\n",
       "      <td>-0.108345</td>\n",
       "      <td>-0.410294</td>\n",
       "      <td>0.355399</td>\n",
       "      <td>0.436213</td>\n",
       "      <td>0.338044</td>\n",
       "      <td>0.327855</td>\n",
       "      <td>0.337982</td>\n",
       "      <td>0.444032</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448348</td>\n",
       "      <td>0.431593</td>\n",
       "      <td>0.368259</td>\n",
       "      <td>-0.600515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117896</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.467334</td>\n",
       "      <td>0.234777</td>\n",
       "      <td>0.539678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFA</th>\n",
       "      <td>-0.448063</td>\n",
       "      <td>-0.342681</td>\n",
       "      <td>-0.054428</td>\n",
       "      <td>0.098116</td>\n",
       "      <td>0.174904</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>0.196297</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>0.160499</td>\n",
       "      <td>0.165608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157430</td>\n",
       "      <td>0.151850</td>\n",
       "      <td>-0.132850</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>-0.117896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195518</td>\n",
       "      <td>0.162144</td>\n",
       "      <td>-0.166319</td>\n",
       "      <td>0.271564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread1</th>\n",
       "      <td>-0.400615</td>\n",
       "      <td>-0.072252</td>\n",
       "      <td>-0.394259</td>\n",
       "      <td>0.690928</td>\n",
       "      <td>0.732451</td>\n",
       "      <td>0.646316</td>\n",
       "      <td>0.714687</td>\n",
       "      <td>0.646315</td>\n",
       "      <td>0.651883</td>\n",
       "      <td>0.649745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670719</td>\n",
       "      <td>0.607578</td>\n",
       "      <td>0.538909</td>\n",
       "      <td>-0.669196</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.195518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.646920</td>\n",
       "      <td>0.488723</td>\n",
       "      <td>0.961909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread2</th>\n",
       "      <td>-0.228277</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>-0.246411</td>\n",
       "      <td>0.380236</td>\n",
       "      <td>0.381336</td>\n",
       "      <td>0.320029</td>\n",
       "      <td>0.404053</td>\n",
       "      <td>0.319997</td>\n",
       "      <td>0.450566</td>\n",
       "      <td>0.452798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>0.400178</td>\n",
       "      <td>0.315831</td>\n",
       "      <td>-0.430786</td>\n",
       "      <td>0.467334</td>\n",
       "      <td>0.162144</td>\n",
       "      <td>0.646920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524845</td>\n",
       "      <td>0.641415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.201440</td>\n",
       "      <td>0.182949</td>\n",
       "      <td>-0.079596</td>\n",
       "      <td>0.429830</td>\n",
       "      <td>0.303092</td>\n",
       "      <td>0.424047</td>\n",
       "      <td>0.409698</td>\n",
       "      <td>0.423996</td>\n",
       "      <td>0.505347</td>\n",
       "      <td>0.510674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535212</td>\n",
       "      <td>0.465312</td>\n",
       "      <td>0.471325</td>\n",
       "      <td>-0.595321</td>\n",
       "      <td>0.234777</td>\n",
       "      <td>-0.166319</td>\n",
       "      <td>0.488723</td>\n",
       "      <td>0.524845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.475093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPE</th>\n",
       "      <td>-0.359729</td>\n",
       "      <td>-0.065420</td>\n",
       "      <td>-0.339180</td>\n",
       "      <td>0.719109</td>\n",
       "      <td>0.744991</td>\n",
       "      <td>0.669057</td>\n",
       "      <td>0.768204</td>\n",
       "      <td>0.669063</td>\n",
       "      <td>0.690998</td>\n",
       "      <td>0.692418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.719671</td>\n",
       "      <td>0.641904</td>\n",
       "      <td>0.550427</td>\n",
       "      <td>-0.688676</td>\n",
       "      <td>0.539678</td>\n",
       "      <td>0.271564</td>\n",
       "      <td>0.961909</td>\n",
       "      <td>0.641415</td>\n",
       "      <td>0.475093</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 22 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-121bb377-02d5-4e65-808b-b31b4040cb98')\"\n",
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       "    fill: #1967D2;\n",
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       "  .colab-df-quickchart:hover {\n",
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       "\n",
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       "        const containerElement = document.querySelector('#' + key);\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
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       "\n",
       "      <script>\n",
       "\n",
       "function displayQuickchartButton(domScope) {\n",
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       "        displayQuickchartButton(document);\n",
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       "    .colab-df-convert:hover {\n",
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       "    [theme=dark] .colab-df-convert {\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-121bb377-02d5-4e65-808b-b31b4040cb98');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "MDVP:Fo(Hz)          1.000000      0.400327      0.602803       -0.106765   \n",
       "MDVP:Fhi(Hz)         0.400327      1.000000      0.084839        0.105580   \n",
       "MDVP:Flo(Hz)         0.602803      0.084839      1.000000       -0.136072   \n",
       "MDVP:Jitter(%)      -0.106765      0.105580     -0.136072        1.000000   \n",
       "MDVP:Jitter(Abs)    -0.373000     -0.025727     -0.275528        0.935258   \n",
       "MDVP:RAP            -0.066314      0.100042     -0.096635        0.990308   \n",
       "MDVP:PPQ            -0.101633      0.094352     -0.092359        0.974104   \n",
       "Jitter:DDP          -0.066332      0.100015     -0.096603        0.990309   \n",
       "MDVP:Shimmer        -0.087435      0.005164     -0.141103        0.767399   \n",
       "MDVP:Shimmer(dB)    -0.062556      0.046498     -0.115316        0.802896   \n",
       "Shimmer:APQ3        -0.083748     -0.000879     -0.148028        0.744907   \n",
       "Shimmer:APQ5        -0.061097     -0.007597     -0.097769        0.723923   \n",
       "MDVP:APQ            -0.066798      0.007741     -0.102637        0.756514   \n",
       "Shimmer:DDA         -0.083763     -0.000869     -0.148017        0.744918   \n",
       "NHR                 -0.013346      0.166263     -0.106473        0.906811   \n",
       "HNR                  0.041347     -0.029451      0.201967       -0.727884   \n",
       "RPDE                -0.371439     -0.108345     -0.410294        0.355399   \n",
       "DFA                 -0.448063     -0.342681     -0.054428        0.098116   \n",
       "spread1             -0.400615     -0.072252     -0.394259        0.690928   \n",
       "spread2             -0.228277      0.004140     -0.246411        0.380236   \n",
       "D2                   0.201440      0.182949     -0.079596        0.429830   \n",
       "PPE                 -0.359729     -0.065420     -0.339180        0.719109   \n",
       "\n",
       "                  MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "MDVP:Fo(Hz)              -0.373000 -0.066314 -0.101633   -0.066332   \n",
       "MDVP:Fhi(Hz)             -0.025727  0.100042  0.094352    0.100015   \n",
       "MDVP:Flo(Hz)             -0.275528 -0.096635 -0.092359   -0.096603   \n",
       "MDVP:Jitter(%)            0.935258  0.990308  0.974104    0.990309   \n",
       "MDVP:Jitter(Abs)          1.000000  0.922751  0.897142    0.922752   \n",
       "MDVP:RAP                  0.922751  1.000000  0.957121    1.000000   \n",
       "MDVP:PPQ                  0.897142  0.957121  1.000000    0.957123   \n",
       "Jitter:DDP                0.922752  1.000000  0.957123    1.000000   \n",
       "MDVP:Shimmer              0.700846  0.758212  0.796449    0.758186   \n",
       "MDVP:Shimmer(dB)          0.714286  0.789464  0.838157    0.789433   \n",
       "Shimmer:APQ3              0.694713  0.743598  0.762033    0.743579   \n",
       "Shimmer:APQ5              0.646612  0.708445  0.785559    0.708424   \n",
       "MDVP:APQ                  0.645927  0.735876  0.802846    0.735859   \n",
       "Shimmer:DDA               0.694730  0.743606  0.762046    0.743587   \n",
       "NHR                       0.834742  0.919274  0.844012    0.919301   \n",
       "HNR                      -0.654049 -0.722339 -0.731760   -0.722288   \n",
       "RPDE                      0.436213  0.338044  0.327855    0.337982   \n",
       "DFA                       0.174904  0.063601  0.196297    0.063544   \n",
       "spread1                   0.732451  0.646316  0.714687    0.646315   \n",
       "spread2                   0.381336  0.320029  0.404053    0.319997   \n",
       "D2                        0.303092  0.424047  0.409698    0.423996   \n",
       "PPE                       0.744991  0.669057  0.768204    0.669063   \n",
       "\n",
       "                  MDVP:Shimmer  MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA  \\\n",
       "MDVP:Fo(Hz)          -0.087435         -0.062556  ... -0.066798    -0.083763   \n",
       "MDVP:Fhi(Hz)          0.005164          0.046498  ...  0.007741    -0.000869   \n",
       "MDVP:Flo(Hz)         -0.141103         -0.115316  ... -0.102637    -0.148017   \n",
       "MDVP:Jitter(%)        0.767399          0.802896  ...  0.756514     0.744918   \n",
       "MDVP:Jitter(Abs)      0.700846          0.714286  ...  0.645927     0.694730   \n",
       "MDVP:RAP              0.758212          0.789464  ...  0.735876     0.743606   \n",
       "MDVP:PPQ              0.796449          0.838157  ...  0.802846     0.762046   \n",
       "Jitter:DDP            0.758186          0.789433  ...  0.735859     0.743587   \n",
       "MDVP:Shimmer          1.000000          0.987164  ...  0.949841     0.987572   \n",
       "MDVP:Shimmer(dB)      0.987164          1.000000  ...  0.960779     0.962987   \n",
       "Shimmer:APQ3          0.987572          0.962983  ...  0.896179     1.000000   \n",
       "Shimmer:APQ5          0.982786          0.973634  ...  0.948999     0.959891   \n",
       "MDVP:APQ              0.949841          0.960779  ...  1.000000     0.896181   \n",
       "Shimmer:DDA           0.987572          0.962987  ...  0.896181     1.000000   \n",
       "NHR                   0.720967          0.743362  ...  0.692664     0.715025   \n",
       "HNR                  -0.835952         -0.828633  ... -0.801422    -0.827334   \n",
       "RPDE                  0.444032          0.406784  ...  0.448348     0.431593   \n",
       "DFA                   0.160499          0.165608  ...  0.157430     0.151850   \n",
       "spread1               0.651883          0.649745  ...  0.670719     0.607578   \n",
       "spread2               0.450566          0.452798  ...  0.501220     0.400178   \n",
       "D2                    0.505347          0.510674  ...  0.535212     0.465312   \n",
       "PPE                   0.690998          0.692418  ...  0.719671     0.641904   \n",
       "\n",
       "                       NHR       HNR      RPDE       DFA   spread1   spread2  \\\n",
       "MDVP:Fo(Hz)      -0.013346  0.041347 -0.371439 -0.448063 -0.400615 -0.228277   \n",
       "MDVP:Fhi(Hz)      0.166263 -0.029451 -0.108345 -0.342681 -0.072252  0.004140   \n",
       "MDVP:Flo(Hz)     -0.106473  0.201967 -0.410294 -0.054428 -0.394259 -0.246411   \n",
       "MDVP:Jitter(%)    0.906811 -0.727884  0.355399  0.098116  0.690928  0.380236   \n",
       "MDVP:Jitter(Abs)  0.834742 -0.654049  0.436213  0.174904  0.732451  0.381336   \n",
       "MDVP:RAP          0.919274 -0.722339  0.338044  0.063601  0.646316  0.320029   \n",
       "MDVP:PPQ          0.844012 -0.731760  0.327855  0.196297  0.714687  0.404053   \n",
       "Jitter:DDP        0.919301 -0.722288  0.337982  0.063544  0.646315  0.319997   \n",
       "MDVP:Shimmer      0.720967 -0.835952  0.444032  0.160499  0.651883  0.450566   \n",
       "MDVP:Shimmer(dB)  0.743362 -0.828633  0.406784  0.165608  0.649745  0.452798   \n",
       "Shimmer:APQ3      0.715017 -0.827330  0.431596  0.151840  0.607574  0.400194   \n",
       "Shimmer:APQ5      0.656570 -0.814920  0.396942  0.214747  0.645012  0.457585   \n",
       "MDVP:APQ          0.692664 -0.801422  0.448348  0.157430  0.670719  0.501220   \n",
       "Shimmer:DDA       0.715025 -0.827334  0.431593  0.151850  0.607578  0.400178   \n",
       "NHR               1.000000 -0.715837  0.368259 -0.132850  0.538909  0.315831   \n",
       "HNR              -0.715837  1.000000 -0.600515 -0.010869 -0.669196 -0.430786   \n",
       "RPDE              0.368259 -0.600515  1.000000 -0.117896  0.585191  0.467334   \n",
       "DFA              -0.132850 -0.010869 -0.117896  1.000000  0.195518  0.162144   \n",
       "spread1           0.538909 -0.669196  0.585191  0.195518  1.000000  0.646920   \n",
       "spread2           0.315831 -0.430786  0.467334  0.162144  0.646920  1.000000   \n",
       "D2                0.471325 -0.595321  0.234777 -0.166319  0.488723  0.524845   \n",
       "PPE               0.550427 -0.688676  0.539678  0.271564  0.961909  0.641415   \n",
       "\n",
       "                        D2       PPE  \n",
       "MDVP:Fo(Hz)       0.201440 -0.359729  \n",
       "MDVP:Fhi(Hz)      0.182949 -0.065420  \n",
       "MDVP:Flo(Hz)     -0.079596 -0.339180  \n",
       "MDVP:Jitter(%)    0.429830  0.719109  \n",
       "MDVP:Jitter(Abs)  0.303092  0.744991  \n",
       "MDVP:RAP          0.424047  0.669057  \n",
       "MDVP:PPQ          0.409698  0.768204  \n",
       "Jitter:DDP        0.423996  0.669063  \n",
       "MDVP:Shimmer      0.505347  0.690998  \n",
       "MDVP:Shimmer(dB)  0.510674  0.692418  \n",
       "Shimmer:APQ3      0.465317  0.641892  \n",
       "Shimmer:APQ5      0.501556  0.700765  \n",
       "MDVP:APQ          0.535212  0.719671  \n",
       "Shimmer:DDA       0.465312  0.641904  \n",
       "NHR               0.471325  0.550427  \n",
       "HNR              -0.595321 -0.688676  \n",
       "RPDE              0.234777  0.539678  \n",
       "DFA              -0.166319  0.271564  \n",
       "spread1           0.488723  0.961909  \n",
       "spread2           0.524845  0.641415  \n",
       "D2                1.000000  0.475093  \n",
       "PPE               0.475093  1.000000  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('status',axis=1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "executionInfo": {
     "elapsed": 479,
     "status": "ok",
     "timestamp": 1689500061109,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "5GPmjur6DMqX",
    "outputId": "bc221854-e447-45f5-bd34-0f37f286cf67"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['status'].value_counts(normalize=True).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 429,
     "status": "ok",
     "timestamp": 1689500112734,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "FryodeXgDMwo",
    "outputId": "89e04ca0-e0a2-4580-ce7e-b64a9294e689"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
       "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
       "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
       "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',\n",
       "       'spread1', 'spread2', 'D2', 'PPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X0nsA6jADg0K"
   },
   "source": [
    "### Target and feature data segregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ez6jWTwiDiqb"
   },
   "outputs": [],
   "source": [
    "target='status'\n",
    "features=['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
    "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
    "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
    "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'RPDE', 'DFA',\n",
    "       'spread1', 'spread2', 'D2', 'PPE']\n",
    "X=df[features]\n",
    "y=df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1689500148671,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "XKCw1gdNDlsa",
    "outputId": "26b6a41b-8912-465b-dbe8-daece8559f79"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((192, 22), (192,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 438,
     "status": "ok",
     "timestamp": 1689500158041,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "elT00wdiDnxO",
    "outputId": "a0643c49-72b0-457e-9390-a4af9042f50a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 694,
     "status": "ok",
     "timestamp": 1689500167545,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "NPEV5dkQDqIw",
    "outputId": "3bdd9555-387b-4cac-dc3b-f383e1595245"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7712418300653595"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy\n",
    "y_base_accuracy=y_train.value_counts(normalize=True).max()\n",
    "y_base_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "74oOA-p0Dshv"
   },
   "outputs": [],
   "source": [
    "scale=StandardScaler()\n",
    "X_train=pd.DataFrame(scale.fit_transform(X_train),columns=X.columns)\n",
    "X_test=pd.DataFrame(scale.transform(X_test),columns=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 451,
     "status": "ok",
     "timestamp": 1689500185091,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "4Bzt87zLDuXb",
    "outputId": "7e976229-8435-49b4-8004-2ae86daf0324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True  True  True False  True False  True False  True\n",
      "  True False False  True  True False  True  True  True  True]\n",
      "[1 1 7 1 1 1 5 1 3 1 4 1 1 2 8 1 1 6 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "lm=LogisticRegression()\n",
    "rfe=RFE(lm,n_features_to_select=15)\n",
    "rfe.fit(X_train,y_train)\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "irOv6T7XDwjb"
   },
   "outputs": [],
   "source": [
    "col=X_train.columns[rfe.support_]\n",
    "X_train=X_train[col]\n",
    "X_test=X_test[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 441,
     "status": "ok",
     "timestamp": 1689500206261,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "7sC-7zLBDzZi",
    "outputId": "c975fd7b-f63a-48e0-f46a-d3d3ba57d8d7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1689500251770,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "B_AHhQLeD8BB",
    "outputId": "7c661d84-6ce0-4025-b396-732b7b2b9272"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8823529411764706\n",
      "0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "#accuracy improved by 10%\n",
    "print(lm.score(X_train,y_train))\n",
    "print(lm.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "executionInfo": {
     "elapsed": 1726,
     "status": "ok",
     "timestamp": 1689500261753,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "fCeBvHcTEA7V",
    "outputId": "2ddb8c08-5910-4379-c33e-be21a0a72d35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X_train.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6xfiJhYYGTjn"
   },
   "source": [
    "In above model, we let some of the data go to waste when we used the RFE function to choose columns which provide best predictions. Furthermore, there are cases of high correlation among the independent variables in 'X_train'. So, we move on to decision tree model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S9lXozXrED7K"
   },
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1689500280153,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "3_PuzCN9EEpe",
    "outputId": "d592fdbd-fa16-41d8-a01e-806d2b016678"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148
    },
    "executionInfo": {
     "elapsed": 661,
     "status": "ok",
     "timestamp": 1689500679601,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "hC0X9v3VFlsM",
    "outputId": "d7e29bf2-ee0c-495c-a467-ae5247d71017"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(random_state=42))])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    DecisionTreeClassifier(random_state=42)\n",
    ")\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z_Jx5ETAGjjV"
   },
   "source": [
    " please rerun this cell to show the HTML representation or trust the notebook.\n",
    "On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7bsF07iSGn8c"
   },
   "source": [
    "accuracy scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 423,
     "status": "ok",
     "timestamp": 1689500951725,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "ayWf8WQIGph6",
    "outputId": "a001adfc-1d58-4fff-b79d-46ddadec47de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "print(dt.score(X_train,y_train))\n",
    "print(dt.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "41k1S-k1GwTV"
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1689500992602,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "xVPcz73RGyTG",
    "outputId": "d7c2eaae-774f-4141-b9b8-00ca4ed79afd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1689501001582,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "RYskJ4byG1sy",
    "outputId": "f2f1dc65-1f24-4cd2-801a-afa22cd86a22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('randomforestclassifier',\n",
      "                 RandomForestClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    RandomForestClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2585,
     "status": "ok",
     "timestamp": 1689501014294,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "-tEZ1Qh4G4Xl",
    "outputId": "f3d7b4ba-9e9f-4c7c-ec44-887d20769271"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87096774 0.87096774 0.93548387 0.96666667 0.9       ]\n"
     ]
    }
   ],
   "source": [
    "cv_acc_scores = cross_val_score(clf,X_train,y_train,cv=5,n_jobs=-1)\n",
    "print(cv_acc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1689501024615,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "nzE3RiLVG7bc",
    "outputId": "7fc5672b-035f-4823-9be0-fc423fb53cbd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'randomforestclassifier__n_estimators': range(25, 100, 10),\n",
       " 'randomforestclassifier__max_depth': range(10, 21)}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'randomforestclassifier__n_estimators':range(25,100,10),\n",
    "    'randomforestclassifier__max_depth':range(10,21,1)\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "executionInfo": {
     "elapsed": 437,
     "status": "ok",
     "timestamp": 1689501034077,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "0_v5zoiAG9mz",
    "outputId": "9a6195a8-1d3f-4777-df14-9e15cb219a69"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;randomforestclassifier&#x27;,\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;randomforestclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;randomforestclassifier&#x27;,\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;randomforestclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(10, 21),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ssaNacynH2Zc"
   },
   "source": [
    " please rerun this cell to show the HTML representation or trust the notebook.\n",
    "On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "executionInfo": {
     "elapsed": 44298,
     "status": "ok",
     "timestamp": 1689501333608,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "QDHv9DnDH7Hg",
    "outputId": "0a5fcc39-c639-47a4-faee-11f21dc5c300"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 88 candidates, totalling 440 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;randomforestclassifier&#x27;,\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;randomforestclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;randomforestclassifier&#x27;,\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;randomforestclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(10, 21),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1689501333608,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "hDD9WyaoH_WO",
    "outputId": "b9396e8e-bded-4e5d-d91a-874ce8e136a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2gC-VoXAIO1e"
   },
   "source": [
    "### Gradient boosting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 419,
     "status": "ok",
     "timestamp": 1689501377793,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "r4a8xJZmIQe2",
    "outputId": "27a438c7-ff91-49da-c863-bb03645714ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n",
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('gradientboostingclassifier',\n",
      "                 GradientBoostingClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    GradientBoostingClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1689501387582,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "dehK685cIUAg",
    "outputId": "84d7dc3c-d958-45c5-fcc8-bd3a91df0c39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gradientboostingclassifier__n_estimators': range(25, 100, 10),\n",
       " 'gradientboostingclassifier__max_depth': range(10, 21)}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'gradientboostingclassifier__n_estimators':range(25,100,10),\n",
    "    'gradientboostingclassifier__max_depth':range(10,21,1)\n",
    "}\n",
    "params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "executionInfo": {
     "elapsed": 465,
     "status": "ok",
     "timestamp": 1689501413034,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "KcTUYt5sIZ_P",
    "outputId": "861c950b-49c9-42c8-bf65-a277ef9bae26"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;gradientboostingclassifier&#x27;,\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;gradientboostingclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;gradientboostingclassifier&#x27;,\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;gradientboostingclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;gradientboostingclassifier&#x27;,\n",
       "                 GradientBoostingClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('gradientboostingclassifier',\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingclassifier__max_depth': range(10, 21),\n",
       "                         'gradientboostingclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "executionInfo": {
     "elapsed": 53942,
     "status": "ok",
     "timestamp": 1689502894491,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "GyFJhAYsN1d9",
    "outputId": "49e8bd20-dcfe-410d-8feb-a00feb1eeb71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 88 candidates, totalling 440 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;gradientboostingclassifier&#x27;,\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;gradientboostingclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;gradientboostingclassifier&#x27;,\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingclassifier__max_depth&#x27;: range(10, 21),\n",
       "                         &#x27;gradientboostingclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;gradientboostingclassifier&#x27;,\n",
       "                 GradientBoostingClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('gradientboostingclassifier',\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingclassifier__max_depth': range(10, 21),\n",
       "                         'gradientboostingclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sSj-DEs2N75T"
   },
   "source": [
    " please rerun this cell to show the HTML representation or trust the notebook.\n",
    "On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 62,
     "status": "ok",
     "timestamp": 1689502894493,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "BErAQt-hN9B8",
    "outputId": "6aff7d49-5021-4e73-92fb-19852d8d97a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kvhX3zpjOA4K"
   },
   "source": [
    "### Adaboost classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 51,
     "status": "ok",
     "timestamp": 1689502894494,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "ykZ2CtiGODVX",
    "outputId": "f618bbce-28bf-4d7f-ca10-61eeddf0d003"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n",
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('adaboostclassifier', AdaBoostClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    AdaBoostClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1689502909366,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "YgZBuqpVOHkD",
    "outputId": "5b3e4f44-8d4e-43b6-e7f0-fa8ba716c799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adaboostclassifier__n_estimators': range(25, 100, 10)}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'adaboostclassifier__n_estimators':range(25,100,10)\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 404,
     "status": "ok",
     "timestamp": 1689502916464,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "FDCbJ0HAOJUL",
    "outputId": "2ba6af4e-abe1-41f0-b4fa-b7e40c51e8c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adaboostclassifier__n_estimators': range(25, 100, 10)}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'adaboostclassifier__n_estimators': range(25, 100, 10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1689502929617,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "--ee7rYFOMeE",
    "outputId": "b36ac99e-a10c-482c-e200-54b96866020d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;adaboostclassifier&#x27;,\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;adaboostclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;adaboostclassifier&#x27;,\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;adaboostclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;adaboostclassifier&#x27;, AdaBoostClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('adaboostclassifier',\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'adaboostclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "executionInfo": {
     "elapsed": 6544,
     "status": "ok",
     "timestamp": 1689502954974,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "vP1nQetbOQvi",
    "outputId": "7699a7f2-f1cd-4072-d19c-32d71c57965f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;adaboostclassifier&#x27;,\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;adaboostclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;adaboostclassifier&#x27;,\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;adaboostclassifier__n_estimators&#x27;: range(25, 100, 10)},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;adaboostclassifier&#x27;, AdaBoostClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('adaboostclassifier',\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'adaboostclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 374,
     "status": "ok",
     "timestamp": 1689502963923,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "xGslXpoyOT7B",
    "outputId": "a3edae1e-1077-4178-dd4b-fa7a619142be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2FSqzmvnOW5n"
   },
   "source": [
    "The better model in all of the above cases is the Decision tree Classifier as it can bypass the issue of multicollinearity and is much less complex compared to other models, hance, faster. The worst is Adaboost classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decisiontree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 381,
     "status": "ok",
     "timestamp": 1689502978784,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "Br2OmEjROXhV",
    "outputId": "90e5524c-098c-43f8-a750-7cc82932dcdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "## Decision tree\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1689502985590,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "cEZtTQEiOaQA",
    "outputId": "fd726147-b7b6-413e-f084-d29628f0c88b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(random_state=42))])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    DecisionTreeClassifier(random_state=42)\n",
    ")\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 390,
     "status": "ok",
     "timestamp": 1689502995440,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "aJ6FTA3NOcmU",
    "outputId": "d5e36ee7-6185-45ae-beee-65f0326e29af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "## accuracy score\n",
    "print(dt.score(X_train,y_train))\n",
    "print(dt.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KGFOo4YtOeU8"
   },
   "outputs": [],
   "source": [
    "y_pred2=dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 397,
     "status": "ok",
     "timestamp": 1689503016151,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "iRtFZUSgOf9_",
    "outputId": "2b457d9b-98b2-435d-bfd2-6e7ff7500985"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy = 1.0\n",
      "test accuracy = 0.9\n",
      "test precision = 0.93\n",
      "test recall = 0.93\n",
      "test error = 0.1\n"
     ]
    }
   ],
   "source": [
    "cnf=confusion_matrix(y_test,y_pred2)\n",
    "print('training accuracy = {}'.format(round(accuracy_score(y_train,dt.predict(X_train)),2)))\n",
    "print('test accuracy = {}'.format(round((cnf[0][0]+cnf[1][1])/(cnf[0][0]+cnf[0][1]+cnf[1][0]+cnf[1][1]),2)))\n",
    "print('test precision = {}'.format(round((cnf[1][1])/(cnf[0][1]+cnf[1][1]),2)))\n",
    "print('test recall = {}'.format(round((cnf[1][1])/(cnf[1][0]+cnf[1][1]),2)))\n",
    "print('test error = {}'.format(round((cnf[0][1]+cnf[1][0])/(cnf[0][0]+cnf[0][1]+cnf[1][0]+cnf[1][1]),2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1689503040669,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "zcqhUhL1OkAU",
    "outputId": "895186c7-9581-4301-9e6c-c88a07456d81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82        11\n",
      "           1       0.93      0.93      0.93        28\n",
      "\n",
      "    accuracy                           0.90        39\n",
      "   macro avg       0.87      0.87      0.87        39\n",
      "weighted avg       0.90      0.90      0.90        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "executionInfo": {
     "elapsed": 1049,
     "status": "ok",
     "timestamp": 1689503039158,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "HhMEgazvOkrG",
    "outputId": "0de6efd0-0b3d-483d-b7c5-c498c03d296b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7b4e220780a0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_test,y_pred2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 1359,
     "status": "ok",
     "timestamp": 1689503112029,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "ZS44dpmkOyWG",
    "outputId": "06af78b1-6a76-44d1-d39e-ff02dc5d1664"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance\n",
    "importances = dt.named_steps['decisiontreeclassifier'].feature_importances_\n",
    "\n",
    "# Add importances into a Series\n",
    "feat_imp = pd.Series(importances,index=X_train.columns).sort_values()\n",
    "# plot\n",
    "feat_imp.tail(15).plot(kind='barh')\n",
    "plt.xlabel(\"Gini Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.title(\"Decision tree model Feature Importance\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 385,
     "status": "ok",
     "timestamp": 1689503121046,
     "user": {
      "displayName": "Kiranmai Arnepalli",
      "userId": "17827453611620659025"
     },
     "user_tz": -330
    },
    "id": "d3nIyJAUO7OE",
    "outputId": "50f4258c-8c01-485c-f166-22f9dcdf7a20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9821941765776485"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp.tail(9).sum()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOTl9noytAaJFlY9iNG+XGg",
   "provenance": []
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}