Logistic Regression all variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with all the variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the Data and Fit All the Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>disc_number</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>is_hit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.428</td>\n",
       "      <td>3</td>\n",
       "      <td>-10.161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.326</td>\n",
       "      <td>100.033</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>289596</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
       "0          0.67   0.428    3   -10.161     0       0.0369         0.537   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  time_signature  disc_number  \\\n",
       "0          0.000003     0.118    0.326  100.033               4            1   \n",
       "\n",
       "   duration_ms  is_hit  \n",
       "0       289596       0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "data = pd.read_pickle('merged_data.pkl') \n",
    "\n",
    "# identfiy numerical volumns\n",
    "columns = ['danceability','energy','key','loudness','mode',\n",
    " 'speechiness','acousticness','instrumentalness',\n",
    " 'liveness','valence','tempo','time_signature','disc_number','duration_ms','is_hit']\n",
    "\n",
    "df = data[columns] # get numerical columns\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('is_hit', axis=1) # features for model\n",
    "y = df['is_hit'] # target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 0)\n",
    "\n",
    "# fit L1-regularized logistic regression on training set\n",
    "logRegr = LogisticRegression(solver='lbfgs').fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction accuracy: 0.8880982748204337\n"
     ]
    }
   ],
   "source": [
    "score = logRegr.score(x_test, y_test)\n",
    "error_rate = 1 - score\n",
    "print(\"Prediction accuracy:\", 1 - error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross-validation\n",
    "rfc_cv_score = cross_val_score(logRegr, X, y, cv=10, scoring=\"roc_auc\")\n",
    "\n",
    "# predictions\n",
    "y_predict = logRegr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "Predicted      0\n",
      "Actual          \n",
      "0          13230\n",
      "1           1667\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94     13230\n",
      "           1       0.00      0.00      0.00      1667\n",
      "\n",
      "   micro avg       0.89      0.89      0.89     14897\n",
      "   macro avg       0.44      0.50      0.47     14897\n",
      "weighted avg       0.79      0.89      0.84     14897\n",
      "\n",
      "=== All AUC Scores ===\n",
      "[0.55954285 0.5272056  0.57737906 0.60082517 0.58614531 0.61554357\n",
      " 0.60745836 0.56391268 0.54028699 0.55168529]\n",
      "\n",
      "\n",
      "=== Mean AUC Score ===\n",
      "Mean AUC Score - Logistic Regression  0.5729984895464244\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Confusion Matrix ===\")\n",
    "# print(confusion_matrix(y_test, y_predict))\n",
    "x = pd.crosstab(y_test, y_predict, rownames=['Actual'], colnames=['Predicted'])\n",
    "print(x)\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_predict))\n",
    "print(\"=== All AUC Scores ===\")\n",
    "print(rfc_cv_score)\n",
    "print('\\n')\n",
    "print(\"=== Mean AUC Score ===\")\n",
    "print(\"Mean AUC Score - Logistic Regression \", rfc_cv_score.mean())"
   ]
  }
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