main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data loading libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Importing data visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Importing statistical analysis libraries \n",
    "from scipy.stats import ttest_ind\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Basic Exploration**\n",
    "* Dataset shape: (284807, 31)\n",
    "* Dtypes: All columns are float except the target which is `int64`\n",
    "* No column has missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Target Exploration**\n",
    "* Dataset is imbalanced, class-1 has only 0.17% contribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class-frequency:\n",
      " 0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n",
      "\n",
      "Class-Distributino:\n",
      " 0    99.83\n",
      "1     0.17\n",
      "Name: Class, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Class-frequency:\\n', data['Class'].value_counts())\n",
    "print()\n",
    "print('Class-Distributino:\\n', round(data['Class'].value_counts(normalize=True)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Checking Outliers**\n",
    "* `Amount` and other PCA features have outliers but they perhaps be fraud cases, hence, need further investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>2.239053e-15</td>\n",
       "      <td>1.673327e-15</td>\n",
       "      <td>-1.247012e-15</td>\n",
       "      <td>8.190001e-16</td>\n",
       "      <td>1.207294e-15</td>\n",
       "      <td>4.887456e-15</td>\n",
       "      <td>1.437716e-15</td>\n",
       "      <td>-3.772171e-16</td>\n",
       "      <td>9.564149e-16</td>\n",
       "      <td>1.039917e-15</td>\n",
       "      <td>6.406204e-16</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.952742e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>9.153160e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.709250e-01</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-5.791881e+00</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-4.498945e+00</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-5.449772e+01</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-6.485393e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-5.828843e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.117214e-01</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>-1.356806e-02</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>4.807155e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-6.248109e-02</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>6.625050e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>6.488208e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.330408e-01</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>7.126883e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>8.877742e+00</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>3.942090e+01</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "                V10           V11           V12           V13           V14  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   2.239053e-15  1.673327e-15 -1.247012e-15  8.190001e-16  1.207294e-15   \n",
       "std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   \n",
       "min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   \n",
       "25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   \n",
       "50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   \n",
       "75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   \n",
       "max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01   \n",
       "\n",
       "                V15           V16           V17           V18           V19  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   4.887456e-15  1.437716e-15 -3.772171e-16  9.564149e-16  1.039917e-15   \n",
       "std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   \n",
       "min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   \n",
       "25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   \n",
       "50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   \n",
       "75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   \n",
       "max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00   \n",
       "\n",
       "                V20           V21           V22           V23           V24  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   6.406204e-16  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    3.942090e+01  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(data.drop(columns=['Time', 'Amount', 'Class']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Amount']].boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Bivariate Analysis**\n",
    "### **Quantative Vs Qualitative**\n",
    "* t-test showing V8, V22, V23, and V25 does not have statistically significant impact on target variable.\n",
    "* Dropped these variable from the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ttest_util(df, col):\n",
    "    l = list(df.groupby('Class')[col].apply(np.array))\n",
    "    var_l1 = l[0].var()\n",
    "    var_l2 = l[1].var()\n",
    "    return l[0], l[1], var_l2/var_l1<=4 and var_l2/var_l1>=0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ttest(df):\n",
    "\n",
    "    cols = df.columns.drop('Class')\n",
    "\n",
    "    for col in cols:\n",
    "        a, b, equal_var = ttest_util(df, col)\n",
    "        _, p_value = ttest_ind(a, b, equal_var=equal_var)\n",
    "        print(f'p-value for {col} is: {p_value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value for Time is: 4.8182688917960346e-11\n",
      "p-value for V1 is: 5.4916429825378326e-45\n",
      "p-value for V2 is: 1.2885797612095705e-59\n",
      "p-value for V3 is: 4.7860814074282e-75\n",
      "p-value for V4 is: 4.587316921388042e-136\n",
      "p-value for V5 is: 1.4970277071054122e-33\n",
      "p-value for V6 is: 4.2131113453230875e-120\n",
      "p-value for V7 is: 4.2922313194412136e-52\n",
      "p-value for V8 is: 0.06275811803424983\n",
      "p-value for V9 is: 1.1935046623364425e-79\n",
      "p-value for V10 is: 3.382883650613687e-93\n",
      "p-value for V11 is: 4.577212375356592e-120\n",
      "p-value for V12 is: 1.4813165028380743e-112\n",
      "p-value for V13 is: 0.014737338961005653\n",
      "p-value for V14 is: 1.040150709902715e-140\n",
      "p-value for V15 is: 0.02420142600145766\n",
      "p-value for V16 is: 7.905325639437749e-84\n",
      "p-value for V17 is: 1.5580938057841998e-71\n",
      "p-value for V18 is: 2.6392225457940417e-52\n",
      "p-value for V19 is: 5.801517302142051e-77\n",
      "p-value for V20 is: 7.964292023532662e-27\n",
      "p-value for V21 is: 4.882129855322934e-05\n",
      "p-value for V22 is: 0.8346791940015029\n",
      "p-value for V23 is: 0.5710402154248273\n",
      "p-value for V24 is: 0.0001163760280885243\n",
      "p-value for V25 is: 0.0775250093814013\n",
      "p-value for V26 is: 0.01741970537124198\n",
      "p-value for V27 is: 0.006129683626016929\n",
      "p-value for V28 is: 3.5953535619896607e-07\n",
      "p-value for Amount is: 0.002651220649191683\n"
     ]
    }
   ],
   "source": [
    "ttest(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['V8', 'V22', 'V23', 'V25'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Quantative Vs Quantative**\n",
    "* Data has PCA components as most of the features resulting into correlation zero.\n",
    "* Amount is correlated and can be derived from rest of the feaures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(round(data.corr(),2), annot=True, cmap = 'crest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Checking MultiCollinearity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_vif(data, col):\n",
    "    return variance_inflation_factor(data.values, list(data.columns).index(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIF-factor value for Amount is: 8.685883234406392\n",
      "VIF-factor value for Amount is: 1.9868843018835156\n"
     ]
    }
   ],
   "source": [
    "for col in ['Amount', 'Time']:\n",
    "    print('VIF-factor value for Amount is:', cal_vif(data, col))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Utils**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.feature_selection import RFE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split, PredefinedSplit, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.metrics import classification_report, make_scorer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Scaler(TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self.scaler = RobustScaler()\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.scaler.fit(X)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return self.scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def au_pr_curve(y_act, y_pred):\n",
    "\n",
    "    from sklearn.metrics import precision_recall_curve, auc\n",
    "    precision, recall, _ = precision_recall_curve(y_act, y_pred)\n",
    "    auprc = auc(recall, precision)\n",
    "    return auprc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_scorer():\n",
    "    return make_scorer(au_pr_curve, response_method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_TopkFeatures(X, y, k, estimator = LogisticRegression()):\n",
    "    columns = X.columns\n",
    "    X = Scaler().fit_transform(X)\n",
    "    estimator_ = RFE(estimator=estimator)\n",
    "    params_dict = {'n_features_to_select': k}\n",
    "    model = GridSearchCV(estimator_, param_grid=params_dict, scoring=custom_scorer())\n",
    "    model.fit(X, y)\n",
    "    selected_features = columns[model.best_estimator_.support_]\n",
    "    return selected_features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smote_enn(X, y):\n",
    "    sme = SMOTEENN(random_state = 42)\n",
    "    X_res, y_res = sme.fit_resample(X, y)\n",
    "    return X_res, y_res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def undersampling(X, y):\n",
    "    cc = ClusterCentroids(\n",
    "    estimator=MiniBatchKMeans(n_init=1, random_state=0), random_state=42,\n",
    "    sampling_strategy=0.2\n",
    "    )\n",
    "    X_res, y_res = cc.fit_resample(X, y)\n",
    "    return X_res, y_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_splits(X, y, dev_prop = 0.15, test_prop = 0.15):\n",
    "\n",
    "    tr_X, test_X, tr_y, test_y = train_test_split(X, y, test_size=test_prop, random_state=42, stratify=y)\n",
    "    train_X, dev_X, train_y, dev_y = train_test_split(tr_X, tr_y, test_size=dev_prop/(1-test_prop), random_state=42, stratify=tr_y)\n",
    "\n",
    "    return train_X, dev_X, test_X, train_y, dev_y, test_y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(data, scaling = False):\n",
    "    df = data.copy()\n",
    "    df = shuffle(df, random_state=True)\n",
    "\n",
    "    X = data.drop('Class', axis=1)\n",
    "    y = data['Class']\n",
    "\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_report(y_act, y_pred):\n",
    "    print(classification_report(y_act, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(model_name):\n",
    "    if model_name=='Logistic_Regression':\n",
    "        return (\n",
    "            model_name,\n",
    "            LogisticRegression(),\n",
    "            {}\n",
    "        )\n",
    "    elif model_name=='DecisionTree':\n",
    "        return (\n",
    "            model_name,\n",
    "            DecisionTreeClassifier(),\n",
    "            {\n",
    "                'max_depth': [5, 10, 20, 50],\n",
    "                'min_samples_leaf': [1, 2, 5],\n",
    "                'min_samples_split': [2, 5]\n",
    "            }\n",
    "        )\n",
    "    elif model_name=='RandomForest':\n",
    "        return (\n",
    "            model_name,\n",
    "            RandomForestClassifier(),\n",
    "            {\n",
    "                'max_depth': [5, 10, 20, 50],\n",
    "                'min_samples_leaf': [1, 2, 5],\n",
    "                'min_samples_split': [2, 5],\n",
    "                'n_estimators': [50, 100, 300]\n",
    "            }\n",
    "        )\n",
    "    elif model_name=='XGBoost':\n",
    "        return (\n",
    "            model_name,\n",
    "            XGBClassifier(),\n",
    "            {\n",
    "                'eta': [0.01, 0.05, 0.1, 0.3],\n",
    "                'n_estimators': [100, 200, 300],\n",
    "                'max_depth': [3, 4, 5, 6],\n",
    "                'min_child_weight': [1, 5, 10, 20],\n",
    "                'gamma': [0, 0.1, 0.2, 0.3],\n",
    "                'subsample': [0.6, 0.8, 0.9],\n",
    "                'colsample_bytree': [0.6, 0.8, 0.9]\n",
    "            }\n",
    "        )\n",
    "    else:\n",
    "        raise Exception('Plese select model name from: [\"XGBoost\", \"DecisionTree\", \"RandomForest\", \"Logistic_Regression\"]')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LR:\n",
    "    def __init__(self) -> None:\n",
    "        pass\n",
    "\n",
    "    def fit(self, X_train, y_train, X_dev=None, y_dev=None):\n",
    "\n",
    "        # feature scaling\n",
    "        print('Scaling the features ...')\n",
    "        self.scaler = Scaler()\n",
    "        X_train = pd.DataFrame(self.scaler.fit_transform(X_train), columns=X_train.columns)\n",
    "        \n",
    "        # class balancing \n",
    "        print('Balancing the class ...')\n",
    "        print('Class distribution before resampling:\\n', y_train.value_counts())\n",
    "        print()\n",
    "        X_train, y_train = undersampling(X_train, y_train)\n",
    "        print('Class distribution after resampling:\\n', y_train.value_counts())\n",
    "        \n",
    "        \n",
    "        # Loading and fitting model\n",
    "        print('fitting the model ...')\n",
    "        self.name, model, params_dict = get_model('Logistic_Regression')\n",
    "\n",
    "        if X_dev is not None:\n",
    "            cv_indices = np.append(np.full(X_train.shape[0], -1), np.full(X_dev.shape[0], 0))\n",
    "            X_train = pd.concat([X_train, X_dev])\n",
    "            y_train = pd.concat([y_train, y_dev])\n",
    "            model = GridSearchCV(model, param_grid=params_dict, cv = PredefinedSplit(cv_indices))\n",
    "        else:\n",
    "            model = GridSearchCV(model, param_grid=params_dict)\n",
    "            print('Best parameters are:', model.best_params_)\n",
    "        \n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        self.model = model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling the features ...\n",
      "Balancing the class ...\n",
      "Class distribution before resampling:\n",
      " 0    199020\n",
      "1       344\n",
      "Name: Class, dtype: int64\n",
      "\n",
      "Class distribution after resampling:\n",
      " 0    1720\n",
      "1     344\n",
      "Name: Class, dtype: int64\n",
      "fitting the model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "X, y = prepare_data(data)\n",
    "train_X, dev_X, test_X, train_y, dev_y, test_y = get_splits(X,y)\n",
    "\n",
    "log_reg = LR()\n",
    "log_reg.fit(train_X, train_y, dev_X, dev_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log_reg.model.predict(test_X)\n",
    "y_pred_probs = log_reg.model.predict_proba(test_X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under Precision-Recall curve is:  0.5971785349583383\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     42648\n",
      "           1       0.79      0.73      0.76        74\n",
      "\n",
      "    accuracy                           1.00     42722\n",
      "   macro avg       0.90      0.86      0.88     42722\n",
      "weighted avg       1.00      1.00      1.00     42722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Area Under Precision-Recall curve is: ', au_pr_curve(test_y, y_pred_probs))\n",
    "print()\n",
    "get_report(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'Feature': train_X.columns, 'Importance': log_reg.model.coef_.ravel()}).sort_values(by = 'Importance')\n",
    "plt.figure(figsize=(10,7))\n",
    "# feature_importance.plot(kind = 'barh', x='Feature', y='Importance')\n",
    "plt.barh(feature_importance['Feature'], feature_importance['Importance'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Decision-Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing train/dev/test splits\n",
    "X, y = prepare_data(data)\n",
    "train_X, dev_X, test_X, train_y, dev_y, test_y = get_splits(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing undersampling\n",
    "train_X, train_y = undersampling(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 219, in __call__\n",
      "    return self._score(\n",
      "  File \"/home/tushar/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\", line 267, in _score\n",
      "    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)\n",
      "TypeError: au_pr_curve() got an unexpected keyword argument 'response_method'\n",
      "\n",
      "  warnings.warn(\n",
      "/home/tushar/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['V2', 'V4', 'V13', 'V14', 'V26'], dtype='object')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting top-k best features\n",
    "selected_features = get_TopkFeatures(train_X, train_y, [5, 7, 10, 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V2', 'V4', 'V13', 'V14', 'V26'], dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_X[selected_features]\n",
    "dev_X = dev_X[selected_features]\n",
    "test_X = test_X[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting cv for cross-validation\n",
    "\n",
    "cv_indices = np.append(np.full(train_X.shape[0], -1), np.full(dev_X.shape[0], 0))\n",
    "train_X = pd.concat([train_X, dev_X])\n",
    "train_y = pd.concat([train_y, dev_y])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters are: {'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "# fitting decision tree model with hyperparameter tuning\n",
    "\n",
    "_, dt_model, dt_params = get_model('DecisionTree')\n",
    "dt_model = GridSearchCV(dt_model, param_grid=dt_params, scoring=custom_scorer(), cv=PredefinedSplit(cv_indices))\n",
    "\n",
    "dt_model.fit(train_X, train_y)\n",
    "print('Best parameters are:', dt_model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dt_model.best_estimator_.predict(test_X)\n",
    "y_pred_probs = dt_model.best_estimator_.predict_proba(test_X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under Precision Recall curve is:  0.60709720535426\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     42648\n",
      "           1       0.61      0.74      0.67        74\n",
      "\n",
      "    accuracy                           1.00     42722\n",
      "   macro avg       0.81      0.87      0.84     42722\n",
      "weighted avg       1.00      1.00      1.00     42722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Area Under Precision Recall curve is: ',au_pr_curve(test_y, y_pred_probs))\n",
    "print()\n",
    "get_report(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAGbCAYAAADp+bXWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATuElEQVR4nO3dcYyk933X8c+3PrnlSH1EOIXYCd22SRUCTlr3iJBQApURqXtSipQ0uYpWOEqxSChVKChYCn9UrVAPIqigrQIumDSJuNQ9EcfUTUJJr6qpYswddWK5IXFSTLETSFvja4xVoM6XP3a2WZ3Ot2Pvd2d2b18vaeWZZ57Z/f40e+v3Pc+zc9XdAQBgxletewAAgCuJuAIAGCSuAAAGiSsAgEHiCgBg0JF1D7Dl2muv7Y2NjXWPAQCwo/Pnz/92d7/gUo/tm7ja2NjIuXPn1j0GAMCOquq/PdNjTgsCAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMOjIugfY8uBjF7Jx2z3rHgMAOKAeOXVi3SMkceQKAGCUuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQZeNq6o6W1WvvWjb26vq3VX1kap6oqp+/hme+0+r6snJYQEA9rudjlydTnLyom0nF9vfleT7LvWkqjqe5Pm7ng4A4IDZKa7OJDlRVVcnSVVtJLkuyb3d/bEkX7r4CVV1VTbD6x2zowIA7H+XjavufjzJ/UluXmw6meTO7u7LPO0Hktzd3V/Y6YtX1a1Vda6qzj391IVlZwYA2LeWuaB9+6nBrVOCl1RV1yX57iQ/scwX7+7bu/t4dx+/6uixZZ4CALCvLRNXH0pyU1XdmORod5+/zL7fmuQlST5bVY8kOVpVn939mAAAB8ORnXbo7ier6mySO3KZo1aLfe9J8se37lfVk939kl1PCQBwQCz7Plenk7wy2+Kqqu5N8nPZPKr16MVv2QAAcBjteOQqSbr7riR10bZXL/G85z23sQAADibv0A4AMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg46se4AtN1x/LOdOnVj3GAAAu+LIFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADDoyLoH2PLgYxeycds96x5jTz1y6sS6RwAA9pgjVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAx6znFVVWer6rUXbXt7Vb17cfuaqnq0qn5yt0MCABwUuzlydTrJyYu2nVxsT5IfTfIru/j8AAAHzm7i6kySE1V1dZJU1UaS65LcW1XfluSPJfl3u54QAOAAec5x1d2PJ7k/yc2LTSeT3JmkkvyjJH9np89RVbdW1bmqOvf0Uxee6ygAAPvGbi9o335qcOuU4NuS/EJ3P7rTk7v79u4+3t3Hrzp6bJejAACs35FdPv9DSX68qm5McrS7z1fVDyV5dVW9LcnzklxdVU929227HRYAYL/bVVx195NVdTbJHVlcyN7df2Xr8aq6JclxYQUAHBYT73N1Oskr85XfEgQAOLR2e1ow3X1XNi9iv9Rj70nynt1+DQCAg8I7tAMADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAoCPrHmDLDdcfy7lTJ9Y9BgDArjhyBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAw6su4Btjz42IVs3HbP+Od95NSJ8c8JAPBMHLkCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBg0HOOq6o6W1WvvWjb26vqw1X18ap6qKo+WVVv2v2YAAAHw5FdPPd0kpNJPrpt28kk70jyhe5+uKquS3K+qj7a3U/s4msBABwIuzkteCbJiaq6OkmqaiPJdUnu7e6Hk6S7P5/ki0lesMs5AQAOhOccV939eJL7k9y82HQyyZ3d3Vv7VNWrklyd5HO7GRIA4KDY7QXtW6cGs/jv6a0HquqFSd6X5M3d/eVLPbmqbq2qc1V17umnLuxyFACA9dttXH0oyU1VdWOSo919Pkmq6pok9yR5Z3ff90xP7u7bu/t4dx+/6uixXY4CALB+u4qr7n4yydkkd2Rx1GpxDdYHk7y3u8/sekIAgANk4n2uTid5Zb5ySvCNSV6T5JaqemDx8S0DXwcAYN/bzVsxJEm6+64kte3++5O8f7efFwDgIPIO7QAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAw6Mi6B9hyw/XHcu7UiXWPAQCwK45cAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIOOrHuALQ8+diEbt91z2X0eOXViRdMAADw3jlwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAwSVwAAg8QVAMAgcQUAMEhcAQAMElcAAIPEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAwSFwBAAy6bFxV1dmqeu1F295eVR+uqo9X1UNV9cmqetO2x6uq/n5VfaaqPlVVP7hXwwMA7DdHdnj8dJKTST66bdvJJO9I8oXufriqrktyvqo+2t1PJLklyYuTvKy7v1xVXzc/NgDA/rTTacEzSU5U1dVJUlUbSa5Lcm93P5wk3f35JF9M8oLFc96a5Ee6+8uLx7+4B3MDAOxLl42r7n48yf1Jbl5sOpnkzu7urX2q6lVJrk7yucWmb0rypqo6tzh9+NJn+vxVdetiv3NPP3VhN+sAANgXlrmgfevUYBb/Pb31QFW9MMn7krx560hVkq9O8nvdfTzJTye545k+cXff3t3Hu/v4VUePPZf5AQD2lWXi6kNJbqqqG5Mc7e7zSVJV1yS5J8k7u/u+bfs/muTfLG5/MMkrBucFANjXdoyr7n4yydlsHoE6nSSLa7A+mOS93X3moqfcleTbF7f/fJLPTA0LALDfLfs+V6eTvDJfOSX4xiSvSXJLVT2w+PiWxWOnkry+qh5M8mNJvn9wXgCAfW2nt2JIknT3XUlq2/33J3n/M+z7RJITA7MBABw43qEdAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYdGTdA2y54fpjOXfqxLrHAADYFUeuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEFH1j3Algcfu5CN2+75g/uPnDqxxmkAAJ4bR64AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAZdNq6q6mxVvfaibW+vqndX1Ueq6omq+vmLHv+XVfWJqvpkVZ2pquftxeAAAPvRTkeuTic5edG2k4vt70ryfZd4zt/q7ld29yuS/GaSH9j1lAAAB8ROcXUmyYmqujpJqmojyXVJ7u3ujyX50sVP6O7fXexbSf5Qkp4cGABgP7tsXHX340nuT3LzYtPJJHd292WDqar+VZL/keRlSX7iMvvdWlXnqurc009deFaDAwDsR8tc0L791ODWKcHL6u43Z/MI16eSvOky+93e3ce7+/hVR48tMQoAwP62TFx9KMlNVXVjkqPdfX6ZT9zdTyf5QJLX72I+AIADZce46u4nk5xNckd2OGpVm16ydTvJ65L8l4E5AQAOhCNL7nc6yQez7TcHq+rebF5T9byqejTJW5L8YpKfqaprklSSTyR56+jEAAD72FJx1d13ZTOWtm979TPs/ud2ORMAwIHlHdoBAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYNCRdQ+w5Ybrj+XcqRPrHgMAYFccuQIAGCSuAAAGiSsAgEHiCgBgkLgCABgkrgAABokrAIBB4goAYJC4AgAYJK4AAAaJKwCAQeIKAGCQuAIAGCSuAAAGiSsAgEHiCgBgUHX3umdIklTVl5J8et1zrMG1SX573UOswWFdd3J4127dh89hXfthXXdyuNb+9d39gks9cGTVk1zGp7v7+LqHWLWqOmfdh8thXbt1Hz6Hde2Hdd3J4V77dk4LAgAMElcAAIP2U1zdvu4B1sS6D5/DunbrPnwO69oP67qTw732P7BvLmgHALgS7KcjVwAAB564AgAYtPK4qqrvqKpPV9Vnq+q2Szz+1VX1s4vH/2NVbax6xr2wxLpfU1X/uap+v6resI4Z98IS6/6hqvr1qvpkVX2sqr5+HXPuhSXW/ter6sGqeqCq/kNVvXwdc07bad3b9nt9VXVVXRG/tr3E631LVf3W4vV+oKq+fx1z7oVlXvOqeuPiz/pDVfWvVz3jXljiNf/xba/3Z6rqiTWMOW6Jdf+JqjpbVb+2+Nn+neuYc626e2UfSa5K8rkk35jk6iSfSPLyi/Z5W5J/trh9MsnPrnLGNa57I8krkrw3yRvWPfMK1/3tSY4ubr/1Sni9n8Xar9l2+3VJPrLuuVex7sV+X5vkV5Lcl+T4uude0et9S5KfXPesa1r7S5P8WpLnL+5/3brnXsW6L9r/bya5Y91zr+j1vj3JWxe3X57kkXXPveqPVR+5elWSz3b3b3T3/03ygSTfddE+35XkZxa3zyS5qapqhTPuhR3X3d2PdPcnk3x5HQPukWXWfba7n1rcvS/Ji1Y8415ZZu2/u+3uH05yJfx2yTJ/xpPkR5P8gyS/t8rh9tCy674SLbP2v5bkp7r7fyVJd39xxTPuhWf7mn9PktMrmWxvLbPuTnLN4vaxJJ9f4Xz7wqrj6vok/33b/UcX2y65T3f/fpILSf7oSqbbO8us+0r0bNf9liQf3tOJVmeptVfV36iqzyX5h0l+cEWz7aUd111VNyZ5cXffs8rB9tiy3+uvX5wmOVNVL17NaHtumbV/c5Jvrqpfrar7quo7Vjbd3ln659vicodvSPJLK5hrry2z7h9O8r1V9WiSX8jmUbtDxQXt7AtV9b1Jjid517pnWaXu/qnu/qYkfzfJ31v3PHutqr4qyT9O8rfXPcsa/NskG939iiS/mK8coT8MjmTz1OBfyOYRnJ+uqj+yzoFW7GSSM9399LoHWZHvSfKe7n5Rku9M8r7Fn/1DY9WLfSzJ9r+tvWix7ZL7VNWRbB5S/J2VTLd3lln3lWipdVfVX0zyziSv6+7/s6LZ9tqzfc0/kOQv7+VAK7LTur82yZ9O8stV9UiSP5vk7ivgovYdX+/u/p1t39//Ism3rWi2vbbM9/qjSe7u7v/X3f81yWeyGVsH2bP5M34yV8YpwWS5db8lyZ1J0t0fT/I12fwHnQ+NVcfVf0ry0qr6hqq6OpvfcHdftM/dSf7q4vYbkvxSL66KO8CWWfeVaMd1V9W3Jvnn2QyrK+E6jC3LrH37/1xOJHl4hfPtlcuuu7svdPe13b3R3RvZvM7udd19bj3jjlnm9X7htruvS/KpFc63l5b5+XZXNo9apaquzeZpwt9Y4Yx7Yamf61X1siTPT/LxFc+3V5ZZ928muSlJqupPZjOufmulU67bqq+gz+Yhws9k87cN3rnY9iPZ/AGbbL4IP5fks0nuT/KN677qf0Xr/jPZ/Nvd/87mkbqH1j3zitb975P8zyQPLD7uXvfMK1z7P0ny0GLdZ5P8qXXPvIp1X7TvL+cK+G3BJV/vH1u83p9YvN4vW/fMK1x7ZfN08K8neTDJyXXPvIp1L+7/cJJT6551xa/3y5P86uJ7/YEkf2ndM6/6wz9/AwAw6FBdYAYAsNfEFQDAIHEFADBIXAEADBJXAACDxBUAwCBxBQAw6P8DB/vyttd1YIkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'Feature': train_X.columns, 'Importance':\n",
    "                                   dt_model.best_estimator_.feature_importances_.ravel()}).sort_values(by = 'Importance')\n",
    "plt.figure(figsize=(10,7))\n",
    "# feature_importance.plot(kind = 'barh', x='Feature', y='Importance')\n",
    "plt.barh(feature_importance['Feature'], feature_importance['Importance'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters are: {'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "# fitting random forest model with hyperparameter tuning\n",
    "\n",
    "_, rf_model, rf_params = get_model('RandomForest')\n",
    "rf_model = GridSearchCV(rf_model, param_grid=rf_params, scoring=custom_scorer(), cv=PredefinedSplit(cv_indices))\n",
    "\n",
    "rf_model.fit(train_X, train_y)\n",
    "print('Best parameters are:', rf_model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_model.best_estimator_.predict(test_X)\n",
    "y_pred_probs = rf_model.best_estimator_.predict_proba(test_X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under Precision Recall curve is:  0.6396727674661061\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     42648\n",
      "           1       0.67      0.77      0.72        74\n",
      "\n",
      "    accuracy                           1.00     42722\n",
      "   macro avg       0.84      0.88      0.86     42722\n",
      "weighted avg       1.00      1.00      1.00     42722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Area Under Precision Recall curve is: ',au_pr_curve(test_y, y_pred_probs))\n",
    "print()\n",
    "get_report(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'Feature': train_X.columns, 'Importance':\n",
    "                                   rf_model.best_estimator_.feature_importances_.ravel()}).sort_values(by = 'Importance')\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.barh(feature_importance['Feature'], feature_importance['Importance'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **XGBoost**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters are: {'colsample_bytree': 0.6, 'eta': 0.01, 'gamma': 0, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'subsample': 0.6}\n"
     ]
    }
   ],
   "source": [
    "# fitting XGBoost model with hyperparameter tuning\n",
    "\n",
    "_, xgb_model, xgb_params = get_model('XGBoost')\n",
    "xgb_model = GridSearchCV(xgb_model, param_grid=xgb_params, scoring=custom_scorer(), cv=PredefinedSplit(cv_indices))\n",
    "\n",
    "xgb_model.fit(train_X, train_y)\n",
    "print('Best parameters are:', xgb_model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_model.best_estimator_.predict(test_X)\n",
    "y_pred_probs = xgb_model.best_estimator_.predict_proba(test_X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under Precision Recall curve is:  0.6126527253210121\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     42648\n",
      "           1       0.67      0.68      0.67        74\n",
      "\n",
      "    accuracy                           1.00     42722\n",
      "   macro avg       0.83      0.84      0.84     42722\n",
      "weighted avg       1.00      1.00      1.00     42722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Area Under Precision Recall curve is: ',au_pr_curve(test_y, y_pred_probs))\n",
    "print()\n",
    "get_report(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'Feature': train_X.columns, 'Importance':\n",
    "                                   rf_model.best_estimator_.feature_importances_.ravel()}).sort_values(by = 'Importance')\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.barh(feature_importance['Feature'], feature_importance['Importance'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}