Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "# Metrics\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>V9</th>\n",
       "      <th>...</th>\n",
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       "      <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",
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       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
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       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
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       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u> Exploratory Data Analysis </u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
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      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
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       "  </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>...</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>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>8 rows × 31 columns</p>\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  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-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  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-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",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  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  1.687098e-15 -3.666453e-16 -1.220404e-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  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(), cbar=False, yticklabels=False, cmap='viridis')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are no missing values in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    99.827251\n",
       "1     0.172749\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "print(sns.countplot(x='Class', data=data))\n",
    "data['Class'].value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset is highly imbalanced\n",
    "<h3> 99.83% Non-Fraudulent Transactions\n",
    "\n",
    "0.173% Fraudulent Transactions\n",
    "\n",
    "Hence, we require either oversampling or undersampling </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Amount & Time columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe10d8190>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(data['Amount'], bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Amount of transactions are generally very low, most are less than 1000 and max. tend to be around 1500 </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe129f550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing train test split\n",
    "\n",
    "Setting the stratify parameter of train_test_split to y <br> This will ensure that y_train and y_test will have approximately the same distribution of labels (i.e. Fraud and Non-Fraud Transactions) as you can see in the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Label Distribution for y_train: \n",
      "0    0.998271\n",
      "1    0.001729\n",
      "Name: Class, dtype: float64\n",
      "Class Label Distribution for y_test: \n",
      "0    0.99828\n",
      "1    0.00172\n",
      "Name: Class, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = data.drop('Class', axis=1)\n",
    "y = data['Class']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
    "    \n",
    "print(\"Class Label Distribution for y_train: \")\n",
    "print(y_train.value_counts(normalize=True))\n",
    "print(\"Class Label Distribution for y_test: \")\n",
    "print(y_test.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Thus we get train and test splits with the same mean value for the response variable (Class) </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While implementing a model on such a dataset is that instead of directly scaling the entire dataset using one of the scaler objects, the foll. steps should be performed:\n",
    "1. Perform train test split\n",
    "2. Fit the Scaler object on the train data\n",
    "3. Now scale the train as well as the test data on this Scaler that was fit on the <b> train data only. </b>\n",
    "\n",
    "So now, scaling is done using the information obtained from the training data only thereby reducing the bias that would been incurred had we directly scaled the entire dataset at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <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>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.725670</td>\n",
       "      <td>-0.793970</td>\n",
       "      <td>-3.645000</td>\n",
       "      <td>-0.518351</td>\n",
       "      <td>1.026088</td>\n",
       "      <td>-1.437866</td>\n",
       "      <td>1.341809</td>\n",
       "      <td>1.031605</td>\n",
       "      <td>-0.157574</td>\n",
       "      <td>1.551676</td>\n",
       "      <td>...</td>\n",
       "      <td>8.271199</td>\n",
       "      <td>2.025113</td>\n",
       "      <td>-0.455352</td>\n",
       "      <td>-4.309788</td>\n",
       "      <td>-0.270176</td>\n",
       "      <td>-0.449627</td>\n",
       "      <td>1.883583</td>\n",
       "      <td>-2.067556</td>\n",
       "      <td>1.715840</td>\n",
       "      <td>20.503002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.359760</td>\n",
       "      <td>0.400647</td>\n",
       "      <td>-0.155359</td>\n",
       "      <td>0.135604</td>\n",
       "      <td>0.905446</td>\n",
       "      <td>-0.130550</td>\n",
       "      <td>0.957745</td>\n",
       "      <td>-0.476659</td>\n",
       "      <td>0.816400</td>\n",
       "      <td>0.558361</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.086418</td>\n",
       "      <td>0.151230</td>\n",
       "      <td>0.258656</td>\n",
       "      <td>-0.377804</td>\n",
       "      <td>-0.498395</td>\n",
       "      <td>0.662619</td>\n",
       "      <td>-0.326981</td>\n",
       "      <td>0.526156</td>\n",
       "      <td>0.248630</td>\n",
       "      <td>0.637202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.304532</td>\n",
       "      <td>0.586570</td>\n",
       "      <td>-0.424407</td>\n",
       "      <td>0.376182</td>\n",
       "      <td>-0.400122</td>\n",
       "      <td>-1.091753</td>\n",
       "      <td>-0.695527</td>\n",
       "      <td>-0.593715</td>\n",
       "      <td>-0.442066</td>\n",
       "      <td>-0.551774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.968169</td>\n",
       "      <td>-1.180076</td>\n",
       "      <td>-0.966306</td>\n",
       "      <td>0.863743</td>\n",
       "      <td>0.860986</td>\n",
       "      <td>-0.215912</td>\n",
       "      <td>1.499290</td>\n",
       "      <td>-0.227934</td>\n",
       "      <td>0.112556</td>\n",
       "      <td>-0.042452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.573110</td>\n",
       "      <td>0.890299</td>\n",
       "      <td>-0.009214</td>\n",
       "      <td>-1.015970</td>\n",
       "      <td>0.763780</td>\n",
       "      <td>0.455613</td>\n",
       "      <td>-0.307861</td>\n",
       "      <td>0.345574</td>\n",
       "      <td>-0.336791</td>\n",
       "      <td>0.208883</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.906994</td>\n",
       "      <td>0.211287</td>\n",
       "      <td>0.258506</td>\n",
       "      <td>-0.111293</td>\n",
       "      <td>-0.685350</td>\n",
       "      <td>0.616175</td>\n",
       "      <td>-0.746072</td>\n",
       "      <td>-0.190353</td>\n",
       "      <td>-0.691376</td>\n",
       "      <td>-0.167714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.402521</td>\n",
       "      <td>-0.714899</td>\n",
       "      <td>-0.202645</td>\n",
       "      <td>0.339675</td>\n",
       "      <td>-1.293610</td>\n",
       "      <td>-0.312377</td>\n",
       "      <td>-0.652330</td>\n",
       "      <td>0.294218</td>\n",
       "      <td>0.521836</td>\n",
       "      <td>-1.019064</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537714</td>\n",
       "      <td>0.778600</td>\n",
       "      <td>0.317955</td>\n",
       "      <td>-0.472900</td>\n",
       "      <td>-0.074741</td>\n",
       "      <td>1.406823</td>\n",
       "      <td>-0.099172</td>\n",
       "      <td>0.463562</td>\n",
       "      <td>-0.344589</td>\n",
       "      <td>1.543779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -0.725670 -0.793970 -3.645000 -0.518351  1.026088 -1.437866  1.341809   \n",
       "1 -0.359760  0.400647 -0.155359  0.135604  0.905446 -0.130550  0.957745   \n",
       "2 -0.304532  0.586570 -0.424407  0.376182 -0.400122 -1.091753 -0.695527   \n",
       "3  0.573110  0.890299 -0.009214 -1.015970  0.763780  0.455613 -0.307861   \n",
       "4  0.402521 -0.714899 -0.202645  0.339675 -1.293610 -0.312377 -0.652330   \n",
       "\n",
       "         V7        V8        V9  ...       V20       V21       V22       V23  \\\n",
       "0  1.031605 -0.157574  1.551676  ...  8.271199  2.025113 -0.455352 -4.309788   \n",
       "1 -0.476659  0.816400  0.558361  ... -0.086418  0.151230  0.258656 -0.377804   \n",
       "2 -0.593715 -0.442066 -0.551774  ... -0.968169 -1.180076 -0.966306  0.863743   \n",
       "3  0.345574 -0.336791  0.208883  ... -0.906994  0.211287  0.258506 -0.111293   \n",
       "4  0.294218  0.521836 -1.019064  ...  1.537714  0.778600  0.317955 -0.472900   \n",
       "\n",
       "        V24       V25       V26       V27       V28     Amount  \n",
       "0 -0.270176 -0.449627  1.883583 -2.067556  1.715840  20.503002  \n",
       "1 -0.498395  0.662619 -0.326981  0.526156  0.248630   0.637202  \n",
       "2  0.860986 -0.215912  1.499290 -0.227934  0.112556  -0.042452  \n",
       "3 -0.685350  0.616175 -0.746072 -0.190353 -0.691376  -0.167714  \n",
       "4 -0.074741  1.406823 -0.099172  0.463562 -0.344589   1.543779  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "# Using RobustScaler as it is less prone to outliers\n",
    "rob_scaler = RobustScaler().fit(X_train)\n",
    "X_train = pd.DataFrame(rob_scaler.transform(X_train), columns=X.columns)\n",
    "X_test = pd.DataFrame(rob_scaler.transform(X_test), columns=X.columns)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.725670</td>\n",
       "      <td>-0.793970</td>\n",
       "      <td>-3.645000</td>\n",
       "      <td>-0.518351</td>\n",
       "      <td>1.026088</td>\n",
       "      <td>-1.437866</td>\n",
       "      <td>1.341809</td>\n",
       "      <td>1.031605</td>\n",
       "      <td>-0.157574</td>\n",
       "      <td>1.551676</td>\n",
       "      <td>...</td>\n",
       "      <td>8.271199</td>\n",
       "      <td>2.025113</td>\n",
       "      <td>-0.455352</td>\n",
       "      <td>-4.309788</td>\n",
       "      <td>-0.270176</td>\n",
       "      <td>-0.449627</td>\n",
       "      <td>1.883583</td>\n",
       "      <td>-2.067556</td>\n",
       "      <td>1.715840</td>\n",
       "      <td>20.503002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.359760</td>\n",
       "      <td>0.400647</td>\n",
       "      <td>-0.155359</td>\n",
       "      <td>0.135604</td>\n",
       "      <td>0.905446</td>\n",
       "      <td>-0.130550</td>\n",
       "      <td>0.957745</td>\n",
       "      <td>-0.476659</td>\n",
       "      <td>0.816400</td>\n",
       "      <td>0.558361</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.086418</td>\n",
       "      <td>0.151230</td>\n",
       "      <td>0.258656</td>\n",
       "      <td>-0.377804</td>\n",
       "      <td>-0.498395</td>\n",
       "      <td>0.662619</td>\n",
       "      <td>-0.326981</td>\n",
       "      <td>0.526156</td>\n",
       "      <td>0.248630</td>\n",
       "      <td>0.637202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.304532</td>\n",
       "      <td>0.586570</td>\n",
       "      <td>-0.424407</td>\n",
       "      <td>0.376182</td>\n",
       "      <td>-0.400122</td>\n",
       "      <td>-1.091753</td>\n",
       "      <td>-0.695527</td>\n",
       "      <td>-0.593715</td>\n",
       "      <td>-0.442066</td>\n",
       "      <td>-0.551774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.968169</td>\n",
       "      <td>-1.180076</td>\n",
       "      <td>-0.966306</td>\n",
       "      <td>0.863743</td>\n",
       "      <td>0.860986</td>\n",
       "      <td>-0.215912</td>\n",
       "      <td>1.499290</td>\n",
       "      <td>-0.227934</td>\n",
       "      <td>0.112556</td>\n",
       "      <td>-0.042452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.573110</td>\n",
       "      <td>0.890299</td>\n",
       "      <td>-0.009214</td>\n",
       "      <td>-1.015970</td>\n",
       "      <td>0.763780</td>\n",
       "      <td>0.455613</td>\n",
       "      <td>-0.307861</td>\n",
       "      <td>0.345574</td>\n",
       "      <td>-0.336791</td>\n",
       "      <td>0.208883</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.906994</td>\n",
       "      <td>0.211287</td>\n",
       "      <td>0.258506</td>\n",
       "      <td>-0.111293</td>\n",
       "      <td>-0.685350</td>\n",
       "      <td>0.616175</td>\n",
       "      <td>-0.746072</td>\n",
       "      <td>-0.190353</td>\n",
       "      <td>-0.691376</td>\n",
       "      <td>-0.167714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.402521</td>\n",
       "      <td>-0.714899</td>\n",
       "      <td>-0.202645</td>\n",
       "      <td>0.339675</td>\n",
       "      <td>-1.293610</td>\n",
       "      <td>-0.312377</td>\n",
       "      <td>-0.652330</td>\n",
       "      <td>0.294218</td>\n",
       "      <td>0.521836</td>\n",
       "      <td>-1.019064</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537714</td>\n",
       "      <td>0.778600</td>\n",
       "      <td>0.317955</td>\n",
       "      <td>-0.472900</td>\n",
       "      <td>-0.074741</td>\n",
       "      <td>1.406823</td>\n",
       "      <td>-0.099172</td>\n",
       "      <td>0.463562</td>\n",
       "      <td>-0.344589</td>\n",
       "      <td>1.543779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -0.725670 -0.793970 -3.645000 -0.518351  1.026088 -1.437866  1.341809   \n",
       "1 -0.359760  0.400647 -0.155359  0.135604  0.905446 -0.130550  0.957745   \n",
       "2 -0.304532  0.586570 -0.424407  0.376182 -0.400122 -1.091753 -0.695527   \n",
       "3  0.573110  0.890299 -0.009214 -1.015970  0.763780  0.455613 -0.307861   \n",
       "4  0.402521 -0.714899 -0.202645  0.339675 -1.293610 -0.312377 -0.652330   \n",
       "\n",
       "         V7        V8        V9  ...       V20       V21       V22       V23  \\\n",
       "0  1.031605 -0.157574  1.551676  ...  8.271199  2.025113 -0.455352 -4.309788   \n",
       "1 -0.476659  0.816400  0.558361  ... -0.086418  0.151230  0.258656 -0.377804   \n",
       "2 -0.593715 -0.442066 -0.551774  ... -0.968169 -1.180076 -0.966306  0.863743   \n",
       "3  0.345574 -0.336791  0.208883  ... -0.906994  0.211287  0.258506 -0.111293   \n",
       "4  0.294218  0.521836 -1.019064  ...  1.537714  0.778600  0.317955 -0.472900   \n",
       "\n",
       "        V24       V25       V26       V27       V28     Amount  \n",
       "0 -0.270176 -0.449627  1.883583 -2.067556  1.715840  20.503002  \n",
       "1 -0.498395  0.662619 -0.326981  0.526156  0.248630   0.637202  \n",
       "2  0.860986 -0.215912  1.499290 -0.227934  0.112556  -0.042452  \n",
       "3 -0.685350  0.616175 -0.746072 -0.190353 -0.691376  -0.167714  \n",
       "4 -0.074741  1.406823 -0.099172  0.463562 -0.344589   1.543779  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u> Hyperparameter Tuning of Logistic Regression Model using Random Search</u>\n",
    "\n",
    "# Basic Hyperparameter Tuning components\n",
    "Objective function: takes in hyperparameters and returns the cross validation score we want to maximize or minimize\n",
    "\n",
    "Domain of hyperparameters: values over which we want to search\n",
    "\n",
    "Algorithm: method for selecting the next hyperparameter values to evaluate in the objective function\n",
    "\n",
    "Results: history of hyperparameters and cross validation scores\n",
    "\n",
    "# Grid Search\n",
    "For a given model, it takes in the domain of hyperparameter values and checks every possible combination to find which settings perform the best on an evaluation score specified. Very time consuming.\n",
    "\n",
    "\n",
    "# Random Search\n",
    "It takes in the domain as well, but it randomly selects the set of hyperparameters from the search space. Results in more evenly spaced out hyperparameter values and gives similar results to Grid Search albeit a bit lower. \n",
    "Generally used on an unknown dataset in order to get rough estimate of the domain in which the model performs well and then Grid Search can be implemented to increase the accuracy further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Hyperparameters\n",
    "<h3> Parameters used for tuning are penalty and C only. </h3>\n",
    "\n",
    "Here is the docstring for the listed parameters\n",
    "\n",
    "1. penalty : str, 'l1', 'l2', 'elasticnet' or 'none', optional (default='l2')\n",
    "    Used to specify the norm used in the penalization. The 'newton-cg',\n",
    "    'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is\n",
    "    only supported by the 'saga' solver. If 'none' (not supported by the\n",
    "    liblinear solver), no regularization is applied.\n",
    "\n",
    "    .. versionadded:: 0.19\n",
    "       l1 penalty with SAGA solver (allowing 'multinomial' + L1)\n",
    "\n",
    "2. C : float, optional (default=1.0)\n",
    "    Inverse of regularization strength; must be a positive float.\n",
    "    Like in support vector machines, smaller values specify stronger\n",
    "    regularization.\n",
    "\n",
    "3. solver : str, {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'},              optional (default='liblinear').\n",
    "\n",
    "    Algorithm to use in the optimization problem.\n",
    "\n",
    "    - For small datasets, 'liblinear' is a good choice, whereas 'sag' and\n",
    "      'saga' are faster for large ones.\n",
    "    - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'\n",
    "      handle multinomial loss; 'liblinear' is limited to one-versus-rest\n",
    "      schemes.\n",
    "    - 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty\n",
    "    - 'liblinear' and 'saga' also handle L1 penalty\n",
    "    - 'saga' also supports 'elasticnet' penalty\n",
    "    - 'liblinear' does not handle no penalty\n",
    "\n",
    "    Note that 'sag' and 'saga' fast convergence is only guaranteed on\n",
    "    features with approximately the same scale. You can\n",
    "    preprocess the data with a scaler from sklearn.preprocessing.\n",
    "\n",
    "    .. versionadded:: 0.17\n",
    "       Stochastic Average Gradient descent solver.\n",
    "    .. versionadded:: 0.19\n",
    "       SAGA solver.\n",
    "    .. versionchanged:: 0.20\n",
    "        Default will change from 'liblinear' to 'lbfgs' in 0.22."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Hyperparameter grid dictionary: </h3>\n",
    "\n",
    "penalty takes values l1 and l2 which are the only two options available\n",
    "\n",
    "C is a list of 10 numbers containing the logspace from log10(0.001) to log10(1000) <br> Logspace is generally used for such parameters because we can cover a big range in a reasonable way in way fewer numbers as compared to something like a uniform distribution which is great for performing initial analysis and thus get a rough estimate of the best performing range for the parameter and then focus on it using other distributions.\n",
    "\n",
    "<h3> Setting up RandomizedSearchCV object </h3>\n",
    "We give the function, <br> the model for classification (LogisticRegression)<br> the hyperparameter grid (log_reg_params) <br> n_jobs = Number of jobs to run in parallel (-1 to use all processors) <br> n_iter trades off runtime vs quality of the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score=nan,\n",
       "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                dual=False, fit_intercept=True,\n",
       "                                                intercept_scaling=1,\n",
       "                                                l1_ratio=None, max_iter=100,\n",
       "                                                multi_class='auto', n_jobs=None,\n",
       "                                                penalty='l2', random_state=None,\n",
       "                                                solver='lbfgs', tol=0.0001,\n",
       "                                                verbose=0, warm_start=False),\n",
       "                   iid='deprecated', n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={'C': [0.001, 0.004641588833612777,\n",
       "                                              0.021544346900318832, 0.1,\n",
       "                                              0.46415888336127775,\n",
       "                                              2.154434690031882, 10.0,\n",
       "                                              46.41588833612773,\n",
       "                                              215.44346900318823, 1000.0],\n",
       "                                        'penalty': ['l1', 'l2']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# can also add the 'solver' parameter with values of liblinear and saga to the dictionary later 'solver': ['liblinear', 'saga']}\n",
    "\n",
    "rand_log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': list(np.logspace(np.log10(0.001), np.log10(1000), num=10))} \n",
    "# It contains the estimator (LogisticRegression in our case and also the dictionary of hyperparameters)\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), rand_log_reg_params, n_iter=50, verbose=5, n_jobs=-1)\n",
    "rand_log_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "# k-Fold Cross Validation Algorithm (used to prevent overfitting)\n",
    "1. Shuffle the dataset randomly.\n",
    "2. Split the dataset into k groups\n",
    "3. For each unique group:\n",
    "    \n",
    "    3.1. Take the group as a hold out or test data set\n",
    "    \n",
    "    3.2. Take the remaining groups as a training data set\n",
    "    \n",
    "    3.3. Fit a model on the training set and evaluate it on the test set\n",
    "    \n",
    "    3.4. Retain the evaluation score and discard the model\n",
    "    \n",
    "4. Summarize the skill of the model using the sample of model evaluation scores\n",
    "\n",
    "# Stratified k-Fold Cross Validation\n",
    "In stratified k-fold cross-validation, the folds are selected so that \"***the mean response value is approximately equal in all the folds***\". In the case of a dichotomous classification, this means that each fold contains roughly the same proportions of the two types of class labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining lists which will contain various metric values as cross-validation progresses\n",
    "accuracy_lst_log_reg = []\n",
    "precision_lst_log_reg = []\n",
    "recall_lst_log_reg = []\n",
    "f1_lst_log_reg = []\n",
    "auc_lst_log_reg = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling imbalance: Undersampling vs Oversampling\n",
    "<h3> Undersampling </h3>\n",
    "Undersampling techniques remove examples from the training dataset that belong to the majority class in order to better balance the class distribution, such as reducing the skew from a 1:100 to a 1:10, 1:2, or even a 1:1 class distribution. \n",
    "\n",
    "<h3> Oversampling </h3>\n",
    "This is different from oversampling that involves adding examples to the minority class in an effort to reduce the skew in the class distribution.\n",
    "\n",
    "Oversampling techniques are generally preferred over Undersampling techniques because they balance the dataset and at the same time do not result in loss of relevant data that Undersampling ends up doing. One of the primary reasons where undersampling comes into picture is when the data is very huge (containing sufficient minority examples) and minimizing cost of the project is a big factor."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SMOTE <a href=\"http://rikunert.com/SMOTE_explained\"> (Reference)</a>\n",
    "One of the most commonly used Oversampling techniques is <b> Synthetic Minority Oversampling Technique - SMOTE </b> <br>\n",
    "\n",
    "SMOTE synthesises new minority instances between existing (real) minority instances.\n",
    "\n",
    "Imagine that SMOTE draws lines between existing minority instances like this.\n",
    "<img src=\"static/SMOTE_Visualisation_1.jpg\">\n",
    "SMOTE then imagines new, synthetic minority instances somewhere on these lines.\n",
    "<img src=\"static/SMOTE_Visualisation_2.jpg\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling with Cross-Validation & Data Leakage\n",
    "You have to perform Oversampling within the Cross-Validation itself and not before it on the training set only, otherwise it would lead to data leakage. \n",
    "\n",
    "This is because it is possible that by Oversampling before, you are pretty much duplicating data from one fold to another, which could end up being in the test fold, and due to this, the test data is not really unseen and it could lead to overfitting.\n",
    "\n",
    "<h3> Oversampling before Cross Validation (Wrong Way) </h3>\n",
    "\n",
    "<img src=\"static/Oversampling + Cross Validation.jpg\">\n",
    "<h3> Oversampling during Cross Validation (Right Way) </h3>\n",
    "\n",
    "<img src=\"static/Oversampling in Cross Validation.jpg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   12.1s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   13.2s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   11.7s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   11.5s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   15.4s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Oversampling Training with Metric Calculations Completed\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Pipeline' object has no attribute 'best_est_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-9ebf08eca078>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cross Validation Oversampling Training with Metric Calculations Completed\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[0mmodel_file_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"logistic_regression.pkl\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0mjoblib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_est_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_file_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'Pipeline' object has no attribute 'best_est_'"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from imblearn.pipeline import make_pipeline as make_imbalanced_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "# Stratified 5 Fold\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None)\n",
    "\n",
    "smote_model = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "\n",
    "# When we fit data on the pipeline, it is first oversampled using SMOTE and then it is fit on the LogisticRegression\n",
    "# Model whose hyperparameters are stored in rand_log_reg\n",
    "# Also, Oversampling happens within the Cross-Validation, as explained above\n",
    "pipeline = make_imbalanced_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg)\n",
    "\n",
    "# Takes a lot of time to execute\n",
    "# train and test are a list of indices obtained from the StratifiedKFold().split() method\n",
    "# We perform iloc on the X and y dataframes to get X_train, X_test and y_train, y_test\n",
    "# and both y_test and y_train have same distribution of 0 and 1 class values, i.e, approx. 99.82% and 0.17% respectively\n",
    "for train, test in sss.split(X_train, y_train):\n",
    "    \n",
    "    # Time consuming step\n",
    "    model = pipeline.fit(X_train.iloc[train], y_train.iloc[train])\n",
    "    best_est_rand = rand_log_reg.best_estimator_\n",
    "        \n",
    "    # Testing the best estimator (best_est_rand) on the best performing set of parameters (inherently done by predict method)\n",
    "    prediction = best_est_rand.predict(X_train.iloc[test])\n",
    "    \n",
    "    # Appending metric scores to their respective list\n",
    "    accuracy_lst_log_reg.append(pipeline.score(X_train.iloc[test], y_train.iloc[test]))\n",
    "    precision_lst_log_reg.append(precision_score(y_train.iloc[test], prediction))\n",
    "    recall_lst_log_reg.append(recall_score(y_train.iloc[test], prediction))\n",
    "    f1_lst_log_reg.append(f1_score(y_train.iloc[test], prediction))\n",
    "    auc_lst_log_reg.append(roc_auc_score(y_train.iloc[test], prediction))\n",
    "\n",
    "print(\"Cross Validation Oversampling Training with Metric Calculations Completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Best C Parameters obtained were around 0.0046 - 50, so we'll focus on this range while implementing GridSearch\n",
    "<br><br>Best penalty obtained was l2 in all cases so I've used that for GridSearch </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 2.154434690031882,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_est_rand.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Printing the mean of the metric measures recorded in the lists </h2>\n",
    "\n",
    "<h3> Average Recall is 91% <br> <br> Average ROC AUC is 94% </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9749303254405406\n",
      "precision: 0.05917786515908787\n",
      "recall: 0.9037000973709833\n",
      "f1: 0.11104687598132393\n",
      "roc: 0.9393770359590426\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy:\", np.mean(accuracy_lst_log_reg))\n",
    "print(\"precision:\", np.mean(precision_lst_log_reg))\n",
    "print(\"recall:\", np.mean(recall_lst_log_reg))\n",
    "print(\"f1:\", np.mean(f1_lst_log_reg))\n",
    "print(\"roc:\", np.mean(auc_lst_log_reg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> On the actual test data X_test, we get Recall Score of 91% for Fraud Class</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "   No Fraud       1.00      0.98      0.93      0.99      0.95      0.91     56864\n",
      "      Fraud       0.06      0.93      0.98      0.12      0.95      0.90        98\n",
      "\n",
      "avg / total       1.00      0.98      0.93      0.99      0.95      0.91     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "rand_log_reg_predictions = best_est_rand.predict(X_test)\n",
    "print(classification_report_imbalanced(y_test, rand_log_reg_predictions, target_names=labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Average Precision-Recall Score on test data is 74% </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.76\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "# In the case of logistic regression, the predict_proba function returns the value of \"f(x)=1/1+e^(−(β0+β1x1+⋯+βkxk))\"\n",
    "# for given input X with k features\n",
    "\n",
    "# The decision function returns the value of \"d(x)=β0+β1x1+⋯+βkxk\"\n",
    "# The predict function returns 1 if value of predict_proba is > 0.5 else 0\n",
    "\n",
    "y_score = best_est_rand.predict_proba(X_test)[:, 1]\n",
    "average_precision = average_precision_score(y_test, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(average_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Plotting the Precision-Recall Curve </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x150a1dade20>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_score)\n",
    "\n",
    "precision_recall_df = pd.DataFrame({'precision': precision, 'recall': recall})\n",
    "\n",
    "plot_title = 'Precision-Recall Curve\\nAvg. Precision Recall Score=' + str(average_precision)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(x='recall',y='precision',data=precision_recall_df)\n",
    "plt.title(plot_title)\n",
    "plt.fill_between(recall, precision, color='lightblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC Curve\\nROC Score = 0.9854320333226143')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "\n",
    "false_positive_rate, true_positive_rate, _ = roc_curve(y_test,  y_score)\n",
    "\n",
    "auc = roc_auc_score(y_test, y_score)\n",
    "\n",
    "roc_df = pd.DataFrame({'fpr': false_positive_rate, 'tpr': true_positive_rate})\n",
    "\n",
    "plot_title = 'ROC Curve\\nROC Score = ' + str(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(x='fpr', y='tpr', data=roc_df)\n",
    "plt.fill_between(roc_df['fpr'], roc_df['tpr'], color='lightblue')\n",
    "plt.title(plot_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150a3a710a0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix_log_reg = confusion_matrix(y_true=y_test, y_pred=rand_log_reg_predictions)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(confusion_matrix_log_reg, annot=True, cmap='copper', fmt='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We got 91 True Positive Cases and only 9 False Negative Cases on the actual test data! </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Report for Logistic Regression with RandomSearch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for RandomSearchCV\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       1.00      0.98      0.93      0.99      0.95      0.91     56864\n",
      "          1       0.06      0.93      0.98      0.12      0.95      0.90        98\n",
      "\n",
      "avg / total       1.00      0.98      0.93      0.99      0.95      0.91     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report for RandomSearchCV\")\n",
    "print(classification_report_imbalanced(y_true=y_test, y_pred=rand_log_reg_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Random Forest (no Hyperparameter Tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('smote',\n",
       "                 SMOTE(k_neighbors=5, n_jobs=None, random_state=42,\n",
       "                       sampling_strategy='minority')),\n",
       "                ('randomforestclassifier',\n",
       "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                        class_weight=None, criterion='gini',\n",
       "                                        max_depth=None, max_features='auto',\n",
       "                                        max_leaf_nodes=None, max_samples=None,\n",
       "                                        min_impurity_decrease=0.0,\n",
       "                                        min_impurity_split=None,\n",
       "                                        min_samples_leaf=1, min_samples_split=2,\n",
       "                                        min_weight_fraction_leaf=0.0,\n",
       "                                        n_estimators=100, n_jobs=-1,\n",
       "                                        oob_score=False, random_state=None,\n",
       "                                        verbose=0, warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import make_pipeline as make_imbalanced_pipeline\n",
    "\n",
    "smote_model = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "random_forest_model = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "pipeline = make_imbalanced_pipeline(smote_model, random_forest_model)\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_predictions = random_forest_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe68f4700>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix_random_forest = confusion_matrix(y_true=y_test, y_pred=random_forest_predictions)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(confusion_matrix_random_forest, annot=True, cmap='copper', fmt='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We got 86 True Positive Cases and 12 False Negative Cases on the actual test data. So a little worse than the Logistic Regression Model. </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Report for Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       1.00      1.00      0.88      1.00      0.94      0.89     56864\n",
      "          1       0.90      0.88      1.00      0.89      0.94      0.87        98\n",
      "\n",
      "avg / total       1.00      1.00      0.88      1.00      0.94      0.89     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "print(classification_report_imbalanced(y_true=y_test, y_pred=random_forest_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> We got 88% recall for the Fraud Class </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u> Implementing Naive Bayes Classifier (no Hyperparameter Tuning) </u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('smote',\n",
       "                 SMOTE(k_neighbors=5, n_jobs=None, random_state=42,\n",
       "                       sampling_strategy='minority')),\n",
       "                ('gaussiannb', GaussianNB(priors=None, var_smoothing=1e-09))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import make_pipeline as make_imbalanced_pipeline\n",
    "\n",
    "smote_model = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "naive_bayes_model = GaussianNB()\n",
    "\n",
    "pipeline = make_imbalanced_pipeline(smote_model, naive_bayes_model)\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "naive_bayes_predictions = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bfe221cf70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I2CMi7oyI+yJidUScV42PjIilEfFw9XXfLnPOiYi1EfFgREzrMj4pIlZWz10UEVGND4+Ia6rxOyLikN7WZICRJKlwmY0dO2Ez8L7MPAp4NzA9IqYAZwPLMnMCsKz6nog4EpgNTASmAxdHxNDqWpcA84AJ1TG9Gp8LPJ+ZhwEXAhf0tiADjCRJhcsG//R5/U4vVd++oToSmAEsrMYXAjOrxzOAqzNzc2Y+BqwFJkfEGGDvzLw9O/e9rug2Z8u1rgOmbqnO9MQAI0lS4TqysSMi5kXEii7HvO6vERFDI+JeoBVYmpl3AKMzcz1A9XVUdfpY4Iku01uqsbHV4+7j28zJzDZgI7Dfjn5mm3glSSpco028mbkAWNDHOe3AuyPizcANEfGOXk7vqXKSvYz3NqdHVmAkSdJOy8wXgNvo7F15utoWovraWp3WAozvMm0c8FQ1Pq6H8W3mRMQwYB/guR2twwAjSVLh+ruJNyIOqCovRMQI4I+BNcBiYE512hzgxurxYmB2dWfRoXQ2695ZbTO9GBFTqv6WM7rN2XKtWcBPspfSkltIkiQVbgDeB2YMsLC6k2gIsCgzb4qI24FFETEXWAecXq1ndUQsAh4A2oD51RYUwJnA5cAI4JbqALgUuDIi1tJZeZnd24IMMJIkFa6/40tm3g8c3cP4BmDqDuacD5zfw/gKYLv+mcx8lSoA7Qy3kCRJUnGswEiSVLiOGn6UgAFGkqTC1TC/GGAkSSpdHT/M0QAjSVLh6hdfbOKVJEkFsgIjSVLhbOLtB3Hcef39EpIk1VoN84sVGEmSSmcTryRJKk4N84tNvJIkqTxWYCRJKlxHDW+kNsBIklS4Om4hGWAkSSpcHZt47YGRJEnFsQIjSVLhaliAMcBIklQ6m3glSVJxrMBIkqTi2MQrSZJUACswkiQVroYFGAOMJEmlq+MWkgFGkqTCdTR7AU1gD4wkSSqOFRhJkgrnFpIkSSpODfOLAUaSpNJZgZEkScXpqF9+sYlXkiSVxwqMJEmFSz/MUZIklaaGLTAGGEmSSmcTryRJKo5NvJIkSQWwAiNJUuFs4pUkScWpYQuMAUaSpNLVsYnXHhhJklQcKzCSJBWujnchGWAkSSpcHbeQDDCSJBWufvHFACNJUvHqWIGxiVeSJBXHCowkSYWrYQHGACNJUuk6aphgDDCSJBWufvHFACNJUvFs4pUkSSqAFRhJkgpXwwKMFRhJkkrXkdnQ0ZeIGB8Rt0bELyNidUScVY2PjIilEfFw9XXfLnPOiYi1EfFgREzrMj4pIlZWz10UEVGND4+Ia6rxOyLikN7WZICRJKlwmY0dO6EN+FxmHgFMAeZHxJHA2cCyzJwALKu+p3puNjARmA5cHBFDq2tdAswDJlTH9Gp8LvB8Zh4GXAhc0NuCDDCSJKlXmbk+M++uHr8I/BIYC8wAFlanLQRmVo9nAFdn5ubMfAxYC0yOiDHA3pl5e3Z2Hl/Rbc6Wa10HTN1SnemJAUaSpMJlg39+F9XWztHAHcDozFwPnSEHGFWdNhZ4osu0lmpsbPW4+/g2czKzDdgI7LejdRhgJEkqXKNbSBExLyJWdDnm9fQ6EbEncD3w2cz8TS9L6qlykr2M9zanR96FJElS4Rp9J97MXAAs6O2ciHgDneHlB5n5w2r46YgYk5nrq+2h1mq8BRjfZfo44KlqfFwP413ntETEMGAf4LkdrccKjCRJhevvJt6qF+VS4JeZ+Q9dnloMzKkezwFu7DI+u7qz6FA6m3XvrLaZXoyIKdU1z+g2Z8u1ZgE/yV7eoc8KjCRJ6svxwF8AKyPi3mrsi8DXgUURMRdYB5wOkJmrI2IR8ACddzDNz8z2at6ZwOXACOCW6oDOgHRlRKyls/Iyu7cFRX+//XBE1PDtdSRJdZaZO7x7pj+cNe2dDf1d+40frxzQ9e4KVmAkSSpcHd+J1wAjSVLhGm3iLZEBRpKkwtUwv3gXkiRJKo8VGEmSCtffN+QMRgYYSZIKV7/4YoCRJKl4dazA2AMjSZKKYwVGkqTCddSvAGOAkSSpdHXcQjLASJJUuBrmFwOMJEmlyxreh2QTryRJKo4VGEmSCmcTryRJKo5NvJIkqTg1zC8GGEmSSmcTryRJUgGswEiSVDibeCVJUnFs4pUkScWpYX6xB0aSJJXHCowkSYVzC0mSJBWno9kLaAIDjCRJhbMCI0mSilPD/GITryRJKo8BRts5/PDDueeee7YeGzdu5Kyzzmr2sqTd1mc+8xlWrlzJqlWrtv6uzZo1i1WrVtHe3s6kSZOavEINdpnZ0FEit5C0nYceeoijjz4agCFDhvDkk09yww03NHlV0u5p4sSJfOxjH2Py5Mm89tprLFmyhJtvvplVq1Zx2mmn8Z3vfKfZS1QByowgjbECo15NnTqVRx55hHXr1jV7KdJu6YgjjmD58uVs2rSJ9vZ2fvrTn3LqqaeyZs0aHnrooWYvT4XoyGzoKNHvHWAi4qO7ciEanGbPns1VV13V7GVIu61Vq1ZxwgknMHLkSEaMGMHJJ5/M+PHjm70sFSazsaNEjVRgztvRExExLyJWRMSKBq6vJnvDG97AKaecwrXXXtvspUi7rTVr1nDBBRewdOlSlixZwn333UdbW1uzlyUNer32wETE/Tt6Chi9o3mZuQBYUF2j0Gyn97///dx99920trY2eynSbu2yyy7jsssuA+D888+npaWlyStSaUptxG1EX028o4FpwPPdxgP4f/2yIg0aH/rQh9w+kgbAAQccwDPPPMP48eM57bTTOO6445q9JBWmhvmlzwBzE7BnZt7b/YmIuK1fVqRBYcSIEZx00kl8/OMfb/ZSpN3e9ddfz3777cfrr7/O/PnzeeGFF5g5cybf/OY3OeCAA7j55pu59957mT59erOXqkGqo4b3IUV/l53cQpIk1U1mxkC+3gePPrihv2tvuufxAV3vruD7wEiSVDi3kCRJUnFs4pUkScWpYX4xwEiSVLo6NvH6UQKSJKk4VmAkSSqcW0iSJKk4NvFKkqTi1DC/GGAkSSpdRw0TjE28kiSpOFZgJEkqXP3qLwYYSZKKZxOvJEkqTg3ziz0wkiSpPAYYSZIKl5kNHX2JiMsiojUiVnUZGxkRSyPi4errvl2eOyci1kbEgxExrcv4pIhYWT13UURENT48Iq6pxu+IiEP6WpMBRpKkwnVkY8dOuByY3m3sbGBZZk4AllXfExFHArOBidWciyNiaDXnEmAeMKE6tlxzLvB8Zh4GXAhc0NeCDDCSJBUuG/zT5/UzfwY81214BrCwerwQmNll/OrM3JyZjwFrgckRMQbYOzNvz86yzxXd5my51nXA1C3VmR0xwEiSVLjMxo7f0+jMXN/5+rkeGFWNjwWe6HJeSzU2tnrcfXybOZnZBmwE9uvtxQ0wkiTVXETMi4gVXY55jVyuh7HsZby3OTvkbdSSJBWu0feBycwFwILfcdrTETEmM9dX20Ot1XgLML7LeeOAp6rxcT2Md53TEhHDgH3YfstqG1ZgJEkq3AA08fZkMTCnejwHuLHL+OzqzqJD6WzWvbPaZnoxIqZU/S1ndJuz5VqzgJ9kH6nMCowkSYXbmUbcRkTEVcB7gP0jogX4MvB1YFFEzAXWAacDZObqiFgEPAC0AfMzs7261Jl03tE0ArilOgAuBa6MiLV0Vl5m97mm/n774Yio4fsDSpLqLDN7vYNmV5v81lEN/V175yOtA7reXcEKjCRJhavjRwkYYCRJKpwf5ihJkorTQCNusQwwkiQVro4VGG+jliRJxbECI0lS4epXfzHASJJUvDpuIRlgJEkqnE28kiSpOHWswNjEK0mSimMFRpKkwtWv/mKAkSSpeHXcQjLASJJUuBrmF3tgJElSeazASJJUuI4almAMMJIkFa6G+cUAI0lS6bKG9yEZYCRJKlwdKzA28UqSpOJYgZEkqXA28UqSpOLUML8YYCRJKp1NvJIkqTh1rMDYxCtJkopjBUaSpMLZxCtJkopTw/xigJEkqXRZwwRjD4wkSSqOFRhJkgpXv/qLAUaSpOLZxCtJkopTw/xigJEkqXQ28UqSJBXACowkSYWrYQHGACNJUun8MEdJklScjvrlFwOMJEmls4lXkiSpAFZgJEkqXA0LMAYYSZJKZxOvJEkqTh2beO2BkSRJxbECI0lS4ep4F5IBRpKkwtUwvxhgJEkqnRUYSZJUnI5mL6AJbOKVJEnFsQIjSVLh3EKSJEnFqWF+McBIklQ6KzCSJKk4NvFKkiQVwAqMJEmFcwtJkiQVp4b5hahjatPOi4h5mbmg2euQ6sbfPal39sCoL/OavQCppvzdk3phgJEkScUxwEiSpOIYYNQX9+Cl5vB3T+qFTbySJKk4VmAkSVJxDDDqUURMj4gHI2JtRJzd7PVIdRERl0VEa0SsavZapMHMAKPtRMRQ4FvA+4EjgQ9FxJHNXZVUG5cD05u9CGmwM8CoJ5OBtZn5aGa+BlwNzGjymqRayMyfAc81ex3SYGeAUU/GAk90+b6lGpMkaVAwwKgn0cOYt6tJkgYNA4x60gKM7/L9OOCpJq1FkqTtGGDUk18AEyLi0Ih4IzAbWNzkNUmStJUBRtvJzDbgU8CPgV8CizJzdXNXJdVDRFwF3A68LSJaImJus9ckDUa+E68kSSqOFRhJklQcA4wkSSqOAUaSJBXHACNJkopjgJEkScUxwEiSpOIYYCRJUnEMMJIkqTj/H9d4mev76YOiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix_random_forest = confusion_matrix(y_true=y_test, y_pred=naive_bayes_predictions)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(confusion_matrix_random_forest, annot=True, cmap='copper', fmt='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> We got 91 True Positive Cases and 7 False Negative Cases on the actual test data. So it performed the same as Logistic Regression which even used Hyperparameter Tuning and extensive cross-validation! </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Report for Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       1.00      0.97      0.93      0.99      0.95      0.91     56864\n",
      "          1       0.06      0.93      0.97      0.11      0.95      0.90        98\n",
      "\n",
      "avg / total       1.00      0.97      0.93      0.98      0.95      0.91     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.metrics import classification_report_imbalanced\n",
    "\n",
    "print(classification_report_imbalanced(y_true=y_test, y_pred=naive_bayes_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> We got a Recall of 93% for Fraud Class </h2>"
   ]
  }
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