Credit_Card_Fraud_Detection_Neural_Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported successfully\n"
     ]
    }
   ],
   "source": [
    "#importing libraries\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)//create  and \n",
    "#import data and also create a matrix of features with dependent variable\n",
    "from math import sqrt #inbuilt funtion\n",
    "\n",
    "import sklearn.utils\n",
    "from sklearn.preprocessing import RobustScaler #Scaling the features\n",
    "from sklearn.model_selection import StratifiedShuffleSplit #Splitting the dataset\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, make_scorer, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, precision_recall_curve, average_precision_score, auc\n",
    "from sklearn.model_selection import GridSearchCV #hyperparameter tuning\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "#Visual Analysis\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import seaborn as sns   #library for data visualisation based on matplotlib\n",
    "import IPython #command shell for interactive computing\n",
    "\n",
    "from imblearn.over_sampling import ADASYN #Adaptive Synthetic Oversampling\n",
    "from collections import Counter\n",
    "from scipy import stats\n",
    "\n",
    "#Neural Networks implementation\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import Adam #used to optimise the dataset in the neural network algorithm.\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasClassifier # kerasClasiifier is the function\n",
    "from keras.constraints import maxnorm\n",
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print('Imported successfully')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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>0</th>\n",
       "      <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",
       "      <td>-0.338321</td>\n",
       "      <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",
       "      <td>0.066928</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading data in a dataframe 'creditcard_data' and using head() to display the first 5 instances.\n",
    "creditcard_data= pd.read_csv(\"creditcard.csv\")\n",
    "creditcard_data.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard_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": [
    "creditcard_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard_data['Class'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of instances per class\n",
    "creditcard_data.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "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": [
    "creditcard_data.Class.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19aa19defd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Before sampling (PCA is performed for visualization only)\n",
    "pca= PCA(n_components=2)\n",
    "creditcard_2d= pd.DataFrame(pca.fit_transform(creditcard_data.iloc[:,0:30]))#locates rows,columns\n",
    "creditcard_2d= pd.concat([creditcard_2d, creditcard_data['Class']], axis=1)\n",
    "creditcard_2d.columns= ['x', 'y', 'Class']\n",
    "sns.lmplot(x='x', y='y', data=creditcard_2d, fit_reg=False, hue='Class',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>...</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>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-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  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-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  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-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   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-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   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.998273\n",
       "1    0.001727\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(creditcard_data.Class.value_counts())/(creditcard_data.Class.count())\n",
    "#checking the percentage of each class in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for feature Time\n",
    "# Plotting all the class value separatly with their datas\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))\n",
    "\n",
    "ax1.hist(creditcard_data[\"Time\"][creditcard_data[\"Class\"] == 1], bins = 50)\n",
    "ax1.set_title('Fraudulent')\n",
    "\n",
    "ax2.hist(creditcard_data[\"Time\"][creditcard_data[\"Class\"] == 0], bins = 50)\n",
    "ax2.set_title('Genuine')\n",
    "\n",
    "plt.xlabel('Seconds after transaction number zero')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      2.255124e+09\n",
       "V1        3.836489e+00\n",
       "V2        2.726820e+00\n",
       "V3        2.299029e+00\n",
       "V4        2.004684e+00\n",
       "V5        1.905081e+00\n",
       "V6        1.774946e+00\n",
       "V7        1.530401e+00\n",
       "V8        1.426479e+00\n",
       "V9        1.206992e+00\n",
       "V10       1.185594e+00\n",
       "V11       1.041855e+00\n",
       "V12       9.984034e-01\n",
       "V13       9.905708e-01\n",
       "V14       9.189055e-01\n",
       "V15       8.378034e-01\n",
       "V16       7.678191e-01\n",
       "V17       7.213734e-01\n",
       "V18       7.025394e-01\n",
       "V19       6.626619e-01\n",
       "V20       5.943254e-01\n",
       "V21       5.395255e-01\n",
       "V22       5.266428e-01\n",
       "V23       3.899507e-01\n",
       "V24       3.668084e-01\n",
       "V25       2.717308e-01\n",
       "V26       2.325429e-01\n",
       "V27       1.629192e-01\n",
       "V28       1.089550e-01\n",
       "Amount    6.256007e+04\n",
       "Class     1.724507e-03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creditcard_data.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Amount'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "#Boxplot for the Amount feature, in order to visualiza the outliers.\n",
    "sns.boxplot(x=creditcard_data['Class'], y=creditcard_data['Amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>Std_Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <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",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <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",
       "      <td>1.783274</td>\n",
       "    </tr>\n",
       "    <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>-0.638672</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",
       "      <td>0</td>\n",
       "      <td>-0.269825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <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>0.771679</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",
       "      <td>4.983721</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>0.005274</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",
       "      <td>1.418291</td>\n",
       "    </tr>\n",
       "    <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>0.798278</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",
       "      <td>0.670579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  ...       V22       V23       V24       V25       V26  \\\n",
       "0  0.098698  0.363787  ...  0.277838 -0.110474  0.066928  0.128539 -0.189115   \n",
       "1  0.085102 -0.255425  ... -0.638672  0.101288 -0.339846  0.167170  0.125895   \n",
       "2  0.247676 -1.514654  ...  0.771679  0.909412 -0.689281 -0.327642 -0.139097   \n",
       "3  0.377436 -1.387024  ...  0.005274 -0.190321 -1.175575  0.647376 -0.221929   \n",
       "4 -0.270533  0.817739  ...  0.798278 -0.137458  0.141267 -0.206010  0.502292   \n",
       "\n",
       "        V27       V28  Amount  Class  Std_Amount  \n",
       "0  0.133558 -0.021053  149.62      0    1.783274  \n",
       "1 -0.008983  0.014724    2.69      0   -0.269825  \n",
       "2 -0.055353 -0.059752  378.66      0    4.983721  \n",
       "3  0.062723  0.061458  123.50      0    1.418291  \n",
       "4  0.219422  0.215153   69.99      0    0.670579  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standardizing the Amount column (All other 'V' columns are already scaled as they've undergone PCA transformation).\n",
    "Rob_scaler=RobustScaler() #Robust to outliers\n",
    "creditcard_data['Std_Amount'] = Rob_scaler.fit_transform(creditcard_data['Amount'].values.reshape(-1, 1))\n",
    "creditcard_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Std_Amount</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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <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",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\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>...</td>\n",
       "      <td>-0.069083</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <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>...</td>\n",
       "      <td>0.524980</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\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>...</td>\n",
       "      <td>-0.208038</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\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>...</td>\n",
       "      <td>0.408542</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Std_Amount  Time        V1        V2        V3        V4        V5  \\\n",
       "0    1.783274   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321   \n",
       "1   -0.269825   0.0  1.191857  0.266151  0.166480  0.448154  0.060018   \n",
       "2    4.983721   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198   \n",
       "3    1.418291   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4    0.670579   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193   \n",
       "\n",
       "         V6        V7        V8  ...       V20       V21       V22       V23  \\\n",
       "0  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838 -0.110474   \n",
       "1 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672  0.101288   \n",
       "2  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679  0.909412   \n",
       "3  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274 -0.190321   \n",
       "4  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278 -0.137458   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop Amount and move Std_Amount at index '0'\n",
    "creditcard_data = creditcard_data.drop('Amount',axis=1)\n",
    "\n",
    "Std_Amount = creditcard_data['Std_Amount']\n",
    "creditcard_data.drop('Std_Amount', axis=1, inplace=True)\n",
    "creditcard_data.insert(0, 'Std_Amount', Std_Amount)\n",
    "creditcard_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [265518 180305  42664 ...  29062  13766  17677] Test: [263020  11378 147283 ... 274532 269819  64170]\n",
      "Train: [ 72227 114282  16818 ... 264471 191914 284017] Test: [202638  32978 128121 ... 244024 127667  48318]\n",
      "Train: [ 20895 114622 167683 ... 244502 178972 218506] Test: [284352  82483  90981 ... 171224 168807 271602]\n",
      "Train: [122248 181660 194400 ... 104631 277586  29432] Test: [225673  63348  68025 ... 279451  77554  76043]\n",
      "Train: [241684 223467 136928 ...  86495 160550  49633] Test: [157557 204860  83760 ... 251478 178967 216850]\n",
      "Train: [186996 144807  69756 ... 195673 152135 100500] Validation: [214502 127768  18878 ...  45194 143895 114668]\n",
      "Train: [203609 160516 170008 ... 168236  40608 156569] Validation: [152253  33885 217198 ... 219379 125093 195120]\n",
      "Train: [166473 135845 190847 ... 114436 217810 119523] Validation: [135986 136793 153361 ... 221564 222657 122137]\n",
      "Train: [ 16294 187750  97608 ... 157188 191303 201639] Validation: [125168  42557 218854 ... 117014  95701  40813]\n",
      "Train: [ 14162 152802 227171 ... 187465 177484    175] Validation: [ 81275 208151  56152 ...  58305 130407 190408]\n"
     ]
    }
   ],
   "source": [
    "#Splitting data before sampling\n",
    "#Splitting data into train and test set in 80% and 20% respectively, using Stratified Shuffle Split\n",
    "\n",
    "X = creditcard_data.drop('Class', axis=1)\n",
    "Y = creditcard_data['Class']\n",
    "\n",
    "sss1 = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)#default splitting value=5\n",
    "\n",
    "for train_index1, test_index in sss1.split(X, Y):\n",
    "    print(\"Train:\", train_index1, \"Test:\", test_index)\n",
    "    Xtrain, Xtest = X.iloc[train_index1], X.iloc[test_index]\n",
    "    Ytrain, Ytest = Y.iloc[train_index1], Y.iloc[test_index]\n",
    "\n",
    "#Splitting the train set further into train and validation set, which leaves train set 60% of the originial dataset and, test and validation sets 20% each respectively.\n",
    "sss2 = StratifiedShuffleSplit(n_splits=5, test_size=0.25, random_state=42)\n",
    "\n",
    "for train_index2, val_index in sss2.split(Xtrain, Ytrain):\n",
    "    print(\"Train:\", train_index2, \"Validation:\", val_index)\n",
    "    Xtrain_final, Xval = X.iloc[train_index2], X.iloc[val_index]\n",
    "    Ytrain_final, Yval = Y.iloc[train_index2], Y.iloc[val_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Distributions: \n",
      "\n",
      "[0.99807471 0.00192529]\n",
      "[0.99827955 0.00172045]\n",
      "[0.99845511 0.00154489]\n"
     ]
    }
   ],
   "source": [
    "# Check if the labels are distributed equally in all the datasets after splitting\n",
    "train_unique_label, train_counts_label = np.unique(Ytrain_final, return_counts=True)#removes the redundant value\n",
    "test_unique_label, test_counts_label = np.unique(Ytest, return_counts=True)\n",
    "val_unique_label, val_counts_label = np.unique(Yval, return_counts=True)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(Ytrain_final))\n",
    "print(test_counts_label/ len(Ytest))\n",
    "print(val_counts_label/ len(Yval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(170883, 30)\n",
      "(170883,)\n",
      "(56962, 30)\n",
      "(56962,)\n",
      "(56962, 30)\n",
      "(56962,)\n"
     ]
    }
   ],
   "source": [
    "#Dimensionality of the datasets retrieved after splitting\n",
    "print(Xtrain_final.shape)\n",
    "print(Ytrain_final.shape)\n",
    "print(Xtest.shape)\n",
    "print(Ytest.shape)\n",
    "print(Xval.shape)\n",
    "print(Yval.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Imbalanced Correlation Matrix')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking correlation between features and the likelihood of the transaction to be fraud on the unbalanced dataset\n",
    "f, ax1 = plt.subplots(figsize=(24,10))\n",
    "\n",
    "corr = creditcard_data.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Imbalanced Correlation Matrix\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Balancing the class starts here since the correlation matrix is affected by the high imbalance betwen the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled dataset shape Counter({1: 170602, 0: 170554})\n",
      "Shape of X_adasampled: (341156, 30)\n",
      "Shape of Y_adasampled: (341156,)\n"
     ]
    }
   ],
   "source": [
    "#Using ADASYN for Oversampling\n",
    "ada = ADASYN(sampling_strategy='minority', random_state=42)\n",
    "\n",
    "#Oversampling is applied only on the training set\n",
    "X_adasampled, Y_adasampled = ada.fit_resample(Xtrain_final, Ytrain_final)\n",
    "print('Resampled dataset shape %s' % Counter(Y_adasampled))\n",
    "print('Shape of X_adasampled: {}'.format(X_adasampled.shape))\n",
    "print('Shape of Y_adasampled: {}'.format(Y_adasampled.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Distributions: \n",
      "\n",
      "[0.49992965 0.50007035]\n"
     ]
    }
   ],
   "source": [
    "#check the disribution of both the labels\n",
    "train_label, train_count = np.unique(Y_adasampled, return_counts=True)\n",
    "print('Label Distributions: \\n')\n",
    "print(train_count/ len(Y_adasampled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Std_Amount      Time        V1        V2        V3        V4        V5  \\\n",
      "0   -0.286173   25183.0 -0.313555  0.586734  3.039981  2.848687 -0.158865   \n",
      "1    0.237546   97469.0  1.883183 -0.640580  0.094691  0.386752 -0.859323   \n",
      "2    1.170265  144962.0 -5.593796 -3.140554 -0.055757  1.539374  1.321195   \n",
      "3   -0.267729  140847.0 -0.851124  0.819195  2.014788 -0.634556 -0.166583   \n",
      "4    1.369384   58534.0 -1.339343  0.842304  2.210153  3.009876 -0.501056   \n",
      "\n",
      "         V6        V7        V8  ...       V19       V20       V21       V22  \\\n",
      "0  0.838171 -0.305911  0.136755  ...  0.211776  0.064667 -0.064730  0.538368   \n",
      "1  0.238667 -1.172713  0.238254  ... -0.228614 -0.191241 -0.063316  0.025427   \n",
      "2  1.449076  0.494910 -0.187489  ...  0.313058 -3.165442 -0.732926  1.617461   \n",
      "3  0.779931 -0.248282  0.440142  ...  0.608179  0.309633  0.133497  0.615617   \n",
      "4  1.506612  0.044767  0.574622  ...  1.033634  0.245178 -0.188658 -0.159114   \n",
      "\n",
      "        V23       V24       V25       V26       V27       V28  \n",
      "0  0.017643  0.514390 -0.970922 -0.131954  0.072962 -0.014056  \n",
      "1  0.346800  0.700140 -0.686270  0.387537 -0.060475 -0.044121  \n",
      "2  1.182141 -1.534997  0.203254 -0.308358 -0.982457 -0.584698  \n",
      "3 -0.475375  0.010686  0.582486 -0.302416  0.486000  0.209563  \n",
      "4 -0.241138 -0.402550  0.057283  0.231537  0.067849 -0.249524  \n",
      "\n",
      "[5 rows x 30 columns]\n",
      "   Class\n",
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n"
     ]
    }
   ],
   "source": [
    "Xsm_train_df = pd.DataFrame(X_adasampled)\n",
    "Xsm_train_df.columns = Xtrain.columns\n",
    "Ysm_train_df = pd.DataFrame(Y_adasampled)\n",
    "Ysm_train_df = Ysm_train_df.rename(columns={0: 'Class'})\n",
    "print(Xsm_train_df.head())\n",
    "print(Ysm_train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Std_Amount</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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.286173</td>\n",
       "      <td>25183.0</td>\n",
       "      <td>-0.313555</td>\n",
       "      <td>0.586734</td>\n",
       "      <td>3.039981</td>\n",
       "      <td>2.848687</td>\n",
       "      <td>-0.158865</td>\n",
       "      <td>0.838171</td>\n",
       "      <td>-0.305911</td>\n",
       "      <td>0.136755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064667</td>\n",
       "      <td>-0.064730</td>\n",
       "      <td>0.538368</td>\n",
       "      <td>0.017643</td>\n",
       "      <td>0.514390</td>\n",
       "      <td>-0.970922</td>\n",
       "      <td>-0.131954</td>\n",
       "      <td>0.072962</td>\n",
       "      <td>-0.014056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.237546</td>\n",
       "      <td>97469.0</td>\n",
       "      <td>1.883183</td>\n",
       "      <td>-0.640580</td>\n",
       "      <td>0.094691</td>\n",
       "      <td>0.386752</td>\n",
       "      <td>-0.859323</td>\n",
       "      <td>0.238667</td>\n",
       "      <td>-1.172713</td>\n",
       "      <td>0.238254</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.191241</td>\n",
       "      <td>-0.063316</td>\n",
       "      <td>0.025427</td>\n",
       "      <td>0.346800</td>\n",
       "      <td>0.700140</td>\n",
       "      <td>-0.686270</td>\n",
       "      <td>0.387537</td>\n",
       "      <td>-0.060475</td>\n",
       "      <td>-0.044121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.170265</td>\n",
       "      <td>144962.0</td>\n",
       "      <td>-5.593796</td>\n",
       "      <td>-3.140554</td>\n",
       "      <td>-0.055757</td>\n",
       "      <td>1.539374</td>\n",
       "      <td>1.321195</td>\n",
       "      <td>1.449076</td>\n",
       "      <td>0.494910</td>\n",
       "      <td>-0.187489</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.165442</td>\n",
       "      <td>-0.732926</td>\n",
       "      <td>1.617461</td>\n",
       "      <td>1.182141</td>\n",
       "      <td>-1.534997</td>\n",
       "      <td>0.203254</td>\n",
       "      <td>-0.308358</td>\n",
       "      <td>-0.982457</td>\n",
       "      <td>-0.584698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.267729</td>\n",
       "      <td>140847.0</td>\n",
       "      <td>-0.851124</td>\n",
       "      <td>0.819195</td>\n",
       "      <td>2.014788</td>\n",
       "      <td>-0.634556</td>\n",
       "      <td>-0.166583</td>\n",
       "      <td>0.779931</td>\n",
       "      <td>-0.248282</td>\n",
       "      <td>0.440142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.309633</td>\n",
       "      <td>0.133497</td>\n",
       "      <td>0.615617</td>\n",
       "      <td>-0.475375</td>\n",
       "      <td>0.010686</td>\n",
       "      <td>0.582486</td>\n",
       "      <td>-0.302416</td>\n",
       "      <td>0.486000</td>\n",
       "      <td>0.209563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.369384</td>\n",
       "      <td>58534.0</td>\n",
       "      <td>-1.339343</td>\n",
       "      <td>0.842304</td>\n",
       "      <td>2.210153</td>\n",
       "      <td>3.009876</td>\n",
       "      <td>-0.501056</td>\n",
       "      <td>1.506612</td>\n",
       "      <td>0.044767</td>\n",
       "      <td>0.574622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245178</td>\n",
       "      <td>-0.188658</td>\n",
       "      <td>-0.159114</td>\n",
       "      <td>-0.241138</td>\n",
       "      <td>-0.402550</td>\n",
       "      <td>0.057283</td>\n",
       "      <td>0.231537</td>\n",
       "      <td>0.067849</td>\n",
       "      <td>-0.249524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Std_Amount      Time        V1        V2        V3        V4        V5  \\\n",
       "0   -0.286173   25183.0 -0.313555  0.586734  3.039981  2.848687 -0.158865   \n",
       "1    0.237546   97469.0  1.883183 -0.640580  0.094691  0.386752 -0.859323   \n",
       "2    1.170265  144962.0 -5.593796 -3.140554 -0.055757  1.539374  1.321195   \n",
       "3   -0.267729  140847.0 -0.851124  0.819195  2.014788 -0.634556 -0.166583   \n",
       "4    1.369384   58534.0 -1.339343  0.842304  2.210153  3.009876 -0.501056   \n",
       "\n",
       "         V6        V7        V8  ...       V20       V21       V22       V23  \\\n",
       "0  0.838171 -0.305911  0.136755  ...  0.064667 -0.064730  0.538368  0.017643   \n",
       "1  0.238667 -1.172713  0.238254  ... -0.191241 -0.063316  0.025427  0.346800   \n",
       "2  1.449076  0.494910 -0.187489  ... -3.165442 -0.732926  1.617461  1.182141   \n",
       "3  0.779931 -0.248282  0.440142  ...  0.309633  0.133497  0.615617 -0.475375   \n",
       "4  1.506612  0.044767  0.574622  ...  0.245178 -0.188658 -0.159114 -0.241138   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  \n",
       "0  0.514390 -0.970922 -0.131954  0.072962 -0.014056      0  \n",
       "1  0.700140 -0.686270  0.387537 -0.060475 -0.044121      0  \n",
       "2 -1.534997  0.203254 -0.308358 -0.982457 -0.584698      0  \n",
       "3  0.010686  0.582486 -0.302416  0.486000  0.209563      0  \n",
       "4 -0.402550  0.057283  0.231537  0.067849 -0.249524      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging the Xsm_train_df and Ysm_train_df based on the index values to get a single dataframe in order to visualize a correlation matrix\n",
    "new_df= pd.merge(Xsm_train_df, Ysm_train_df, how='inner', left_index=True, right_index=True)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Balanced Correlation Matrix')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking correlation between features on the balanced dataset\n",
    "f, ax1 = plt.subplots(figsize=(24,10))\n",
    "\n",
    "corr = new_df.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Balanced Correlation Matrix\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19aa4fd86d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing balanced train dataset\n",
    "X_df= pd.DataFrame(X_adasampled)\n",
    "Y_df= pd.DataFrame(Y_adasampled)\n",
    "Y_df=Y_df.rename(columns={0: 'Class'})\n",
    "train_2d= pd.DataFrame(pca.fit_transform(X_df.iloc[:,0:29]))\n",
    "train_2d= pd.concat([train_2d, Y_df], axis=1)\n",
    "train_2d.columns= ['x', 'y', 'Class']\n",
    "sns.lmplot(x='x', y='y', data=train_2d, fit_reg=False, hue='Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Std_Amount</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>...</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14162</th>\n",
       "      <td>-0.286173</td>\n",
       "      <td>25183.0</td>\n",
       "      <td>-0.313555</td>\n",
       "      <td>0.586734</td>\n",
       "      <td>3.039981</td>\n",
       "      <td>2.848687</td>\n",
       "      <td>-0.158865</td>\n",
       "      <td>0.838171</td>\n",
       "      <td>-0.305911</td>\n",
       "      <td>0.136755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064667</td>\n",
       "      <td>-0.064730</td>\n",
       "      <td>0.538368</td>\n",
       "      <td>0.017643</td>\n",
       "      <td>0.514390</td>\n",
       "      <td>-0.970922</td>\n",
       "      <td>-0.131954</td>\n",
       "      <td>0.072962</td>\n",
       "      <td>-0.014056</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152802</th>\n",
       "      <td>0.237546</td>\n",
       "      <td>97469.0</td>\n",
       "      <td>1.883183</td>\n",
       "      <td>-0.640580</td>\n",
       "      <td>0.094691</td>\n",
       "      <td>0.386752</td>\n",
       "      <td>-0.859323</td>\n",
       "      <td>0.238667</td>\n",
       "      <td>-1.172713</td>\n",
       "      <td>0.238254</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.191241</td>\n",
       "      <td>-0.063316</td>\n",
       "      <td>0.025427</td>\n",
       "      <td>0.346800</td>\n",
       "      <td>0.700140</td>\n",
       "      <td>-0.686270</td>\n",
       "      <td>0.387537</td>\n",
       "      <td>-0.060475</td>\n",
       "      <td>-0.044121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227171</th>\n",
       "      <td>1.170265</td>\n",
       "      <td>144962.0</td>\n",
       "      <td>-5.593796</td>\n",
       "      <td>-3.140554</td>\n",
       "      <td>-0.055757</td>\n",
       "      <td>1.539374</td>\n",
       "      <td>1.321195</td>\n",
       "      <td>1.449076</td>\n",
       "      <td>0.494910</td>\n",
       "      <td>-0.187489</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.165442</td>\n",
       "      <td>-0.732926</td>\n",
       "      <td>1.617461</td>\n",
       "      <td>1.182141</td>\n",
       "      <td>-1.534997</td>\n",
       "      <td>0.203254</td>\n",
       "      <td>-0.308358</td>\n",
       "      <td>-0.982457</td>\n",
       "      <td>-0.584698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217316</th>\n",
       "      <td>-0.267729</td>\n",
       "      <td>140847.0</td>\n",
       "      <td>-0.851124</td>\n",
       "      <td>0.819195</td>\n",
       "      <td>2.014788</td>\n",
       "      <td>-0.634556</td>\n",
       "      <td>-0.166583</td>\n",
       "      <td>0.779931</td>\n",
       "      <td>-0.248282</td>\n",
       "      <td>0.440142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.309633</td>\n",
       "      <td>0.133497</td>\n",
       "      <td>0.615617</td>\n",
       "      <td>-0.475375</td>\n",
       "      <td>0.010686</td>\n",
       "      <td>0.582486</td>\n",
       "      <td>-0.302416</td>\n",
       "      <td>0.486000</td>\n",
       "      <td>0.209563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80544</th>\n",
       "      <td>1.369384</td>\n",
       "      <td>58534.0</td>\n",
       "      <td>-1.339343</td>\n",
       "      <td>0.842304</td>\n",
       "      <td>2.210153</td>\n",
       "      <td>3.009876</td>\n",
       "      <td>-0.501056</td>\n",
       "      <td>1.506612</td>\n",
       "      <td>0.044767</td>\n",
       "      <td>0.574622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245178</td>\n",
       "      <td>-0.188658</td>\n",
       "      <td>-0.159114</td>\n",
       "      <td>-0.241138</td>\n",
       "      <td>-0.402550</td>\n",
       "      <td>0.057283</td>\n",
       "      <td>0.231537</td>\n",
       "      <td>0.067849</td>\n",
       "      <td>-0.249524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Std_Amount      Time        V1        V2        V3        V4  \\\n",
       "14162    -0.286173   25183.0 -0.313555  0.586734  3.039981  2.848687   \n",
       "152802    0.237546   97469.0  1.883183 -0.640580  0.094691  0.386752   \n",
       "227171    1.170265  144962.0 -5.593796 -3.140554 -0.055757  1.539374   \n",
       "217316   -0.267729  140847.0 -0.851124  0.819195  2.014788 -0.634556   \n",
       "80544     1.369384   58534.0 -1.339343  0.842304  2.210153  3.009876   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V20       V21  \\\n",
       "14162  -0.158865  0.838171 -0.305911  0.136755  ...  0.064667 -0.064730   \n",
       "152802 -0.859323  0.238667 -1.172713  0.238254  ... -0.191241 -0.063316   \n",
       "227171  1.321195  1.449076  0.494910 -0.187489  ... -3.165442 -0.732926   \n",
       "217316 -0.166583  0.779931 -0.248282  0.440142  ...  0.309633  0.133497   \n",
       "80544  -0.501056  1.506612  0.044767  0.574622  ...  0.245178 -0.188658   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "14162   0.538368  0.017643  0.514390 -0.970922 -0.131954  0.072962 -0.014056   \n",
       "152802  0.025427  0.346800  0.700140 -0.686270  0.387537 -0.060475 -0.044121   \n",
       "227171  1.617461  1.182141 -1.534997  0.203254 -0.308358 -0.982457 -0.584698   \n",
       "217316  0.615617 -0.475375  0.010686  0.582486 -0.302416  0.486000  0.209563   \n",
       "80544  -0.159114 -0.241138 -0.402550  0.057283  0.231537  0.067849 -0.249524   \n",
       "\n",
       "        Class  \n",
       "14162       0  \n",
       "152802      0  \n",
       "227171      0  \n",
       "217316      0  \n",
       "80544       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#An undersampled dataset is created to tune the parameters using Grid-Search\n",
    "Ytrain_df = pd.DataFrame(Ytrain_final)\n",
    "Ytrain_df = Ytrain_df.rename(columns={0: 'Class'})\n",
    "Train_set= pd.merge(Xtrain_final, Ytrain_df, how='inner', left_index=True, right_index=True)\n",
    "Train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170883, 31)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length_frauds=len(Train_set[Train_set.Class==1]) #length of fraud\n",
    "length_frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Std_Amount</th>\n",
       "      <th>Time</th>\n",
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       "      <th>Class</th>\n",
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       "      <th>143188</th>\n",
       "      <td>-0.279466</td>\n",
       "      <td>85181.0</td>\n",
       "      <td>-3.003459</td>\n",
       "      <td>2.096150</td>\n",
       "      <td>-0.487030</td>\n",
       "      <td>3.069453</td>\n",
       "      <td>-1.774329</td>\n",
       "      <td>0.251804</td>\n",
       "      <td>-4.328776</td>\n",
       "      <td>-2.425478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692537</td>\n",
       "      <td>0.163967</td>\n",
       "      <td>1.245648</td>\n",
       "      <td>-0.269241</td>\n",
       "      <td>0.537102</td>\n",
       "      <td>-0.220757</td>\n",
       "      <td>-0.059555</td>\n",
       "      <td>0.460710</td>\n",
       "      <td>-0.033551</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>86155</th>\n",
       "      <td>1.110878</td>\n",
       "      <td>61108.0</td>\n",
       "      <td>-2.756007</td>\n",
       "      <td>0.683821</td>\n",
       "      <td>-1.390169</td>\n",
       "      <td>1.501887</td>\n",
       "      <td>-1.165614</td>\n",
       "      <td>-0.131207</td>\n",
       "      <td>-1.478741</td>\n",
       "      <td>-0.246922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.106945</td>\n",
       "      <td>0.320474</td>\n",
       "      <td>0.611027</td>\n",
       "      <td>0.174864</td>\n",
       "      <td>-0.502151</td>\n",
       "      <td>-0.174713</td>\n",
       "      <td>1.179242</td>\n",
       "      <td>-1.166315</td>\n",
       "      <td>0.821215</td>\n",
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       "      <td>-0.293440</td>\n",
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       "      <td>-4.153014</td>\n",
       "      <td>8.204797</td>\n",
       "      <td>-15.031714</td>\n",
       "      <td>10.330100</td>\n",
       "      <td>-3.994426</td>\n",
       "      <td>-3.250013</td>\n",
       "      <td>-10.415698</td>\n",
       "      <td>4.620804</td>\n",
       "      <td>...</td>\n",
       "      <td>1.412625</td>\n",
       "      <td>1.976988</td>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.485908</td>\n",
       "      <td>-1.198821</td>\n",
       "      <td>-0.526567</td>\n",
       "      <td>0.634874</td>\n",
       "      <td>1.627209</td>\n",
       "      <td>0.723235</td>\n",
       "      <td>1</td>\n",
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       "      <th>163586</th>\n",
       "      <td>0.600852</td>\n",
       "      <td>116067.0</td>\n",
       "      <td>0.949241</td>\n",
       "      <td>1.333519</td>\n",
       "      <td>-4.855402</td>\n",
       "      <td>1.835006</td>\n",
       "      <td>-1.053245</td>\n",
       "      <td>-2.562826</td>\n",
       "      <td>-2.286986</td>\n",
       "      <td>0.260906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424608</td>\n",
       "      <td>0.306598</td>\n",
       "      <td>-0.854627</td>\n",
       "      <td>0.101177</td>\n",
       "      <td>-0.281497</td>\n",
       "      <td>0.024444</td>\n",
       "      <td>-0.107944</td>\n",
       "      <td>0.625289</td>\n",
       "      <td>0.315677</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>223578</th>\n",
       "      <td>-0.183050</td>\n",
       "      <td>143438.0</td>\n",
       "      <td>-5.256434</td>\n",
       "      <td>0.364541</td>\n",
       "      <td>-5.412085</td>\n",
       "      <td>2.400031</td>\n",
       "      <td>0.697301</td>\n",
       "      <td>-1.998755</td>\n",
       "      <td>-5.835532</td>\n",
       "      <td>-0.887739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.403685</td>\n",
       "      <td>-0.648259</td>\n",
       "      <td>0.511285</td>\n",
       "      <td>-1.110045</td>\n",
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       "      <td>0.580487</td>\n",
       "      <td>-0.173630</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "        Std_Amount      Time        V1        V2         V3         V4  \\\n",
       "143188   -0.279466   85181.0 -3.003459  2.096150  -0.487030   3.069453   \n",
       "86155     1.110878   61108.0 -2.756007  0.683821  -1.390169   1.501887   \n",
       "9487     -0.293440   14073.0 -4.153014  8.204797 -15.031714  10.330100   \n",
       "163586    0.600852  116067.0  0.949241  1.333519  -4.855402   1.835006   \n",
       "223578   -0.183050  143438.0 -5.256434  0.364541  -5.412085   2.400031   \n",
       "\n",
       "              V5        V6         V7        V8  ...       V20       V21  \\\n",
       "143188 -1.774329  0.251804  -4.328776 -2.425478  ...  0.692537  0.163967   \n",
       "86155  -1.165614 -0.131207  -1.478741 -0.246922  ... -0.106945  0.320474   \n",
       "9487   -3.994426 -3.250013 -10.415698  4.620804  ...  1.412625  1.976988   \n",
       "163586 -1.053245 -2.562826  -2.286986  0.260906  ...  0.424608  0.306598   \n",
       "223578  0.697301 -1.998755  -5.835532 -0.887739  ... -0.403685 -0.648259   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "143188  1.245648 -0.269241  0.537102 -0.220757 -0.059555  0.460710 -0.033551   \n",
       "86155   0.611027  0.174864 -0.502151 -0.174713  1.179242 -1.166315  0.821215   \n",
       "9487    0.256510  0.485908 -1.198821 -0.526567  0.634874  1.627209  0.723235   \n",
       "163586 -0.854627  0.101177 -0.281497  0.024444 -0.107944  0.625289  0.315677   \n",
       "223578  0.511285 -1.110045  0.178987  0.220235 -0.699809  0.580487 -0.173630   \n",
       "\n",
       "        Class  \n",
       "143188      1  \n",
       "86155       1  \n",
       "9487        1  \n",
       "163586      1  \n",
       "223578      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking only Frauds in a dataframe\n",
    "fraud_df= Train_set.loc[Train_set['Class'] == 1]\n",
    "fraud_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(329, 31)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Taking only Non-frauds\n",
    "non_fraud_df= Train_set.loc[Train_set['Class'] == 0]\n",
    "#Randomly select the same number of records as the Frauds\n",
    "non_frauds=non_fraud_df.sample(n=length_frauds)\n",
    "non_frauds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>218341</th>\n",
       "      <td>0.530986</td>\n",
       "      <td>141275.0</td>\n",
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       "      <td>-0.365270</td>\n",
       "      <td>0.833738</td>\n",
       "      <td>0.019839</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.014066</td>\n",
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       "      <td>0.703757</td>\n",
       "      <td>0.122222</td>\n",
       "      <td>-0.050474</td>\n",
       "      <td>0.094505</td>\n",
       "      <td>0</td>\n",
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       "      <th>123270</th>\n",
       "      <td>-0.293440</td>\n",
       "      <td>76867.0</td>\n",
       "      <td>1.082566</td>\n",
       "      <td>1.094862</td>\n",
       "      <td>-1.367020</td>\n",
       "      <td>2.012554</td>\n",
       "      <td>0.708142</td>\n",
       "      <td>-0.807712</td>\n",
       "      <td>0.151952</td>\n",
       "      <td>0.158353</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.135789</td>\n",
       "      <td>-0.325284</td>\n",
       "      <td>-0.734344</td>\n",
       "      <td>-0.106725</td>\n",
       "      <td>-0.224999</td>\n",
       "      <td>0.569167</td>\n",
       "      <td>-0.335033</td>\n",
       "      <td>0.089140</td>\n",
       "      <td>0.112337</td>\n",
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       "      <th>193445</th>\n",
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       "      <td>130138.0</td>\n",
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       "      <td>0.218465</td>\n",
       "      <td>-0.861918</td>\n",
       "      <td>-0.435150</td>\n",
       "      <td>0.429523</td>\n",
       "      <td>-0.728059</td>\n",
       "      <td>1.217877</td>\n",
       "      <td>0.144056</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.596200</td>\n",
       "      <td>0.254156</td>\n",
       "      <td>0.953942</td>\n",
       "      <td>0.178601</td>\n",
       "      <td>-0.473259</td>\n",
       "      <td>0.183801</td>\n",
       "      <td>-0.141318</td>\n",
       "      <td>-0.038427</td>\n",
       "      <td>0.033587</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>112840</th>\n",
       "      <td>0.321805</td>\n",
       "      <td>72824.0</td>\n",
       "      <td>-1.111495</td>\n",
       "      <td>-0.257575</td>\n",
       "      <td>2.250210</td>\n",
       "      <td>1.152671</td>\n",
       "      <td>0.432904</td>\n",
       "      <td>1.254126</td>\n",
       "      <td>-0.584163</td>\n",
       "      <td>-0.609682</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.510614</td>\n",
       "      <td>0.862913</td>\n",
       "      <td>0.927825</td>\n",
       "      <td>-0.343058</td>\n",
       "      <td>-0.256268</td>\n",
       "      <td>-0.600742</td>\n",
       "      <td>-0.180331</td>\n",
       "      <td>0.026762</td>\n",
       "      <td>-0.358335</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>139382</th>\n",
       "      <td>0.385524</td>\n",
       "      <td>83126.0</td>\n",
       "      <td>-1.189721</td>\n",
       "      <td>0.869849</td>\n",
       "      <td>2.342483</td>\n",
       "      <td>3.155366</td>\n",
       "      <td>-0.656843</td>\n",
       "      <td>0.978601</td>\n",
       "      <td>-0.554871</td>\n",
       "      <td>0.825237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370836</td>\n",
       "      <td>0.165544</td>\n",
       "      <td>0.630473</td>\n",
       "      <td>-0.297924</td>\n",
       "      <td>-0.057551</td>\n",
       "      <td>0.444471</td>\n",
       "      <td>0.565400</td>\n",
       "      <td>0.247714</td>\n",
       "      <td>0.045279</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "        Std_Amount      Time        V1        V2        V3        V4  \\\n",
       "218341    0.530986  141275.0 -1.264154  0.426594  0.497832 -0.170862   \n",
       "123270   -0.293440   76867.0  1.082566  1.094862 -1.367020  2.012554   \n",
       "193445    2.164466  130138.0 -1.751949  0.218465 -0.861918 -0.435150   \n",
       "112840    0.321805   72824.0 -1.111495 -0.257575  2.250210  1.152671   \n",
       "139382    0.385524   83126.0 -1.189721  0.869849  2.342483  3.155366   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V20       V21  \\\n",
       "218341  1.157415 -0.365270  0.833738  0.019839  ...  0.099974 -0.203993   \n",
       "123270  0.708142 -0.807712  0.151952  0.158353  ... -0.135789 -0.325284   \n",
       "193445  0.429523 -0.728059  1.217877  0.144056  ... -0.596200  0.254156   \n",
       "112840  0.432904  1.254126 -0.584163 -0.609682  ... -0.510614  0.862913   \n",
       "139382 -0.656843  0.978601 -0.554871  0.825237  ...  0.370836  0.165544   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "218341 -0.443964  0.014066  0.647392  0.703757  0.122222 -0.050474  0.094505   \n",
       "123270 -0.734344 -0.106725 -0.224999  0.569167 -0.335033  0.089140  0.112337   \n",
       "193445  0.953942  0.178601 -0.473259  0.183801 -0.141318 -0.038427  0.033587   \n",
       "112840  0.927825 -0.343058 -0.256268 -0.600742 -0.180331  0.026762 -0.358335   \n",
       "139382  0.630473 -0.297924 -0.057551  0.444471  0.565400  0.247714  0.045279   \n",
       "\n",
       "        Class  \n",
       "218341      0  \n",
       "123270      1  \n",
       "193445      0  \n",
       "112840      1  \n",
       "139382      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Appending both Frauds and Non-Frauds\n",
    "undersampled_df= fraud_df.append(non_frauds)\n",
    "#Randomly shuffling all the instances\n",
    "undersampled_df = sklearn.utils.shuffle(undersampled_df)\n",
    "undersampled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(658, 30)\n",
      "(658,)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Std_Amount</th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <th>218341</th>\n",
       "      <td>0.530986</td>\n",
       "      <td>141275.0</td>\n",
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       "      <td>0.426594</td>\n",
       "      <td>0.497832</td>\n",
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       "      <td>-0.365270</td>\n",
       "      <td>0.833738</td>\n",
       "      <td>0.019839</td>\n",
       "      <td>...</td>\n",
       "      <td>1.315487</td>\n",
       "      <td>0.099974</td>\n",
       "      <td>-0.203993</td>\n",
       "      <td>-0.443964</td>\n",
       "      <td>0.014066</td>\n",
       "      <td>0.647392</td>\n",
       "      <td>0.703757</td>\n",
       "      <td>0.122222</td>\n",
       "      <td>-0.050474</td>\n",
       "      <td>0.094505</td>\n",
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       "      <th>123270</th>\n",
       "      <td>-0.293440</td>\n",
       "      <td>76867.0</td>\n",
       "      <td>1.082566</td>\n",
       "      <td>1.094862</td>\n",
       "      <td>-1.367020</td>\n",
       "      <td>2.012554</td>\n",
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       "      <td>-0.807712</td>\n",
       "      <td>0.151952</td>\n",
       "      <td>0.158353</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.880812</td>\n",
       "      <td>-0.135789</td>\n",
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       "      <td>-0.734344</td>\n",
       "      <td>-0.106725</td>\n",
       "      <td>-0.224999</td>\n",
       "      <td>0.569167</td>\n",
       "      <td>-0.335033</td>\n",
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       "      <th>193445</th>\n",
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       "      <td>0.218465</td>\n",
       "      <td>-0.861918</td>\n",
       "      <td>-0.435150</td>\n",
       "      <td>0.429523</td>\n",
       "      <td>-0.728059</td>\n",
       "      <td>1.217877</td>\n",
       "      <td>0.144056</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151479</td>\n",
       "      <td>-0.596200</td>\n",
       "      <td>0.254156</td>\n",
       "      <td>0.953942</td>\n",
       "      <td>0.178601</td>\n",
       "      <td>-0.473259</td>\n",
       "      <td>0.183801</td>\n",
       "      <td>-0.141318</td>\n",
       "      <td>-0.038427</td>\n",
       "      <td>0.033587</td>\n",
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       "    <tr>\n",
       "      <th>112840</th>\n",
       "      <td>0.321805</td>\n",
       "      <td>72824.0</td>\n",
       "      <td>-1.111495</td>\n",
       "      <td>-0.257575</td>\n",
       "      <td>2.250210</td>\n",
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       "      <td>-0.584163</td>\n",
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       "      <td>-0.510614</td>\n",
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       "      <td>-0.343058</td>\n",
       "      <td>-0.256268</td>\n",
       "      <td>-0.600742</td>\n",
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       "      <th>139382</th>\n",
       "      <td>0.385524</td>\n",
       "      <td>83126.0</td>\n",
       "      <td>-1.189721</td>\n",
       "      <td>0.869849</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "        Std_Amount      Time        V1        V2        V3        V4  \\\n",
       "218341    0.530986  141275.0 -1.264154  0.426594  0.497832 -0.170862   \n",
       "123270   -0.293440   76867.0  1.082566  1.094862 -1.367020  2.012554   \n",
       "193445    2.164466  130138.0 -1.751949  0.218465 -0.861918 -0.435150   \n",
       "112840    0.321805   72824.0 -1.111495 -0.257575  2.250210  1.152671   \n",
       "139382    0.385524   83126.0 -1.189721  0.869849  2.342483  3.155366   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V19       V20  \\\n",
       "218341  1.157415 -0.365270  0.833738  0.019839  ...  1.315487  0.099974   \n",
       "123270  0.708142 -0.807712  0.151952  0.158353  ... -0.880812 -0.135789   \n",
       "193445  0.429523 -0.728059  1.217877  0.144056  ... -0.151479 -0.596200   \n",
       "112840  0.432904  1.254126 -0.584163 -0.609682  ...  0.407188 -0.510614   \n",
       "139382 -0.656843  0.978601 -0.554871  0.825237  ...  1.284419  0.370836   \n",
       "\n",
       "             V21       V22       V23       V24       V25       V26       V27  \\\n",
       "218341 -0.203993 -0.443964  0.014066  0.647392  0.703757  0.122222 -0.050474   \n",
       "123270 -0.325284 -0.734344 -0.106725 -0.224999  0.569167 -0.335033  0.089140   \n",
       "193445  0.254156  0.953942  0.178601 -0.473259  0.183801 -0.141318 -0.038427   \n",
       "112840  0.862913  0.927825 -0.343058 -0.256268 -0.600742 -0.180331  0.026762   \n",
       "139382  0.165544  0.630473 -0.297924 -0.057551  0.444471  0.565400  0.247714   \n",
       "\n",
       "             V28  \n",
       "218341  0.094505  \n",
       "123270  0.112337  \n",
       "193445  0.033587  \n",
       "112840 -0.358335  \n",
       "139382  0.045279  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Separating labels from the other features\n",
    "X_undersample = undersampled_df.drop('Class', axis=1)\n",
    "Y_undersample = undersampled_df['Class']\n",
    "print(X_undersample.shape)\n",
    "print(Y_undersample.shape)\n",
    "X_undersample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting undersampled train sets in numpy arrays\n",
    "X_us_arr= X_undersample.values\n",
    "Y_us_arr= Y_undersample.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting test and validation dataframes in numpy arrays\n",
    "Xval_arr=Xval.values\n",
    "Yval_arr=Yval.values\n",
    "Xtest_arr=Xtest.values\n",
    "Ytest_arr=Ytest.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPERIMENTAL DESIGN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Multi-layer Perceptron with 1 hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "#Plotting number of repeats against the average score from those repeats.\n",
    "#Expected: As the number of repeats increase, the average score stabilizes\n",
    "f_score_list=[0.7627118644067796, 0.749003984063745, 0.7419354838709676, 0.7457627118644068, 0.7018867924528301, 0.7294117647058823, 0.6643109540636042, 0.753968253968254, 0.7018867924528301, 0.6888888888888889, 0.6478873239436619, 0.7430830039525692, 0.7410358565737051, 0.7479674796747967, 0.7175572519083968, 0.7634854771784232, 0.7265625, 0.6690647482014389, 0.7159533073929961, 0.7258064516129031, 0.7540983606557377, 0.7315175097276265, 0.7045454545454546, 0.7580645161290323, 0.7076923076923076, 0.6816479400749064, 0.7407407407407407, 0.7041198501872659, 0.7209302325581397, 0.7171314741035857, 0.6789667896678966, 0.7272727272727273, 0.732283464566929, 0.7583333333333334, 0.73015873015873, 0.7230769230769232, 0.7531380753138075, 0.7459016393442623, 0.6893939393939396, 0.7622950819672131, 0.7295081967213115, 0.7450980392156862, 0.7782426778242677, 0.6992481203007519, 0.748971193415638, 0.7126436781609196, 0.7364341085271319, 0.6842105263157894, 0.7317073170731707, 0.7848101265822784, 0.7104247104247104, 0.6666666666666666, 0.7591836734693878, 0.7401574803149606, 0.6946564885496183, 0.7272727272727273, 0.7175572519083968, 0.6946564885496183, 0.6940298507462687, 0.7109375, 0.6966292134831462, 0.7510204081632654, 0.7603305785123966, 0.6713780918727914, 0.7460317460317459, 0.7398373983739839, 0.7449392712550608, 0.673913043478261, 0.6881720430107526, 0.7551867219917012, 0.7215686274509804, 0.6789667896678966, 0.6966292134831462, 0.7551867219917012, 0.7666666666666667, 0.7215686274509804, 0.732283464566929, 0.732283464566929, 0.7181467181467182, 0.7250996015936256, 0.6791044776119403, 0.7109375, 0.7203065134099617, 0.7410358565737051, 0.7603305785123966, 0.681159420289855, 0.7551867219917012, 0.7368421052631579, 0.7142857142857143, 0.6416382252559727, 0.7294117647058823, 0.7531380753138075, 0.7698744769874477, 0.7131782945736435, 0.775, 0.5987261146496816, 0.7099236641221374, 0.7265625, 0.7372549019607844, 0.7280000000000001]\n",
    "f_df=pd.DataFrame(f_score_list)\n",
    "fscores= f_df.values\n",
    "final_mean= np.mean(fscores[0:101])\n",
    "means = list()\n",
    "for i in range(1,len(f_score_list)+1):\n",
    "    data = fscores[0:i, 0]\n",
    "    mean_fscore = np.mean(data)\n",
    "    means.append(mean_fscore)\n",
    "# line plot of the values generated in the list\n",
    "plt.plot(means)\n",
    "plt.plot([final_mean for x in range(len(means))])\n",
    "plt.show() #From this plot the point of diminishing returns can be located."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPLEMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for confusion matrix\n",
    "def conf_matrix(predicted_values):\n",
    "    Predictions_CM = confusion_matrix(Ytest_arr, predicted_values, labels = [0, 1])\n",
    "    class_feat=creditcard_data['Class'].copy()\n",
    "    class_feat= class_feat.unique()\n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    sns.heatmap(Predictions_CM, annot=True, fmt='d', xticklabels=class_feat, yticklabels=class_feat)\n",
    "    plt.ylabel('Actual Class')\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 65)                2015      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 66        \n",
      "=================================================================\n",
      "Total params: 2,081\n",
      "Trainable params: 2,081\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/30\n",
      "488/488 - 28s - loss: 296.6738 - accuracy: 0.6129 - val_loss: 2.0113 - val_accuracy: 0.9371\n",
      "Epoch 2/30\n",
      "488/488 - 1s - loss: 38.2817 - accuracy: 0.7819 - val_loss: 118.3269 - val_accuracy: 0.0590\n",
      "Epoch 3/30\n",
      "488/488 - 1s - loss: 29.0615 - accuracy: 0.8214 - val_loss: 1.1354 - val_accuracy: 0.9844\n",
      "Epoch 4/30\n",
      "488/488 - 1s - loss: 41.2375 - accuracy: 0.8128 - val_loss: 5.1217 - val_accuracy: 0.9158\n",
      "Epoch 5/30\n",
      "488/488 - 1s - loss: 30.8420 - accuracy: 0.8534 - val_loss: 1.4923 - val_accuracy: 0.9843\n",
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "#Training a Multi-layer perceptron with 1 hidden layer on Oversampled dataset without using dropout and, using the parameters tuned above.\n",
    "n_inputs = X_adasampled.shape[1]\n",
    "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                              min_delta=0,\n",
    "                              patience=2,\n",
    "                              verbose=0, mode='min', restore_best_weights= True)\n",
    "#Model Creation\n",
    "Model1 = Sequential()\n",
    "Model1.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
    "Model1.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "#Compile Model\n",
    "Model1.compile(Adam(lr=0.01), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "Model1.summary()\n",
    "\n",
    "#Fit Model\n",
    "history1= Model1.fit(X_adasampled, Y_adasampled, validation_data=(Xval_arr, Yval_arr), batch_size=700, epochs=30, callbacks=[es], shuffle=True, verbose=2)\n",
    "print(history1.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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"
    },
    {
     "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": [
    "# summarize history for accuracy\n",
    "plt.plot(history1.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history1.history['loss'])\n",
    "plt.plot(history1.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 65)                2015      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 65)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 66        \n",
      "=================================================================\n",
      "Total params: 2,081\n",
      "Trainable params: 2,081\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/40\n",
      "488/488 - 4s - loss: 3069.7834 - accuracy: 0.5101 - val_loss: 0.2365 - val_accuracy: 0.9974\n",
      "Epoch 2/40\n",
      "488/488 - 2s - loss: 22.3672 - accuracy: 0.5398 - val_loss: 0.0387 - val_accuracy: 0.9939\n",
      "Epoch 3/40\n",
      "488/488 - 2s - loss: 15.3606 - accuracy: 0.5401 - val_loss: 0.0669 - val_accuracy: 0.9960\n",
      "Epoch 4/40\n",
      "488/488 - 2s - loss: 9.0338 - accuracy: 0.5780 - val_loss: 33.3343 - val_accuracy: 0.0023\n",
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "#Training a Multi-layer perceptron with 1 hidden layer on Oversampled dataset using the parameters tuned above and adding a Dropout.\n",
    "n_inputs = X_adasampled.shape[1]\n",
    "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                              min_delta=0,\n",
    "                              patience=2,\n",
    "                              verbose=0, mode='min', restore_best_weights= True)\n",
    "Model1_drop = Sequential()\n",
    "Model1_drop.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
    "Model1_drop.add(Dropout(0.5))\n",
    "Model1_drop.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "Model1_drop.compile(Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "Model1_drop.summary()\n",
    "\n",
    "history2= Model1_drop.fit(X_adasampled, Y_adasampled, validation_data=(Xval_arr, Yval_arr), batch_size=700, epochs=40, callbacks=[es], shuffle=True, verbose=2)\n",
    "print(history2.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
    },
    {
     "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": [
    "plt.plot(history2.history['accuracy'])\n",
    "plt.plot(history2.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history2.history['loss'])\n",
    "plt.plot(history2.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 2s 1ms/step - loss: 0.0482 - accuracy: 0.9940: 0s - loss: 0.0485 - accuracy: \n",
      "Accuracy Model1 (Dropout): 0.9940486550331116\n",
      "Recall_score: 0.09183673469387756\n",
      "Precision_score: 0.03474903474903475\n",
      "F-score: 0.05042016806722689\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred_cls = Model1_drop.predict_classes(Xtest_arr, batch_size=200, verbose=0)\n",
    "print('Accuracy Model1 (Dropout): '+ str(Model1_drop.evaluate(Xtest_arr,Ytest_arr)[1]))\n",
    "print('Recall_score: ' + str(recall_score(Ytest_arr,Y_pred_cls)))\n",
    "print('Precision_score: ' + str(precision_score(Ytest_arr, Y_pred_cls)))\n",
    "print('F-score: ' + str(f1_score(Ytest_arr,Y_pred_cls)))\n",
    "conf_matrix(Y_pred_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "#Plotting ROC curve\n",
    "Y_pred_prob = Model1_drop.predict_proba(Xtest_arr).ravel()\n",
    "\n",
    "fpr_model1, tpr_model1, thresholds_model1 = roc_curve(Ytest_arr, Y_pred_prob, pos_label=1)\n",
    "auc_model1 = roc_auc_score(Ytest_arr, Y_pred_prob)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "#plot the roc curve for the model\n",
    "plt.plot(fpr_model1, tpr_model1, label='ROC (area = {:.3f})'.format(auc_model1))\n",
    "#plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "#Calculating Precision and Recall for various thresholds\n",
    "precision, recall, thresholds_pr = precision_recall_curve(Ytest_arr, Y_pred_prob)\n",
    "\n",
    "#Auc for PR curve\n",
    "AUC_PRcurve= auc(recall, precision)\n",
    "\n",
    "plt.figure(1)\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "#plot PR curve\n",
    "plt.plot(precision, recall, label = \"AUC = {:0.2f}\".format(AUC_PRcurve), lw = 3, alpha = 0.7)\n",
    "plt.xlabel('Precision', fontsize = 14)\n",
    "plt.ylabel('Recall', fontsize = 14)\n",
    "plt.title('Precision-Recall Curve', fontsize = 18)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1863354037267081, 0.07782101167315175, 0.0, 0.003436365867770044, 0.06646525679758308, 0.16759776536312848, 0.11698537682789649, 0.012396694214876033, 0.00522875816993464, 0.012987012987012986, 0.13149847094801223, 0.0, 0.11387900355871886, 0.0, 0.005099439061703212, 0.09691629955947136, 0.0, 0.10891089108910891, 0.06866952789699571, 0.0, 0.128, 0.0034417967583893795, 0.09036144578313253, 0.0, 0.08668730650154799, 0.0, 0.06039076376554174, 0.04329004329004328, 0.0034375712506796215, 0.12631578947368421, 0.06276150627615062, 0.0, 0.19424460431654675, 0.0, 0.1051502145922747, 0.0625, 0.04494382022471909, 0.08443271767810025, 0.08118081180811809, 0.10199556541019955, 0.0827250608272506, 0.09165302782324058, 0.01951219512195122, 0.05625000000000001, 0.11846689895470382, 0.10493343774471417, 0.035175879396984924, 0.0, 0.05945945945945946, 0.047619047619047616, 0.12030075187969924, 0.055900621118012424, 0.25000000000000006, 0.10526315789473684, 0.12987012987012986, 0.13061224489795917, 0.0956937799043062, 0.0862533692722372, 0.07586206896551723, 0.18631178707224333]\n",
      "FScore Mean: 0.07175373587822323\n",
      "----------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Training Multi-layer Perceptron with single hidden layer for 60 iterations and, appending the F-score for each iteration.\n",
    "f_scorelist1=[]\n",
    "\n",
    "n_inputs = X_adasampled.shape[1]\n",
    "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                              min_delta=0,\n",
    "                              patience=2,\n",
    "                              verbose=0, mode='min', restore_best_weights= True)\n",
    "for i in range(0,60):\n",
    "    First_Model = Sequential()\n",
    "    First_Model.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
    "    First_Model.add(Dropout(0.5))\n",
    "    First_Model.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "    First_Model.compile(Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    First_Model.fit(X_adasampled, Y_adasampled, validation_data=(Xval_arr, Yval_arr), batch_size=700, epochs=40, callbacks=[es], shuffle=True, verbose=0)\n",
    "    Y_first_pred = First_Model.predict_classes(Xtest_arr, batch_size=200, verbose=0)\n",
    "    \n",
    "    f_score= f1_score(Ytest_arr,Y_first_pred)\n",
    "    f_scorelist1.append(f_score)\n",
    "\n",
    "print(f_scorelist1)\n",
    "print('FScore Mean: '+ str(np.mean(f_scorelist1)))\n",
    "print('-'*88)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "488/488 - 5s - loss: 1173.2773 - accuracy: 0.4999 - val_loss: 1.1152 - val_accuracy: 0.0015\n",
      "Epoch 2/40\n",
      "488/488 - 3s - loss: 11.6953 - accuracy: 0.5014 - val_loss: 0.4644 - val_accuracy: 0.9307\n",
      "Epoch 3/40\n",
      "488/488 - 3s - loss: 5.0887 - accuracy: 0.5072 - val_loss: 1.2350 - val_accuracy: 0.0015\n",
      "Epoch 4/40\n",
      "488/488 - 3s - loss: 2.9305 - accuracy: 0.5116 - val_loss: 1.0020 - val_accuracy: 0.0015\n",
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "#Training Multi-layer perceptron with 2 hidden layers\n",
    "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                              min_delta=0,\n",
    "                              patience=2,\n",
    "                              verbose=0, mode='min', restore_best_weights= True)\n",
    "Model2 = Sequential()\n",
    "Model2.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
    "Model2.add(Dropout(0.5))\n",
    "Model2.add(Dense(65, kernel_initializer='he_normal', activation='relu'))\n",
    "Model2.add(Dropout(0.5))\n",
    "Model2.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "Model2.compile(Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "his_mod2= Model2.fit(X_adasampled, Y_adasampled, validation_data=(Xval_arr, Yval_arr), batch_size=700, epochs=40, callbacks=[es], shuffle=True, verbose=2)\n",
    "print(his_mod2.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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"
    },
    {
     "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": [
    "# summarize history for accuracy\n",
    "plt.plot(his_mod2.history['accuracy'])\n",
    "plt.plot(his_mod2.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(his_mod2.history['loss'])\n",
    "plt.plot(his_mod2.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 3s 1ms/step - loss: 0.4131 - accuracy: 0.9439\n",
      "Accuracy MLP: 0.943927526473999\n",
      "1781/1781 [==============================] - 3s 2ms/step - loss: 0.4131 - accuracy: 0.9439\n",
      "Loss value: 0.41309019923210144\n",
      "Recall_score: 0.12244897959183673\n",
      "Precision_score: 0.0038461538461538464\n",
      "F-score: 0.0074580484773151025\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Accuracy MLP: '+ str(Model2.evaluate(Xtest_arr,Ytest_arr)[1]))\n",
    "print('Loss value: '+ str(Model2.evaluate(Xtest_arr,Ytest_arr)[0]))\n",
    "\n",
    "Y_mod2_pred = Model2.predict_classes(Xtest_arr, batch_size=200, verbose=0)\n",
    "print('Recall_score: ' + str(recall_score(Ytest_arr,Y_mod2_pred)))\n",
    "print('Precision_score: ' + str(precision_score(Ytest_arr, Y_mod2_pred)))\n",
    "print('F-score: ' + str(f1_score(Ytest_arr,Y_mod2_pred)))\n",
    "conf_matrix(Y_mod2_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "Y_pred_prob2 = Model2.predict_proba(Xtest_arr).ravel()\n",
    "\n",
    "fpr_model2, tpr_model2, thresholds_model2 = roc_curve(Ytest_arr, Y_pred_prob2, pos_label=1)\n",
    "auc_model2 = roc_auc_score(Ytest_arr, Y_pred_prob2)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "#plot the roc curve for the model\n",
    "plt.plot(fpr_model1, tpr_model1, label='ROC Model_1 (area = {:.3f})'.format(auc_model1))\n",
    "plt.plot(fpr_model2, tpr_model2, label='ROC MOdel_2 (area = {:.3f})'.format(auc_model2))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "#Calculating Precision and Recall for various thresholds\n",
    "precision_2, recall_2, thresholds_pr_2 = precision_recall_curve(Ytest_arr, Y_pred_prob2)\n",
    "\n",
    "#Auc for PR curve\n",
    "AUC_PRcurve_2= auc(recall_2, precision_2)\n",
    "\n",
    "plt.figure(1)\n",
    "# plot no skill\n",
    "plt.plot([0, 1], [0.5, 0.5], linestyle='--')\n",
    "#plot PR curve\n",
    "plt.plot(precision, recall, label = \"AUC Model_1 = {:0.2f}\".format(AUC_PRcurve), lw = 3, alpha = 0.7)\n",
    "plt.plot(precision_2, recall_2, label = \"AUC Model_2 = {:0.2f}\".format(AUC_PRcurve_2), lw = 3, alpha = 0.7)\n",
    "plt.xlabel('Precision', fontsize = 14)\n",
    "plt.ylabel('Recall', fontsize = 14)\n",
    "plt.title('Precision-Recall Curve', fontsize = 18)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.00727537286285922, 0.006844626967830252, 0.0035837407241493483, 0.0, 0.0, 0.0071174377224199285, 0.0079734219269103, 0.0, 0.007677543186180423, 0.0014749262536873158, 0.0, 0.007207207207207208, 0.0, 0.003435101125170879, 0.0034350409225538483, 0.0034350409225538483, 0.006521739130434781, 0.0, 0.0, 0.0014858841010401186, 0.007079646017699115, 0.0034349807220469685, 0.0, 0.004094097117352932, 0.003435101125170879, 0.005698005698005698, 0.0, 0.0048963133640553, 0.007142857142857143, 0.0, 0.005326625730561515, 0.0, 0.007326007326007325, 0.006611570247933884, 0.0034349807220469685, 0.003578763293275585, 0.007349938750510413, 0.006560449859418931, 0.0, 0.0076238881829733185, 0.0066100094428706326, 0.004029333548231122, 0.007883326763894364, 0.007822685788787483, 0.007434944237918215, 0.006412997007268062, 0.0, 0.0, 0.0034349807220469685, 0.0, 0.00516795865633075, 0.0, 0.0, 0.0034349807220469685, 0.0, 0.007255139056831923, 0.003435101125170879, 0.007173601147776184, 0.0]\n",
      "FScore Mean: 0.00368592277620145\n",
      "----------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Iterating Model-2 with 2 hidden layer for 60 iterations.\n",
    "\n",
    "f_scorelist2=[]\n",
    "\n",
    "n_inputs = X_adasampled.shape[1]\n",
    "es= keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                              min_delta=0,\n",
    "                              patience=2,\n",
    "                              verbose=0, mode='min', restore_best_weights= True)\n",
    "for i in range(0,60):\n",
    "    \n",
    "    Second_Model = Sequential()\n",
    "    Second_Model.add(Dense(65, input_shape=(n_inputs, ), kernel_initializer='he_normal', activation='relu'))\n",
    "    Second_Model.add(Dropout(0.5))\n",
    "    Second_Model.add(Dense(65, kernel_initializer='he_normal', activation='relu'))\n",
    "    Second_Model.add(Dropout(0.5))\n",
    "    Second_Model.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "    Second_Model.compile(Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    Second_Model.fit(X_adasampled, Y_adasampled, validation_data=(Xval_arr, Yval_arr), batch_size=700, epochs=40, callbacks=[es], shuffle=True, verbose=0)\n",
    "    Y_second_pred = Second_Model.predict_classes(Xtest_arr, batch_size=200, verbose=0)\n",
    "    \n",
    "    f_score= f1_score(Ytest_arr,Y_second_pred)\n",
    "    f_scorelist2.append(f_score)\n",
    "\n",
    "print(f_scorelist2)\n",
    "print('FScore Mean: '+ str(np.mean(f_scorelist2)))\n",
    "print('-'*88)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model-1\n",
      "---------\n",
      "Average F-Score: 0.07175373587822323\n",
      "----------------------------------------\n",
      "Model-2\n",
      "---------\n",
      "Average F-Score: 0.00368592277620145\n"
     ]
    }
   ],
   "source": [
    "#Comparing the mean performance of the F-score for both the models\n",
    "print('Model-1')\n",
    "print('---------')\n",
    "print('Average F-Score: '+ str(np.mean(f_scorelist1)))\n",
    "\n",
    "print('-'*40)\n",
    "\n",
    "print('Model-2')\n",
    "print('---------')\n",
    "print('Average F-Score: '+ str(np.mean(f_scorelist2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model-1\n",
      "------------------\n",
      "F_score SD: 0    0.058809\n",
      "dtype: float64\n",
      "----------------------------------------\n",
      "Model-2\n",
      "------------------\n",
      "F_score SD: 0    0.003065\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Comparing the Standard Deviation for F-scores generated after iterations\n",
    "fscore_df1=pd.DataFrame(f_scorelist1)\n",
    "\n",
    "print('Model-1')\n",
    "print('------------------')\n",
    "print('F_score SD: ' + str(fscore_df1.std()))\n",
    "\n",
    "print('-'*40)\n",
    "\n",
    "fscore_df2=pd.DataFrame(f_scorelist2)\n",
    "\n",
    "print('Model-2')\n",
    "print('------------------')\n",
    "print('F_score SD: ' + str(fscore_df2.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "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": [
    "#Visualize the results using a boxplot()\n",
    "\n",
    "results_fscore= pd.concat([fscore_df1, fscore_df2], axis=1)\n",
    "results_fscore.columns = ['Model_1_fscore', 'Model_2_fscore']\n",
    "\n",
    "results_fscore.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model-1 Data is normal\n",
      "Model-2 Data is not normal\n"
     ]
    }
   ],
   "source": [
    "#Testing the Null Hypothesis that a sample comes from a Normal Distribution\n",
    "alpha = 0.05;\n",
    "\n",
    "s, p = stats.normaltest(fscore_df1)\n",
    "if p < alpha:\n",
    "  print('Model-1 Data is not normal')\n",
    "else:\n",
    "  print('Model-1 Data is normal')\n",
    "\n",
    "s, p = stats.normaltest(fscore_df2)\n",
    "if p < alpha:\n",
    "  print('Model-2 Data is not normal')\n",
    "else:\n",
    "  print('Model-2 Data is normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null hypothesis rejected, significant difference between the data-sets\n"
     ]
    }
   ],
   "source": [
    "#Wilcoxon Signed-Rank Test\n",
    "#It tests the null hypothesis that two related paired samples come from the same distribution.\n",
    "#It is a non-parametric version of the paired T-test.\n",
    "\n",
    "s, p = stats.wilcoxon(fscore_df1[0], fscore_df2[0])\n",
    "\n",
    "if p < 0.05:\n",
    "  print('null hypothesis rejected, significant difference between the data-sets')\n",
    "else:\n",
    "  print('null hypothesis accepted, no significant difference between the data-sets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
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