Credit_Card_Fraud_Detection.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minor Project : Tarun Raj Singh (B21CS076) and Aryan Himmatlal Prajapati (B21EE012)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> (PROJECT 2) TOPIC : CREDIT CARD FRAUD DETECTION </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Necessary Libraries and Modules\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from xgboost import XGBClassifier\n",
    "import lightgbm\n",
    "from sklearn.metrics import confusion_matrix as cm \n",
    "from sklearn.metrics import f1_score as f1\n",
    "from sklearn.metrics import recall_score as rs \n",
    "from sklearn.metrics import  precision_score as ps \n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import accuracy_score as acc\n",
    "from tabulate import tabulate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing and Reading dataset into Pandas DataFrame\n",
    "\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Instances of Class 0 ( not fraud ) :  284315\n",
      "Number of Instances of Class 1 ( fraud )     :  492\n",
      "_______________________________________________________\n",
      "Highly Imbalanced by ratio :  577.8760162601626\n"
     ]
    }
   ],
   "source": [
    "# Checking Number of Instances in each class \n",
    "\n",
    "num0 = df['Class'].value_counts()\n",
    "print(\"Number of Instances of Class 0 ( not fraud ) : \",num0[0])\n",
    "print(\"Number of Instances of Class 1 ( fraud )     : \",num0[1])\n",
    "print(\"_______________________________________________________\")\n",
    "print(\"Highly Imbalanced by ratio : \" ,num0[0]/num0[1])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Hence we can see that the dataset is highly Imbalanced as Instances in Class 0 >>>> Instances in Class 1 </h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 250000, 'Extremely High instances')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Distribution of data by number of Instances in each class using a Bar plot\n",
    "\n",
    "x = [\"0 : Not Fraud\",\"1 : Fraud\"]\n",
    "fig, ax = plt.subplots(figsize=(7,4.50))\n",
    "bars = ax.bar([0,1],num0,tick_label=x,alpha=0.75)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.yaxis.grid(True, color='#EEEEEE')\n",
    "\n",
    "bar_color = bars[0].get_facecolor()\n",
    "for bar in bars:\n",
    "  ax.text(\n",
    "      bar.get_x() + bar.get_width() / 2,\n",
    "      bar.get_height() + 8000,\n",
    "      round(bar.get_height(), 1),\n",
    "      horizontalalignment='center',\n",
    "      color=\"dimgrey\",\n",
    "      weight='bold'\n",
    "  )\n",
    "\n",
    "ax.set_xlabel('Class', labelpad=15, color='#333333',weight='bold')\n",
    "ax.set_ylabel('Number of Instances', labelpad=15, color='#333333',weight='bold')\n",
    "ax.set_title('Distribution of Data', pad=15, color='#333333',\n",
    "             weight='bold')\n",
    "low_point  = (1,20000)\n",
    "high_point = (0.18,290000)\n",
    "plt.annotate('Extremely Less instances', xy=low_point, xytext=(1.2,100000),\n",
    "             arrowprops=dict(facecolor='red', shrink=0.05))\n",
    "plt.annotate('Extremely High instances', xy=high_point, xytext=(0.5, 250000),\n",
    "            arrowprops=dict(facecolor='red', shrink=0.05))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If we apply any classifier Algorithm on this highly imbalanced dataset :"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Using Adaboost </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating out Features and labels\n",
    "x_data = df.drop(columns=['Class'])\n",
    "y_data = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into training and testing features and labels\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,random_state = 100,stratify = y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=42)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Adaboost Object\n",
    "\n",
    "Ada_clf = AdaBoostClassifier(random_state=42)\n",
    "Ada_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test data outcomes\n",
    "y_preds = Ada_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       0.78      0.68      0.73       123\n",
      "\n",
      "     accuracy                           1.00     71202\n",
      "    macro avg       0.89      0.84      0.86     71202\n",
      " weighted avg       1.00      1.00      1.00     71202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the Classification report \n",
    "class_names = [\"0 : Not Fraud\",\"1 : Fraud\"]\n",
    "print(classification_report(y_true=y_test,y_pred=y_preds,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b1b21570>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Imbalanced Data and AdaBoost\n",
    "cf_mat_ada_imb = cm(y_test,y_preds,labels=[0,1])\n",
    "disp_ada_imb = ConfusionMatrixDisplay(confusion_matrix=cf_mat_ada_imb,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_ada_imb.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> From the confusion matrix it is clear that the model is very poorly performing for the Class 1 (fraud) instances , due to the highly Imbalance in the data . </h5>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Using XGBoost </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating XGBboost Object \n",
    "\n",
    "XGB_clf = XGBClassifier(random_state=42)\n",
    "XGB_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test data outcomes\n",
    "y_preds_xgb = XGB_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       0.97      0.76      0.85       123\n",
      "\n",
      "     accuracy                           1.00     71202\n",
      "    macro avg       0.98      0.88      0.93     71202\n",
      " weighted avg       1.00      1.00      1.00     71202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the Classification report \n",
    "print(classification_report(y_true=y_test,y_pred=y_preds_xgb,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b1c16950>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Imbalanced Data and XGBoost\n",
    "cf_mat_xgb_imb = cm(y_test,y_preds_xgb,labels=[0,1])\n",
    "disp_xgb_imb = ConfusionMatrixDisplay(confusion_matrix=cf_mat_xgb_imb,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_xgb_imb.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h5> Here also we can clearly see from the confusion matrix that the model is very poor in classifying the class 1 (Fraud) Instances </h5>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Using LGBM </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making LGBM data (Imbalanced)\n",
    "params = {'num_leaves':50}\n",
    "train_data_imb = lightgbm.Dataset(x_train,label=y_train)\n",
    "test_data_imb = lightgbm.Dataset(x_test,label=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018350 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 213605, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.001727\n"
     ]
    }
   ],
   "source": [
    "# Trainign LGBM model on Imbalanced data\n",
    "LGBM_imb = lightgbm.train(params,train_data_imb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the results for Testing set\n",
    "LGBM_preds = LGBM_imb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Predictions to the nearest class labels\n",
    "lgbm_preds_imb = []\n",
    "for i in LGBM_preds:\n",
    "    if i <= 0.5:\n",
    "        lgbm_preds_imb.append(0)\n",
    "    else:\n",
    "        lgbm_preds_imb.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       0.96      0.78      0.86       123\n",
      "\n",
      "     accuracy                           1.00     71202\n",
      "    macro avg       0.98      0.89      0.93     71202\n",
      " weighted avg       1.00      1.00      1.00     71202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the classfication report\n",
    "print(classification_report(y_true=y_test,y_pred=lgbm_preds_imb,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b1b6aec0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Imbalanced Data and LGBM\n",
    "cf_mat_lgbm_imb = cm(y_test,lgbm_preds_imb,labels=[0,1])\n",
    "disp_lgbm_imb = ConfusionMatrixDisplay(confusion_matrix=cf_mat_lgbm_imb,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_lgbm_imb.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> As we can see that the overall Accuracy of all these models using the Imbalanced data is close to 1 , still the Precision , Recall and F1 score for class 1 is not good enough , this is due to the highly imbalanced data which is biased towards predicting class 0 , To improve this , we can apply various Imbalance handling algorithms.</h5>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Imbalance handling methods using Adaboost : "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 1. UnderSampling </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating Instances of each class\n",
    "cl_0_new1 = df[df['Class'] == 0]\n",
    "cl_1_new1 = df[df['Class'] == 1]\n",
    "\n",
    "# UnderSampling the Majority class (i.e , class 0)\n",
    "cl_0_new1 = cl_0_new1.sample(cl_1_new1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Concatinating the undersampled class 0 and original class 1 data and creating new Undersampled dataset having\n",
    " equal number of instances in each class (i.e , 492 instances for both classes)\"\"\"\n",
    "\n",
    "new_df1 = pd.concat([cl_0_new1,cl_1_new1],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting Data into Training and Testing sets\n",
    "x_data1 = new_df1.drop(columns=['Class'])\n",
    "y_data1 = new_df1['Class']\n",
    "\n",
    "x_train1,x_test1,y_train1,y_test1  = train_test_split(x_data1,y_data1,random_state=42,stratify=y_data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=100)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=100)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying Adaboost on this Undersampled Dataset\n",
    "Ada_under_1 = AdaBoostClassifier(random_state=100)\n",
    "Ada_under_1.fit(x_train1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "y_preds_under1 = Ada_under_1.predict(x_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       0.94      0.96      0.95       123\n",
      "    1 : Fraud       0.96      0.93      0.95       123\n",
      "\n",
      "     accuracy                           0.95       246\n",
      "    macro avg       0.95      0.95      0.95       246\n",
      " weighted avg       0.95      0.95      0.95       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report \n",
    "print(classification_report(y_true=y_test1,y_pred=y_preds_under1,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b2be9d20>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for UnderSampling Data and Adaboost\n",
    "cf_mat_under = cm(y_test1,y_preds_under1,labels=[0,1])\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cf_mat_under,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 2. OverSampling </h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating Instances of each class\n",
    "cl_0_new2 = df[df['Class'] == 0]\n",
    "cl_1_new2 = df[df['Class'] == 1]\n",
    "\n",
    "# OverSampling the Minority class (i.e , class 1)\n",
    "cl_new_1 = cl_1_new2.sample(cl_0_new2.shape[0],replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Concatinating the oversampled class 1 and original class 0 data and creating new Oversampled dataset having\n",
    "   equal number of instances in each class (i.e , 284315 instances for both classes)\"\"\"\n",
    "new_df2 = pd.concat([cl_0_new2,cl_new_1],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting Data into Training and Testing sets\n",
    "x_data2 = new_df2.drop(columns=['Class'])\n",
    "y_data2 = new_df2['Class']\n",
    "\n",
    "x_train2,x_test2,y_train2,y_test2  = train_test_split(x_data2,y_data2,random_state=42,stratify=y_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=100)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=100)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying Adaboost on this Oversampled Dataset\n",
    "Ada_over_1 = AdaBoostClassifier(random_state=100)\n",
    "Ada_over_1.fit(x_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "y_preds_over1 = Ada_over_1.predict(x_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       0.97      0.98      0.97     71079\n",
      "    1 : Fraud       0.98      0.97      0.97     71079\n",
      "\n",
      "     accuracy                           0.97    142158\n",
      "    macro avg       0.97      0.97      0.97    142158\n",
      " weighted avg       0.97      0.97      0.97    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report \n",
    "print(classification_report(y_true=y_test2,y_pred=y_preds_over1,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b2c11e10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for OverSampled Data and Adaboost\n",
    "cf_mat_over = cm(y_test2,y_preds_over1,labels=[0,1])\n",
    "disp_over = ConfusionMatrixDisplay(confusion_matrix=cf_mat_over,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_over.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 3. Synthetic Minority Oversampling Technique (SMOTE) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Sythetic data for the Minority Class (i.e , Class 1) and making instances in both classes = 284315\n",
    "smote_over = SMOTE(sampling_strategy='minority')\n",
    "x_syn , y_syn = smote_over.fit_resample(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into training and testing sets\n",
    "x_train_syn , x_test_syn , y_train_syn , y_test_syn = train_test_split(x_syn,y_syn,random_state=42,stratify=y_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=100)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=100)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying Adaboost on this Synthetic sampled Dataset\n",
    "\n",
    "Ada_syn_over = AdaBoostClassifier(random_state=100)\n",
    "Ada_syn_over.fit(x_train_syn,y_train_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "y_preds_syn = Ada_syn_over.predict(x_test_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       0.98      0.99      0.98     71079\n",
      "    1 : Fraud       0.99      0.98      0.98     71079\n",
      "\n",
      "     accuracy                           0.98    142158\n",
      "    macro avg       0.98      0.98      0.98    142158\n",
      " weighted avg       0.98      0.98      0.98    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report \n",
    "print(classification_report(y_true=y_test_syn,y_pred=y_preds_syn,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274e68e01f0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Synthetic Sampled Data and Adaboost\n",
    "cf_mat_syn = cm(y_test_syn,y_preds_syn,labels=[0,1])\n",
    "disp_syn = ConfusionMatrixDisplay(confusion_matrix=cf_mat_syn,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_syn.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Imbalance handling methods using XGBoost : "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1. Under Sampling</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying XGBoost on this Undersampled Dataset\n",
    "XGB_under = XGBClassifier(random_state = 2)\n",
    "XGB_under.fit(x_train1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "XGB_under_preds = XGB_under.predict(x_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       0.95      0.98      0.96       123\n",
      "    1 : Fraud       0.98      0.94      0.96       123\n",
      "\n",
      "     accuracy                           0.96       246\n",
      "    macro avg       0.96      0.96      0.96       246\n",
      " weighted avg       0.96      0.96      0.96       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report \n",
    "print(classification_report(y_true=y_test1,y_pred=XGB_under_preds,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b2a87340>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Synthetic Sampled Data and Adaboost\n",
    "cf_mat_under2 = cm(y_test1,XGB_under_preds,labels=[0,1])\n",
    "disp_under2 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_under2,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_under2.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2. OverSampling </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying XGBoost on this Oversampled Dataset\n",
    "XGB_over = XGBClassifier(random_state = 42)\n",
    "XGB_over.fit(x_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "XGB_over_preds = XGB_over.predict(x_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       1.00      1.00      1.00     71079\n",
      "\n",
      "     accuracy                           1.00    142158\n",
      "    macro avg       1.00      1.00      1.00    142158\n",
      " weighted avg       1.00      1.00      1.00    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report \n",
    "print(classification_report(y_true=y_test2,y_pred=XGB_over_preds,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2748a327bb0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for OverSampled Data and XGBoost\n",
    "cf_mat_over2 = cm(y_test2,XGB_over_preds,labels=[0,1])\n",
    "disp_over2 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_over2,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_over2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt = 0\n",
    "cnt2 = 0\n",
    "for i in range(len(XGB_over_preds)):\n",
    "    if (XGB_over_preds[i] == 1 and np.array(y_test2)[i] == 0):\n",
    "        cnt+=1\n",
    "    if (XGB_over_preds[i] == 0 and np.array(y_test2)[i] == 1):\n",
    "        cnt2+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud Instances Predicted as Fraud (Out of 71,079)   :  10\n",
      "Fraud Instances Predicted as Not Fraud (Out of 71,079)   :  0\n",
      "_________________________________________________________________\n",
      "\n",
      "Total Misclassified Instances (Out of 1,42,198)          :  10\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(\"Not Fraud Instances Predicted as Fraud (Out of 71,079)   : \",cnt)\n",
    "print(\"Fraud Instances Predicted as Not Fraud (Out of 71,079)   : \",cnt2)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(\"\")\n",
    "print(\"Total Misclassified Instances (Out of 1,42,198)          : \", cnt+cnt2)\n",
    "print(\"_________________________________________________________________\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 3. Synthetic Minority Oversampling Technique (SMOTE) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=2, ...)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying XGBoost on this Synthetic sampled Dataset\n",
    "\n",
    "XGB_smote = XGBClassifier(random_state = 2)\n",
    "XGB_smote.fit(x_train_syn,y_train_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test Instances\n",
    "XGB_syn_preds = XGB_smote.predict(x_test_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       1.00      1.00      1.00     71079\n",
      "\n",
      "     accuracy                           1.00    142158\n",
      "    macro avg       1.00      1.00      1.00    142158\n",
      " weighted avg       1.00      1.00      1.00    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=y_test_syn,y_pred=XGB_syn_preds,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274e68b3700>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Synthetic Sampled Data and XGBoost\n",
    "cf_mat_syn2 = cm(y_test_syn,XGB_syn_preds,labels=[0,1])\n",
    "disp_syn2 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_syn2,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_syn2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_xg_2 = 0\n",
    "cnt2_xg_2 = 0\n",
    "for i in range(len(XGB_syn_preds)):\n",
    "    if (XGB_syn_preds[i] == 1 and np.array(y_test_syn)[i] == 0):\n",
    "        cnt_xg_2+=1\n",
    "    if (XGB_syn_preds[i] == 0 and np.array(y_test_syn)[i] == 1):\n",
    "        cnt2_xg_2+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud Instances Predicted as Fraud (Out of 71,079)   :  28\n",
      "Fraud Instances Predicted as Not Fraud (Out of 71,079)   :  1\n",
      "_________________________________________________________________\n",
      "\n",
      "Total Misclassified Instances (Out of 1,42,198)          :  29\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(\"Not Fraud Instances Predicted as Fraud (Out of 71,079)   : \",cnt_xg_2)\n",
    "print(\"Fraud Instances Predicted as Not Fraud (Out of 71,079)   : \",cnt2_xg_2)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(\"\")\n",
    "print(\"Total Misclassified Instances (Out of 1,42,198)          : \", cnt_xg_2+cnt2_xg_2)\n",
    "print(\"_________________________________________________________________\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Imbalance handling methods using LGBM : "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1. Under Sampling</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making LGBM data (Under Sampled)\n",
    "params = {'num_leaves':50}\n",
    "train_data_under = lightgbm.Dataset(x_train1,label=y_train1)\n",
    "test_data_under = lightgbm.Dataset(x_test1,label=y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7384\n",
      "[LightGBM] [Info] Number of data points in the train set: 738, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.500000\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
     ]
    }
   ],
   "source": [
    "# Training LGBM model on this under sampled Data\n",
    "LGBM_under = lightgbm.train(params,train_data_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Predictions of test instances \n",
    "LGBM_under_preds = LGBM_under.predict(x_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Decimal values to nearest class\n",
    "lgbm_preds_under_fin = []\n",
    "for i in LGBM_under_preds:\n",
    "    if i <= 0.5:\n",
    "        lgbm_preds_under_fin.append(0)\n",
    "    else:\n",
    "        lgbm_preds_under_fin.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       0.95      0.98      0.96       123\n",
      "    1 : Fraud       0.98      0.94      0.96       123\n",
      "\n",
      "     accuracy                           0.96       246\n",
      "    macro avg       0.96      0.96      0.96       246\n",
      " weighted avg       0.96      0.96      0.96       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=y_test1,y_pred=lgbm_preds_under_fin,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2748a3ee290>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for underSampled Data and LGBM \n",
    "cf_mat_under3 = cm(y_test1,lgbm_preds_under_fin,labels=[0,1])\n",
    "disp_under3 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_under3,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_under3.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2. OverSampling </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making LGBM data (Over Sampled)\n",
    "params = {'num_leaves':50}\n",
    "train_data_over = lightgbm.Dataset(x_train2,label=y_train2)\n",
    "test_data_over = lightgbm.Dataset(x_test2,label=y_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044776 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7649\n",
      "[LightGBM] [Info] Number of data points in the train set: 426472, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.500000\n"
     ]
    }
   ],
   "source": [
    "# Training LGBM model on this Over sampled Data\n",
    "LGBM_over = lightgbm.train(params,train_data_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Predictions of test instances \n",
    "LGBM_over_preds = LGBM_over.predict(x_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Decimal values to nearest class\n",
    "lgbm_preds_over_fin = []\n",
    "for i in LGBM_over_preds:\n",
    "    if i <= 0.5:\n",
    "        lgbm_preds_over_fin.append(0)\n",
    "    else:\n",
    "        lgbm_preds_over_fin.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       1.00      1.00      1.00     71079\n",
      "\n",
      "     accuracy                           1.00    142158\n",
      "    macro avg       1.00      1.00      1.00    142158\n",
      " weighted avg       1.00      1.00      1.00    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=y_test2,y_pred=lgbm_preds_over_fin,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b2ad7fa0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for OverSampled Data and LGBM \n",
    "cf_mat_over3 = cm(y_test2,lgbm_preds_over_fin,labels=[0,1])\n",
    "disp_over3 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_over3,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_over3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_lgbm = 0\n",
    "cnt2_lgbm = 0\n",
    "for i in range(len(lgbm_preds_over_fin)):\n",
    "    if (lgbm_preds_over_fin[i] == 1 and np.array(y_test2)[i] == 0):\n",
    "        cnt_lgbm+=1\n",
    "    if (lgbm_preds_over_fin[i] == 0 and np.array(y_test2)[i] == 1):\n",
    "        cnt2_lgbm+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud Instances Predicted as Fraud (Out of 71,079)   :  69\n",
      "Fraud Instances Predicted as Not Fraud (Out of 71,079)   :  0\n",
      "_________________________________________________________________\n",
      "\n",
      "Total Misclassified Instances (Out of 1,42,198)          :  69\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(\"Not Fraud Instances Predicted as Fraud (Out of 71,079)   : \",cnt_lgbm)\n",
    "print(\"Fraud Instances Predicted as Not Fraud (Out of 71,079)   : \",cnt2_lgbm)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(\"\")\n",
    "print(\"Total Misclassified Instances (Out of 1,42,198)          : \", cnt_lgbm+cnt2_lgbm)\n",
    "print(\"_________________________________________________________________\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 3. Synthetic Minority Oversampling Technique (SMOTE) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making LGBM data (Synthetic Sampled using SMOTE)\n",
    "params = {'num_leaves':50}\n",
    "train_data_syn = lightgbm.Dataset(x_train_syn,label=y_train_syn)\n",
    "test_data_syn = lightgbm.Dataset(x_test_syn,label=y_test_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027519 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 426472, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.500000\n"
     ]
    }
   ],
   "source": [
    "# Training LGBM model on this Synthetic sampled Data\n",
    "LGBM_syn = lightgbm.train(params,train_data_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Predictions of test instances \n",
    "LGBM_syn_preds = LGBM_syn.predict(x_test_syn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Decimal values to nearest class\n",
    "lgbm_preds_syn_fin = []\n",
    "for i in LGBM_syn_preds:\n",
    "    if i <= 0.5:\n",
    "        lgbm_preds_syn_fin.append(0)\n",
    "    else:\n",
    "        lgbm_preds_syn_fin.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00     71079\n",
      "    1 : Fraud       1.00      1.00      1.00     71079\n",
      "\n",
      "     accuracy                           1.00    142158\n",
      "    macro avg       1.00      1.00      1.00    142158\n",
      " weighted avg       1.00      1.00      1.00    142158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=y_test_syn,y_pred=lgbm_preds_syn_fin,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x274b2a85750>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Synthetic Sampled Data and LGBM \n",
    "cf_mat_syn3 = cm(y_test_syn,lgbm_preds_syn_fin,labels=[0,1])\n",
    "disp_syn3 = ConfusionMatrixDisplay(confusion_matrix=cf_mat_syn3,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_syn3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_lgbm2 = 0\n",
    "cnt2_lgbm2 = 0\n",
    "for i in range(len(lgbm_preds_syn_fin)):\n",
    "    if (lgbm_preds_syn_fin[i] == 1 and np.array(y_test_syn)[i] == 0):\n",
    "        cnt_lgbm2+=1\n",
    "    if (lgbm_preds_syn_fin[i] == 0 and np.array(y_test_syn)[i] == 1):\n",
    "        cnt2_lgbm2+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud Instances Predicted as Fraud (Out of 71,079)   :  122\n",
      "Fraud Instances Predicted as Not Fraud (Out of 71,079)   :  7\n",
      "_________________________________________________________________\n",
      "\n",
      "Total Misclassified Instances (Out of 1,42,198)          :  129\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(\"Not Fraud Instances Predicted as Fraud (Out of 71,079)   : \",cnt_lgbm2)\n",
    "print(\"Fraud Instances Predicted as Not Fraud (Out of 71,079)   : \",cnt2_lgbm2)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(\"\")\n",
    "print(\"Total Misclassified Instances (Out of 1,42,198)          : \", cnt_lgbm2+cnt2_lgbm2)\n",
    "print(\"_________________________________________________________________\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots for Performance on different model selections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####    For AdaBoost     #####\n",
    "\n",
    "f1_s_ada = []\n",
    "ps_s_ada = []\n",
    "rs_s_ada = []\n",
    "acc_s_ada = []\n",
    "\n",
    "f1_s_ada = [f1(y_test,y_preds),f1(y_test1,y_preds_under1),f1(y_test2,y_preds_over1),f1(y_test_syn,y_preds_syn)]\n",
    "ps_s_ada = [ps(y_test,y_preds),ps(y_test1,y_preds_under1),ps(y_test2,y_preds_over1),ps(y_test_syn,y_preds_syn)]\n",
    "rs_s_ada = [rs(y_test,y_preds),rs(y_test1,y_preds_under1),rs(y_test2,y_preds_over1),rs(y_test_syn,y_preds_syn)]\n",
    "acc_s_ada = [acc(y_test,y_preds),acc(y_test1,y_preds_under1),acc(y_test2,y_preds_over1),acc(y_test_syn,y_preds_syn)]\n",
    "\n",
    "f1_s_ada = np.round(np.array(f1_s_ada), 3)\n",
    "ps_s_ada = np.round(np.array(ps_s_ada), 3)\n",
    "rs_s_ada = np.round(np.array(rs_s_ada), 3)\n",
    "acc_s_ada = np.round(np.array(acc_s_ada), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####    For XGBoost     #####\n",
    "\n",
    "f1_s_xgb = []\n",
    "ps_s_xgb = []\n",
    "rs_s_xgb = []\n",
    "acc_s_xgb = []\n",
    "\n",
    "f1_s_xgb = [f1(y_test,y_preds_xgb),f1(y_test1,XGB_under_preds),f1(y_test2,XGB_over_preds),f1(y_test_syn,XGB_syn_preds)]\n",
    "ps_s_xgb = [ps(y_test,y_preds_xgb),ps(y_test1,XGB_under_preds),ps(y_test2,XGB_over_preds),ps(y_test_syn,XGB_syn_preds)]\n",
    "rs_s_xgb = [rs(y_test,y_preds_xgb),rs(y_test1,XGB_under_preds),rs(y_test2,XGB_over_preds),rs(y_test_syn,XGB_syn_preds)]\n",
    "acc_s_xgb = [acc(y_test,y_preds_xgb),acc(y_test1,XGB_under_preds),acc(y_test2,XGB_over_preds),acc(y_test_syn,XGB_syn_preds)]\n",
    "\n",
    "f1_s_xgb = np.round(np.array(f1_s_xgb), 3)\n",
    "ps_s_xgb = np.round(np.array(ps_s_xgb), 3)\n",
    "rs_s_xgb = np.round(np.array(rs_s_xgb), 3)\n",
    "acc_s_xgb = np.round(np.array(acc_s_xgb), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####    For LGBM     #####\n",
    "\n",
    "f1_s_lgbm = []\n",
    "ps_s_lgbm = []\n",
    "rs_s_lgbm = []\n",
    "acc_s_lgbm = []\n",
    "\n",
    "f1_s_lgbm = [f1(y_test,lgbm_preds_imb),f1(y_test1,lgbm_preds_under_fin),f1(y_test2,lgbm_preds_over_fin),f1(y_test_syn,lgbm_preds_syn_fin)]\n",
    "ps_s_lgbm = [ps(y_test,lgbm_preds_imb),ps(y_test1,lgbm_preds_under_fin),ps(y_test2,lgbm_preds_over_fin),ps(y_test_syn,lgbm_preds_syn_fin)]\n",
    "rs_s_lgbm = [rs(y_test,lgbm_preds_imb),rs(y_test1,lgbm_preds_under_fin),rs(y_test2,lgbm_preds_over_fin),rs(y_test_syn,lgbm_preds_syn_fin)]\n",
    "acc_s_lgbm = [acc(y_test,lgbm_preds_imb),acc(y_test1,lgbm_preds_under_fin),acc(y_test2,lgbm_preds_over_fin),acc(y_test_syn,lgbm_preds_syn_fin)]\n",
    "\n",
    "f1_s_lgbm = np.round(np.array(f1_s_lgbm), 3)\n",
    "ps_s_lgbm = np.round(np.array(ps_s_lgbm), 3)\n",
    "rs_s_lgbm = np.round(np.array(rs_s_lgbm), 3)\n",
    "acc_s_lgbm = np.round(np.array(acc_s_lgbm), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for F1 scores \n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "plt.grid()\n",
    "plt.title(\"Plot of F1 Scores for Different methods\")\n",
    "plt.xlabel(\"Imbalance handling Method\",weight = 'bold')\n",
    "plt.ylabel(\"F1 score\",weight = 'bold')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for Precision scores \n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],ps_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],ps_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],ps_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "plt.grid()\n",
    "plt.title(\"Plot of Precission Scores for Different methods\")\n",
    "plt.xlabel(\"Imbalance handling Method\",weight = 'bold')\n",
    "plt.ylabel(\"Precission Scores\",weight = 'bold')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for Recall scores \n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "plt.grid()\n",
    "plt.title(\"Plot of Recall Scores for Different methods\")\n",
    "plt.xlabel(\"Imbalance handling Method\",weight = 'bold')\n",
    "plt.ylabel(\"Recall Scores\",weight = 'bold')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for Accuracy scores \n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "plt.grid()\n",
    "plt.title(\"Plot of Accuracy Scores for Different methods\")\n",
    "plt.xlabel(\"Imbalance handling Method\",weight = 'bold')\n",
    "plt.ylabel(\"Accuracy Scores\",weight = 'bold')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Best model grapgh \n",
    "\n",
    "plt.figure(figsize = (8,5))\n",
    "# plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],acc_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "# plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],rs_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "# plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "# plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_ada,marker='o', linestyle='--',label = \"AdaBoost\",color='b')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_xgb,marker='o', linestyle='--',label = \"XGBoost\",color = 'g')\n",
    "plt.plot([\"Imbalanced\",\"Under Sampled\",\"Over Sampled\",\"SMOTE\"],f1_s_lgbm,marker='o', linestyle='--',label = \"LGBM\",color = 'r')\n",
    "plt.grid()\n",
    "plt.title(\"Plot of Different measures and Scores for different Models\")\n",
    "plt.xlabel(\"Imbalance handling Method\",weight = 'bold')\n",
    "plt.ylabel(\"Scores\",weight = 'bold')\n",
    "low_point  = (\"Over Sampled\",1)\n",
    "high_point = (\"SMOTE\",1)\n",
    "plt.annotate('Best Models', xy=low_point, xytext=(\"Over Sampled\",0.85),\n",
    "             arrowprops=dict(facecolor='blue', shrink=0.05))\n",
    "plt.annotate('Best Models', xy=high_point, xytext=(\"Over Sampled\",0.85),\n",
    "            arrowprops=dict(facecolor='blue', shrink=0.05))\n",
    "# plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Finally Lets Try to predict the actual Data using our best model and check it's score on the actual data. </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the evaluation steps , we got XGBoost and Oversampling model as the best trained model.\n",
    "best_model = XGB_over\n",
    "\n",
    "y_actual = y_data\n",
    "y_best_predicted = best_model.predict(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "0 : Not Fraud       1.00      1.00      1.00    284315\n",
      "    1 : Fraud       0.98      1.00      0.99       492\n",
      "\n",
      "     accuracy                           1.00    284807\n",
      "    macro avg       0.99      1.00      0.99    284807\n",
      " weighted avg       1.00      1.00      1.00    284807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the Classification report \n",
    "class_names = [\"0 : Not Fraud\",\"1 : Fraud\"]\n",
    "print(classification_report(y_true=y_actual,y_pred=y_best_predicted,target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27489fa7ac0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix for Imbalanced Data and AdaBoost\n",
    "act_data_cm = cm(y_actual,y_best_predicted,labels=[0,1])\n",
    "disp_act_ = ConfusionMatrixDisplay(confusion_matrix=act_data_cm,display_labels=[\"0 : Not Fraud\",\"1 : Fraud\"])\n",
    "disp_act_.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_best_model_actual_data = 0\n",
    "cnt2_best_model_actual_data = 0\n",
    "for i in range(len(y_best_predicted)):\n",
    "    if (y_best_predicted[i] == 1 and np.array(y_actual)[i] == 0):\n",
    "        cnt_best_model_actual_data+=1\n",
    "    if (y_best_predicted[i] == 0 and np.array(y_actual)[i] == 1):\n",
    "        cnt2_best_model_actual_data+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud Instances Predicted as Fraud (Out of 2,84,315)   :  10\n",
      "Fraud Instances Predicted as Not Fraud (Out of 492)   :  0\n",
      "_________________________________________________________________\n",
      "\n",
      "Total Misclassified Instances (Out of 2,84,807)          :  10\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(\"Not Fraud Instances Predicted as Fraud (Out of 2,84,315)   : \",cnt_best_model_actual_data)\n",
    "print(\"Fraud Instances Predicted as Not Fraud (Out of 492)   : \",cnt2_best_model_actual_data)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(\"\")\n",
    "print(\"Total Misclassified Instances (Out of 2,84,807)          : \", cnt_best_model_actual_data+cnt2_best_model_actual_data)\n",
    "print(\"_________________________________________________________________\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Our best trained model is correctly classifying all the fraud instances of the actual data , hence  the model is not at all biased towards any of the classes , this can be seen from the fact that it only missclassified 10 points from 2,84,315 non fraud instances as fraud , but did not classified any fraud instances as non fraud. So, every fraud instance is caught with an accuracy of 100% and every non fraud instance is detected corectly with an accuracy of 99.996%</h4>"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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