Credit Card Fraud Detection Using Machine Learning..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the dependencies\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "%matplotlib inline\n",
    "\n",
    "# Supress Warnings\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the dataset to a Pandas Dataframe\n",
    "credit_card_data = pd.read_csv(r\"C:\\Users\\james\\OneDrive\\Documents\\OASIS INFOBYTE INTERNSHIP\\PROJECT LIST DATA ANALYTICS\\LEVEL 2\\FRAUD DETECTION\\DATASET\\creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display top 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display last 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <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>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>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\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",
       "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",
       "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",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Shape of Our Dataset (Number of Rows And Number of Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 284807\n",
      "Number of Columns 31\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows\",credit_card_data.shape[0])\n",
    "print(\"Number of Columns\",credit_card_data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Information About Our Dataset (Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "#dataset information\n",
    "credit_card_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Null Values In The Dataset\n",
    "credit_card_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "##### We will now select and transform relevant features to enhance fraud detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "credit_card_data['Amount']=sc.fit_transform(pd.DataFrame(credit_card_data['Amount']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
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       "      <td>-1.514654</td>\n",
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       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
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       "      <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>1.160686</td>\n",
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       "      <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",
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       "      <td>-1.387024</td>\n",
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       "      <td>-1.175575</td>\n",
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       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
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       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28    Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  0.244964      0  \n",
       "1  0.125895 -0.008983  0.014724 -0.342475      0  \n",
       "2 -0.139097 -0.055353 -0.059752  1.160686      0  \n",
       "3 -0.221929  0.062723  0.061458  0.140534      0  \n",
       "4  0.502292  0.219422  0.215153 -0.073403      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_data = credit_card_data.drop(['Time'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
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       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  0.244964      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724 -0.342475      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  1.160686      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  0.140534      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153 -0.073403      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 30)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the data for duplicated values\n",
    "credit_card_data.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing Duplicated values\n",
    "credit_card_data = credit_card_data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663, 30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9144"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "284807-275663"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there is now a difference of 9144 after removing duplicate values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    275190\n",
       "1       473\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(credit_card_data['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store Feature Matrix In X And Response (Target) In Vector y\n",
    "\n",
    "x = credit_card_data.drop('Class',axis=1)\n",
    "y = credit_card_data['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting The Dataset Into The Training Set And Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Imbalanced Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Under-Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separating the data for analysis\n",
    "normal = credit_card_data[credit_card_data['Class']==0]\n",
    "fraud = credit_card_data[credit_card_data['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275190, 30)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 30)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_sample=normal.sample(n=473)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 30)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.concat([normal_sample,fraud],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    473\n",
       "1    473\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</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>-0.326326</td>\n",
       "      <td>-0.074748</td>\n",
       "      <td>1.723496</td>\n",
       "      <td>1.091868</td>\n",
       "      <td>-0.731032</td>\n",
       "      <td>1.193485</td>\n",
       "      <td>-0.017609</td>\n",
       "      <td>0.438574</td>\n",
       "      <td>0.170799</td>\n",
       "      <td>-0.183872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079371</td>\n",
       "      <td>0.297022</td>\n",
       "      <td>0.384312</td>\n",
       "      <td>-0.269238</td>\n",
       "      <td>-1.100298</td>\n",
       "      <td>0.501900</td>\n",
       "      <td>0.195889</td>\n",
       "      <td>0.199147</td>\n",
       "      <td>0.183554</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.720427</td>\n",
       "      <td>-0.552104</td>\n",
       "      <td>0.851425</td>\n",
       "      <td>1.629354</td>\n",
       "      <td>-0.694075</td>\n",
       "      <td>0.693939</td>\n",
       "      <td>1.639966</td>\n",
       "      <td>-0.034820</td>\n",
       "      <td>-0.501043</td>\n",
       "      <td>-0.032307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033857</td>\n",
       "      <td>0.366835</td>\n",
       "      <td>0.262243</td>\n",
       "      <td>0.252204</td>\n",
       "      <td>-0.061722</td>\n",
       "      <td>-0.254815</td>\n",
       "      <td>0.435418</td>\n",
       "      <td>0.229298</td>\n",
       "      <td>1.405529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.359913</td>\n",
       "      <td>1.104947</td>\n",
       "      <td>1.420393</td>\n",
       "      <td>0.143011</td>\n",
       "      <td>-0.183814</td>\n",
       "      <td>-1.184368</td>\n",
       "      <td>0.677341</td>\n",
       "      <td>-0.060511</td>\n",
       "      <td>-0.327629</td>\n",
       "      <td>-0.493892</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.250172</td>\n",
       "      <td>-0.677302</td>\n",
       "      <td>0.028917</td>\n",
       "      <td>0.644342</td>\n",
       "      <td>-0.224850</td>\n",
       "      <td>0.053682</td>\n",
       "      <td>0.242774</td>\n",
       "      <td>0.100161</td>\n",
       "      <td>-0.345313</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.703578</td>\n",
       "      <td>-0.481657</td>\n",
       "      <td>-1.968830</td>\n",
       "      <td>0.468188</td>\n",
       "      <td>0.197102</td>\n",
       "      <td>-0.675757</td>\n",
       "      <td>0.282543</td>\n",
       "      <td>-0.209036</td>\n",
       "      <td>0.989606</td>\n",
       "      <td>-0.932650</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151003</td>\n",
       "      <td>-0.580993</td>\n",
       "      <td>0.062763</td>\n",
       "      <td>0.497142</td>\n",
       "      <td>-0.102918</td>\n",
       "      <td>-0.130135</td>\n",
       "      <td>-0.040133</td>\n",
       "      <td>0.013697</td>\n",
       "      <td>0.364347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.301585</td>\n",
       "      <td>0.561754</td>\n",
       "      <td>0.754687</td>\n",
       "      <td>0.839509</td>\n",
       "      <td>-0.024987</td>\n",
       "      <td>1.032879</td>\n",
       "      <td>-0.212128</td>\n",
       "      <td>0.212671</td>\n",
       "      <td>0.194073</td>\n",
       "      <td>0.220737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385560</td>\n",
       "      <td>1.381401</td>\n",
       "      <td>-0.056997</td>\n",
       "      <td>0.262695</td>\n",
       "      <td>-1.223114</td>\n",
       "      <td>0.034129</td>\n",
       "      <td>0.231323</td>\n",
       "      <td>0.261094</td>\n",
       "      <td>-0.279665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -0.326326 -0.074748  1.723496  1.091868 -0.731032  1.193485 -0.017609   \n",
       "1 -1.720427 -0.552104  0.851425  1.629354 -0.694075  0.693939  1.639966   \n",
       "2 -0.359913  1.104947  1.420393  0.143011 -0.183814 -1.184368  0.677341   \n",
       "3  1.703578 -0.481657 -1.968830  0.468188  0.197102 -0.675757  0.282543   \n",
       "4  0.301585  0.561754  0.754687  0.839509 -0.024987  1.032879 -0.212128   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.438574  0.170799 -0.183872  ...  0.079371  0.297022  0.384312 -0.269238   \n",
       "1 -0.034820 -0.501043 -0.032307  ...  0.033857  0.366835  0.262243  0.252204   \n",
       "2 -0.060511 -0.327629 -0.493892  ... -0.250172 -0.677302  0.028917  0.644342   \n",
       "3 -0.209036  0.989606 -0.932650  ... -0.151003 -0.580993  0.062763  0.497142   \n",
       "4  0.212671  0.194073  0.220737  ...  0.385560  1.381401 -0.056997  0.262695   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0 -1.100298  0.501900  0.195889  0.199147  0.183554      0  \n",
       "1 -0.061722 -0.254815  0.435418  0.229298  1.405529      0  \n",
       "2 -0.224850  0.053682  0.242774  0.100161 -0.345313      0  \n",
       "3 -0.102918 -0.130135 -0.040133  0.013697  0.364347      0  \n",
       "4 -1.223114  0.034129  0.231323  0.261094 -0.279665      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = new_data.drop('Class',axis=1)\n",
    "y = new_data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the logistic Regression model with Training Data\n",
    "log = LogisticRegression()\n",
    "log.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = log.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.95      0.93        88\n",
      "           1       0.96      0.91      0.93       102\n",
      "\n",
      "    accuracy                           0.93       190\n",
      "   macro avg       0.93      0.93      0.93       190\n",
      "weighted avg       0.93      0.93      0.93       190\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9315789473684211"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred1))\n",
    "accuracy_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Logostic Regression Model gives 93% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the Decision Tree Classifier model with Training Data\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2 = dt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.88        88\n",
      "           1       0.90      0.89      0.90       102\n",
      "\n",
      "    accuracy                           0.89       190\n",
      "   macro avg       0.89      0.89      0.89       190\n",
      "weighted avg       0.89      0.89      0.89       190\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8894736842105263"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred2))\n",
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Decision Tree Classifier Model gives 89% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the Random Forest Classifier model with Training Data\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred3 = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.95      0.92        88\n",
      "           1       0.96      0.90      0.93       102\n",
      "\n",
      "    accuracy                           0.93       190\n",
      "   macro avg       0.93      0.93      0.93       190\n",
      "weighted avg       0.93      0.93      0.93       190\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9263157894736842"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred3))\n",
    "accuracy_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest Classifier Model gives 93% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Models':['LR','DT','RF'],\n",
    "              \"ACC\":[accuracy_score(y_test,y_pred1)*100,\n",
    "                     accuracy_score(y_test,y_pred2)*100,\n",
    "                     accuracy_score(y_test,y_pred3)*100\n",
    "                    ]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>93.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DT</td>\n",
       "      <td>88.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>92.631579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models        ACC\n",
       "0     LR  93.157895\n",
       "1     DT  88.947368\n",
       "2     RF  92.631579"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Models', ylabel='ACC'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=final_data['Models'],y=final_data['ACC'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = credit_card_data.drop('Class',axis=1)\n",
    "y = credit_card_data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663, 29)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_res,y_res = SMOTE().fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    275190\n",
       "1    275190\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_res.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_res,y_res,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = log.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.95     55073\n",
      "           1       0.97      0.92      0.94     55003\n",
      "\n",
      "    accuracy                           0.94    110076\n",
      "   macro avg       0.95      0.94      0.94    110076\n",
      "weighted avg       0.95      0.94      0.94    110076\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9449562120716596"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred1))\n",
    "accuracy_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Logistic Regression Model gives 95% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2 = dt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     55073\n",
      "           1       1.00      1.00      1.00     55003\n",
      "\n",
      "    accuracy                           1.00    110076\n",
      "   macro avg       1.00      1.00      1.00    110076\n",
      "weighted avg       1.00      1.00      1.00    110076\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9979922962316945"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred2))\n",
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Decision Tree Classifier model gives 100% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred3 = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     55073\n",
      "           1       1.00      1.00      1.00     55003\n",
      "\n",
      "    accuracy                           1.00    110076\n",
      "   macro avg       1.00      1.00      1.00    110076\n",
      "weighted avg       1.00      1.00      1.00    110076\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9999364075729495"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred3))\n",
    "accuracy_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest Classifier gives 100% accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Models':['LR','DT','RF'],\n",
    "              \"ACC\":[accuracy_score(y_test,y_pred1)*100,\n",
    "                     accuracy_score(y_test,y_pred2)*100,\n",
    "                     accuracy_score(y_test,y_pred3)*100\n",
    "                    ]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>94.495621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DT</td>\n",
       "      <td>99.799230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>99.993641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models        ACC\n",
       "0     LR  94.495621\n",
       "1     DT  99.799230\n",
       "2     RF  99.993641"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Models', ylabel='ACC'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PCbCxqt602AZV9d3uuRknAO9Isqmq3v4gfqd0v3n6SFraBcDbquqaeeNfoTv9k+TZwO1V9UPgy8AfduPP5WdPytsEnNw9rvO+axKPmfuBSR4F3FVVHwLOAY4Zxx8kLcUjBWkJVbUNOG+BVW8FLuhm1LyLn01p/jbgoiTXAf8C3NJ9zvVJ/gL4QpK9gJ8wmrf/5jmf+STgnCT3duv/9KH/i6SlOUuqJKnx9JEkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKk5v8Apl5xjNzoFZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=final_data['Models'],y=final_data['ACC'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real-Time Monitoring\n",
    "\n",
    "#### Now, we can save the model and execute real time monitoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf1 = RandomForestClassifier()\n",
    "rf1.fit(x_res,y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['credit_card_model']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(rf1,\"credit_card_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"credit_card_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict([[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Transcation\n"
     ]
    }
   ],
   "source": [
    "if pred == 0:\n",
    "    print(\"Normal Transcation\")\n",
    "else:\n",
    "    print(\"Fraudulent Transcation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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