Fraud Transaction Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8704db61",
   "metadata": {},
   "source": [
    "# Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "749829e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a112a88",
   "metadata": {},
   "source": [
    "Dataset Link: https://drive.google.com/uc?export=download&confirm=6gh6&id=1VNpyNkGxHdskfdTNRSjjyNa5qC9u0JyV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06ab00b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the data to a pandas Dataframe\n",
    "transaction_data = pd.read_csv('/home/lucy/Downloads/Fraud.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ff672a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6362620, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb283e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7817.71</td>\n",
       "      <td>C90045638</td>\n",
       "      <td>53860.00</td>\n",
       "      <td>46042.29</td>\n",
       "      <td>M573487274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7107.77</td>\n",
       "      <td>C154988899</td>\n",
       "      <td>183195.00</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>M408069119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7861.64</td>\n",
       "      <td>C1912850431</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>168225.59</td>\n",
       "      <td>M633326333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.36</td>\n",
       "      <td>C1265012928</td>\n",
       "      <td>2671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>M1176932104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>5337.77</td>\n",
       "      <td>C712410124</td>\n",
       "      <td>41720.00</td>\n",
       "      <td>36382.23</td>\n",
       "      <td>C195600860</td>\n",
       "      <td>41898.0</td>\n",
       "      <td>40348.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
       "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
       "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
       "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
       "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
       "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0            0.00        0               0  \n",
       "1  M2044282225             0.0            0.00        0               0  \n",
       "2   C553264065             0.0            0.00        1               0  \n",
       "3    C38997010         21182.0            0.00        1               0  \n",
       "4  M1230701703             0.0            0.00        0               0  \n",
       "5   M573487274             0.0            0.00        0               0  \n",
       "6   M408069119             0.0            0.00        0               0  \n",
       "7   M633326333             0.0            0.00        0               0  \n",
       "8  M1176932104             0.0            0.00        0               0  \n",
       "9   C195600860         41898.0        40348.79        0               0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first five rows of the data\n",
    "transaction_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bac8f1c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>nameOrig</th>\n",
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       "      <th>isFlaggedFraud</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6362610</th>\n",
       "      <td>742</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>C778071008</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>6362611</th>\n",
       "      <td>742</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63416.99</td>\n",
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       "      <td>63416.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1662241365</td>\n",
       "      <td>276433.18</td>\n",
       "      <td>339850.17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6362612</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>C1531301470</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1470998563</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>6362613</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>C1436118706</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1240760502</td>\n",
       "      <td>503464.50</td>\n",
       "      <td>1762283.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6362614</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>C2013999242</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1850423904</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362615</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>C786484425</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C776919290</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362616</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>C1529008245</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1881841831</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6362617</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>C1162922333</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C1365125890</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362618</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>C1685995037</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C2080388513</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362619</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>C1280323807</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C873221189</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "         step      type      amount     nameOrig  oldbalanceOrg  \\\n",
       "6362610   742  TRANSFER    63416.99   C778071008       63416.99   \n",
       "6362611   742  CASH_OUT    63416.99   C994950684       63416.99   \n",
       "6362612   743  TRANSFER  1258818.82  C1531301470     1258818.82   \n",
       "6362613   743  CASH_OUT  1258818.82  C1436118706     1258818.82   \n",
       "6362614   743  TRANSFER   339682.13  C2013999242      339682.13   \n",
       "6362615   743  CASH_OUT   339682.13   C786484425      339682.13   \n",
       "6362616   743  TRANSFER  6311409.28  C1529008245     6311409.28   \n",
       "6362617   743  CASH_OUT  6311409.28  C1162922333     6311409.28   \n",
       "6362618   743  TRANSFER   850002.52  C1685995037      850002.52   \n",
       "6362619   743  CASH_OUT   850002.52  C1280323807      850002.52   \n",
       "\n",
       "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
       "6362610             0.0  C1812552860            0.00            0.00        1   \n",
       "6362611             0.0  C1662241365       276433.18       339850.17        1   \n",
       "6362612             0.0  C1470998563            0.00            0.00        1   \n",
       "6362613             0.0  C1240760502       503464.50      1762283.33        1   \n",
       "6362614             0.0  C1850423904            0.00            0.00        1   \n",
       "6362615             0.0   C776919290            0.00       339682.13        1   \n",
       "6362616             0.0  C1881841831            0.00            0.00        1   \n",
       "6362617             0.0  C1365125890        68488.84      6379898.11        1   \n",
       "6362618             0.0  C2080388513            0.00            0.00        1   \n",
       "6362619             0.0   C873221189      6510099.11      7360101.63        1   \n",
       "\n",
       "         isFlaggedFraud  \n",
       "6362610               0  \n",
       "6362611               0  \n",
       "6362612               0  \n",
       "6362613               0  \n",
       "6362614               0  \n",
       "6362615               0  \n",
       "6362616               0  \n",
       "6362617               0  \n",
       "6362618               0  \n",
       "6362619               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last ten rows of the data\n",
    "transaction_data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b7e63b",
   "metadata": {},
   "source": [
    "# Analyzing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23621662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6362620 entries, 0 to 6362619\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   step            int64  \n",
      " 1   type            object \n",
      " 2   amount          float64\n",
      " 3   nameOrig        object \n",
      " 4   oldbalanceOrg   float64\n",
      " 5   newbalanceOrig  float64\n",
      " 6   nameDest        object \n",
      " 7   oldbalanceDest  float64\n",
      " 8   newbalanceDest  float64\n",
      " 9   isFraud         int64  \n",
      " 10  isFlaggedFraud  int64  \n",
      "dtypes: float64(5), int64(3), object(3)\n",
      "memory usage: 534.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# dataset informations\n",
    "transaction_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62696137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "transaction_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86977b13",
   "metadata": {},
   "source": [
    "So there are no null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "310a2c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6354407\n",
       "1       8213\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of legit transactions & fraudulent transactions\n",
    "\n",
    "transaction_data['isFraud'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e509227",
   "metadata": {},
   "source": [
    "We can clearly see that the dataset is highly unbalanced.\n",
    "where '0' represents Legit transactions and\n",
    "      '1' represents Fraudulent transactions.\n",
    "      \n",
    "For better undestanding see the diagram below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff1dd752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Legit transactions:  6354407\n",
      "Number of Fraud transactions:  8213\n",
      "Percentage of Legit transactions: 99.8709 %\n",
      "Percentage of Fraud transactions: 0.1291 %\n"
     ]
    }
   ],
   "source": [
    "# For a clear counting of dataset\n",
    "\n",
    "legit = len(transaction_data[transaction_data.isFraud == 0])\n",
    "fraud = len(transaction_data[transaction_data.isFraud == 1])\n",
    "legit_transaction_percentage = (legit / (fraud + legit)) * 100\n",
    "fraud_transaction_percentage = (fraud / (fraud + legit)) * 100\n",
    "\n",
    "print(\"Number of Legit transactions: \", legit)\n",
    "print(\"Number of Fraud transactions: \", fraud)\n",
    "print(\"Percentage of Legit transactions: {:.4f} %\".format(legit_transaction_percentage))\n",
    "print(\"Percentage of Fraud transactions: {:.4f} %\".format(fraud_transaction_percentage))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba34d357",
   "metadata": {},
   "source": [
    "# DATA VISUALISATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b9b1872",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33dd4ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "labels = [\"Legit_Transactions\", \"Fraud_Transaction\"]\n",
    "count_classes = transaction_data.value_counts(transaction_data['isFraud'], sort= True)\n",
    "count_classes.plot(kind = \"bar\", rot = 0)\n",
    "plt.title(\"Visualization of Transactions\")\n",
    "plt.ylabel(\"Transaction_Count\")\n",
    "plt.xticks(range(2), labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a217e279",
   "metadata": {},
   "source": [
    "# Problem Solving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7842712",
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a copy of original dataset to train and test the models\n",
    "\n",
    "new_dataset=transaction_data.copy()\n",
    "new_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a924a840",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables with datatype - 'object' are:\n",
      "Index(['type', 'nameOrig', 'nameDest'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Checking if there are any attributes with dtype: object\n",
    "\n",
    "new_List = new_dataset.select_dtypes(include = \"object\").columns\n",
    "print (\"Variables with datatype - 'object' are:\")\n",
    "print (new_List)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cc57e5",
   "metadata": {},
   "source": [
    "We need to Label Encode them as this three Attributes are 'object'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "540f746f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6362620 entries, 0 to 6362619\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Dtype  \n",
      "---  ------          -----  \n",
      " 0   step            int64  \n",
      " 1   type            int64  \n",
      " 2   amount          float64\n",
      " 3   nameOrig        int64  \n",
      " 4   oldbalanceOrg   float64\n",
      " 5   newbalanceOrig  float64\n",
      " 6   nameDest        int64  \n",
      " 7   oldbalanceDest  float64\n",
      " 8   newbalanceDest  float64\n",
      " 9   isFraud         int64  \n",
      " 10  isFlaggedFraud  int64  \n",
      "dtypes: float64(5), int64(6)\n",
      "memory usage: 534.0 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Label Encoding for object to numeric conversion\n",
    "\n",
    "label_encode = LabelEncoder()\n",
    "\n",
    "for i in new_List:\n",
    "    new_dataset[i] = label_encode.fit_transform(new_dataset[i].astype(str))\n",
    "\n",
    "print (new_dataset.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25334f39",
   "metadata": {},
   "source": [
    "The three \"object\" has successfully converted into integer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34720f4b",
   "metadata": {},
   "source": [
    "# Multicolinearity Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56103ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing related Dependencies\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "087c453c",
   "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>Variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>step</td>\n",
       "      <td>2.791610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>type</td>\n",
       "      <td>4.467405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amount</td>\n",
       "      <td>4.149312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nameOrig</td>\n",
       "      <td>2.764234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldbalanceOrg</td>\n",
       "      <td>576.803777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>newbalanceOrig</td>\n",
       "      <td>582.709128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nameDest</td>\n",
       "      <td>3.300975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>oldbalanceDest</td>\n",
       "      <td>73.349937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>newbalanceDest</td>\n",
       "      <td>85.005614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isFraud</td>\n",
       "      <td>1.195305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>isFlaggedFraud</td>\n",
       "      <td>1.002587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Variables         VIF\n",
       "0             step    2.791610\n",
       "1             type    4.467405\n",
       "2           amount    4.149312\n",
       "3         nameOrig    2.764234\n",
       "4    oldbalanceOrg  576.803777\n",
       "5   newbalanceOrig  582.709128\n",
       "6         nameDest    3.300975\n",
       "7   oldbalanceDest   73.349937\n",
       "8   newbalanceDest   85.005614\n",
       "9          isFraud    1.195305\n",
       "10  isFlaggedFraud    1.002587"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_vif(transaction_data):\n",
    "\n",
    "    # Calculating Variance Inflation Factor\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"Variables\"] = transaction_data.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(transaction_data.values, i) for i in range(transaction_data.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "calc_vif(new_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56dac232",
   "metadata": {},
   "source": [
    "There are three highly correlated variables which are: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454e1700",
   "metadata": {},
   "source": [
    "nameOrig-nameDest,\n",
    "oldbalanceOrg-newbalanceOrig and\n",
    "oldbalanceDest-newbalanceDest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b129cb",
   "metadata": {},
   "source": [
    "After combinning these pairs,deopping the individual ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d957804f",
   "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>Variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>step</td>\n",
       "      <td>2.710678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>type</td>\n",
       "      <td>2.863989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>amount</td>\n",
       "      <td>3.890535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>isFraud</td>\n",
       "      <td>1.189937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>isFlaggedFraud</td>\n",
       "      <td>1.002563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>balance_orig</td>\n",
       "      <td>1.332185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>balance_dest</td>\n",
       "      <td>3.790322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>name</td>\n",
       "      <td>3.472358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Variables       VIF\n",
       "0            step  2.710678\n",
       "1            type  2.863989\n",
       "2          amount  3.890535\n",
       "3         isFraud  1.189937\n",
       "4  isFlaggedFraud  1.002563\n",
       "5    balance_orig  1.332185\n",
       "6    balance_dest  3.790322\n",
       "7            name  3.472358"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatinating columns\n",
    "\n",
    "new_dataset['balance_orig'] = new_dataset.apply(lambda x: x['oldbalanceOrg'] - x['newbalanceOrig'],axis=1)\n",
    "new_dataset['balance_dest'] = new_dataset.apply(lambda x: x['oldbalanceDest'] - x['newbalanceDest'],axis=1)\n",
    "new_dataset['name'] = new_dataset.apply(lambda x: x['nameOrig'] + x['nameDest'],axis=1)\n",
    "\n",
    "#dropping columns\n",
    "new_dataset = new_dataset.drop(['oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest','nameOrig','nameDest'],axis=1)\n",
    "\n",
    "calc_vif(new_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261e1ae3",
   "metadata": {},
   "source": [
    "Now using the VIF values and correlation heatmap of previous dataset \n",
    "\"transaction_data\" and newly created dataset \"new_dataset\" ,we can \n",
    "select the variables to include into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9fe5e100",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4913/2503696863.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr=transaction_data.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap of transaction_data\n",
    "corr=transaction_data.corr()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bfb9dc4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap of new_dataset\n",
    "corr=new_dataset.corr()\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b590ab3a",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3740b88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing dependencies\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ed9c069f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_dataset.drop(columns='isFraud', axis=1)\n",
    "Y = new_dataset['isFraud']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc04a96",
   "metadata": {},
   "source": [
    "Splitting Features & Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a10fc7e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (4453834, 7)\n",
      "Shape of X_test:  (1908786, 7)\n"
     ]
    }
   ],
   "source": [
    "# Split the data\n",
    "(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, test_size= 0.3, random_state= 42)\n",
    "\n",
    "print(\"Shape of X_train: \", X_train.shape)\n",
    "print(\"Shape of X_test: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4693bd",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d2ec027",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_dt = decision_tree.predict(X_test)\n",
    "decision_tree_score = decision_tree.score(X_test, Y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f242032e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators= 100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_rf = random_forest.predict(X_test)\n",
    "random_forest_score = random_forest.score(X_test, Y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee327f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logistic_regression = LogisticRegression()\n",
    "logistic_regression.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_lr = logistic_regression.predict(X_test)\n",
    "logistic_regression_score = logistic_regression.score(X_test, Y_test) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4394b76",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f491c45",
   "metadata": {},
   "source": [
    "Scores of Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ba7d19c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Score:  99.94121918329242\n",
      "Random Forest Score:  99.96416570532264\n",
      "Logistic Regression Score:  99.87898067148438\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree Score: \", decision_tree_score)\n",
    "print(\"Random Forest Score: \", random_forest_score)\n",
    "print(\"Logistic Regression Score: \", logistic_regression_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2e1d4d",
   "metadata": {},
   "source": [
    "Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "86abd6fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Decision Tree:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       0.77      0.77      0.77      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.88      0.88      0.88   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "Classification Report for Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       0.98      0.74      0.84      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.99      0.87      0.92   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n",
      "Classification Report for Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906351\n",
      "           1       0.54      0.36      0.43      2435\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.77      0.68      0.71   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "classification_report_dt = classification_report(Y_test, Y_pred_dt)\n",
    "print(\"Classification Report for Decision Tree:\")\n",
    "print(classification_report_dt)\n",
    "\n",
    "# Random Forest\n",
    "\n",
    "classification_report_rf = classification_report(Y_test, Y_pred_rf)\n",
    "print(\"Classification Report for Random Forest:\")\n",
    "print(classification_report_rf)\n",
    "\n",
    "# Logistic Regression\n",
    "\n",
    "classification_report_lr = classification_report(Y_test, Y_pred_lr)\n",
    "print(\"Classification Report for Logistic Regression:\")\n",
    "print(classification_report_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39de432a",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1a998d",
   "metadata": {},
   "source": [
    "We can see the Accuracy of Decision Tree and Random Forest is almost\n",
    "same. Precision is a crucial factor to predict correctly. The Precision\n",
    "and  f1-score for Random Forest is way better than other two. So,\n",
    "Random Forest is the best option. There is no way of taking Logistic \n",
    "regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630d2b70",
   "metadata": {},
   "source": [
    "> With the help of Correlation Heatmap, we have selected the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3da55c1",
   "metadata": {},
   "source": [
    "> Source of the transaction request,legitimacy of the requesting \n",
    "organisation/individual could be the key factors to predict\n",
    "fraudulent customer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c858d5",
   "metadata": {},
   "source": [
    "> verified software, usage of VPN, keeping contact with bank, keep updated\n",
    "software on mobile/pc, using secure websites can prevent this kind of \n",
    "transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3bc769",
   "metadata": {},
   "source": [
    "Thank You."
   ]
  }
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