Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ded9359d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "430a1976",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the file\n",
    "#give path if required\n",
    "df = pd.read_csv('Fraud.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ac6af7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6362620, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2875aac",
   "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": [
    "#data type of each column/feature\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "420b16d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data after dropping duplicate rows:  (6362620, 11)\n"
     ]
    }
   ],
   "source": [
    "#dropping duplicate rows\n",
    "\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(\"Shape of data after dropping duplicate rows: \", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a45f1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step              0\n",
       "type              0\n",
       "amount            0\n",
       "nameOrig          0\n",
       "oldbalanceOrg     0\n",
       "newbalanceOrig    0\n",
       "nameDest          0\n",
       "oldbalanceDest    0\n",
       "newbalanceDest    0\n",
       "isFraud           0\n",
       "isFlaggedFraud    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of null vales in data\n",
    "# Here, depth column has 697 null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59b25802",
   "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>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</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>count</th>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "      <td>6.362620e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.433972e+02</td>\n",
       "      <td>1.798619e+05</td>\n",
       "      <td>8.338831e+05</td>\n",
       "      <td>8.551137e+05</td>\n",
       "      <td>1.100702e+06</td>\n",
       "      <td>1.224996e+06</td>\n",
       "      <td>1.290820e-03</td>\n",
       "      <td>2.514687e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.423320e+02</td>\n",
       "      <td>6.038582e+05</td>\n",
       "      <td>2.888243e+06</td>\n",
       "      <td>2.924049e+06</td>\n",
       "      <td>3.399180e+06</td>\n",
       "      <td>3.674129e+06</td>\n",
       "      <td>3.590480e-02</td>\n",
       "      <td>1.585775e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.560000e+02</td>\n",
       "      <td>1.338957e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>7.487194e+04</td>\n",
       "      <td>1.420800e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.327057e+05</td>\n",
       "      <td>2.146614e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.350000e+02</td>\n",
       "      <td>2.087215e+05</td>\n",
       "      <td>1.073152e+05</td>\n",
       "      <td>1.442584e+05</td>\n",
       "      <td>9.430367e+05</td>\n",
       "      <td>1.111909e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>9.244552e+07</td>\n",
       "      <td>5.958504e+07</td>\n",
       "      <td>4.958504e+07</td>\n",
       "      <td>3.560159e+08</td>\n",
       "      <td>3.561793e+08</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "count  6.362620e+06  6.362620e+06   6.362620e+06    6.362620e+06   \n",
       "mean   2.433972e+02  1.798619e+05   8.338831e+05    8.551137e+05   \n",
       "std    1.423320e+02  6.038582e+05   2.888243e+06    2.924049e+06   \n",
       "min    1.000000e+00  0.000000e+00   0.000000e+00    0.000000e+00   \n",
       "25%    1.560000e+02  1.338957e+04   0.000000e+00    0.000000e+00   \n",
       "50%    2.390000e+02  7.487194e+04   1.420800e+04    0.000000e+00   \n",
       "75%    3.350000e+02  2.087215e+05   1.073152e+05    1.442584e+05   \n",
       "max    7.430000e+02  9.244552e+07   5.958504e+07    4.958504e+07   \n",
       "\n",
       "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \n",
       "count    6.362620e+06    6.362620e+06  6.362620e+06    6.362620e+06  \n",
       "mean     1.100702e+06    1.224996e+06  1.290820e-03    2.514687e-06  \n",
       "std      3.399180e+06    3.674129e+06  3.590480e-02    1.585775e-03  \n",
       "min      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "25%      0.000000e+00    0.000000e+00  0.000000e+00    0.000000e+00  \n",
       "50%      1.327057e+05    2.146614e+05  0.000000e+00    0.000000e+00  \n",
       "75%      9.430367e+05    1.111909e+06  0.000000e+00    0.000000e+00  \n",
       "max      3.560159e+08    3.561793e+08  1.000000e+00    1.000000e+00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The describe() method returns description of the data in the DataFrame.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "334409af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['step', 'amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest',\n",
      "       'newbalanceDest', 'isFraud', 'isFlaggedFraud'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#as we know we have 8 numerical columns from data.info\n",
    "#printing that columns\n",
    "\n",
    "numerical=df.select_dtypes(include='number')\n",
    "numerical_col=numerical.columns\n",
    "print(numerical_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55c01aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['type', 'nameOrig', 'nameDest'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#categorical data\n",
    "category=df.select_dtypes(exclude='number')\n",
    "category_col=category.columns\n",
    "print(category_col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6c10df0",
   "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>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</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>170136.0</td>\n",
       "      <td>160296.36</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>21249.0</td>\n",
       "      <td>19384.72</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>181.0</td>\n",
       "      <td>0.00</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>181.0</td>\n",
       "      <td>0.00</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>41554.0</td>\n",
       "      <td>29885.86</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  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
       "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
       "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
       "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0             0.0        0               0  \n",
       "1             0.0        0               0  \n",
       "2             0.0        1               0  \n",
       "3             0.0        1               0  \n",
       "4             0.0        0               0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can drop nameOrig and nameDest\n",
    "df.drop(['nameOrig','nameDest'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c4fa2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CASH_OUT    2237500\n",
       "PAYMENT     2151495\n",
       "CASH_IN     1399284\n",
       "TRANSFER     532909\n",
       "DEBIT         41432\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "548170fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afe88e03",
   "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>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   1864.28        21249.0        19384.72             0.0   \n",
       "2     1    181.00          181.0            0.00             0.0   \n",
       "3     1    181.00          181.0            0.00         21182.0   \n",
       "4     1  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  isFlaggedFraud  type_CASH_OUT  type_DEBIT  \\\n",
       "0             0.0        0               0              0           0   \n",
       "1             0.0        0               0              0           0   \n",
       "2             0.0        1               0              0           0   \n",
       "3             0.0        1               0              1           0   \n",
       "4             0.0        0               0              0           0   \n",
       "\n",
       "   type_PAYMENT  type_TRANSFER  \n",
       "0             1              0  \n",
       "1             1              0  \n",
       "2             0              1  \n",
       "3             0              0  \n",
       "4             1              0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset=pd.get_dummies(df,drop_first=True)\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef5e07fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_dataset.drop('isFraud',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4984fc70",
   "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>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362615</th>\n",
       "      <td>743</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362616</th>\n",
       "      <td>743</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362617</th>\n",
       "      <td>743</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362618</th>\n",
       "      <td>743</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362619</th>\n",
       "      <td>743</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6362620 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         step      amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0           1     9839.64      170136.00       160296.36            0.00   \n",
       "1           1     1864.28       21249.00        19384.72            0.00   \n",
       "2           1      181.00         181.00            0.00            0.00   \n",
       "3           1      181.00         181.00            0.00        21182.00   \n",
       "4           1    11668.14       41554.00        29885.86            0.00   \n",
       "...       ...         ...            ...             ...             ...   \n",
       "6362615   743   339682.13      339682.13            0.00            0.00   \n",
       "6362616   743  6311409.28     6311409.28            0.00            0.00   \n",
       "6362617   743  6311409.28     6311409.28            0.00        68488.84   \n",
       "6362618   743   850002.52      850002.52            0.00            0.00   \n",
       "6362619   743   850002.52      850002.52            0.00      6510099.11   \n",
       "\n",
       "         newbalanceDest  isFlaggedFraud  type_CASH_OUT  type_DEBIT  \\\n",
       "0                  0.00               0              0           0   \n",
       "1                  0.00               0              0           0   \n",
       "2                  0.00               0              0           0   \n",
       "3                  0.00               0              1           0   \n",
       "4                  0.00               0              0           0   \n",
       "...                 ...             ...            ...         ...   \n",
       "6362615       339682.13               0              1           0   \n",
       "6362616            0.00               0              0           0   \n",
       "6362617      6379898.11               0              1           0   \n",
       "6362618            0.00               0              0           0   \n",
       "6362619      7360101.63               0              1           0   \n",
       "\n",
       "         type_PAYMENT  type_TRANSFER  \n",
       "0                   1              0  \n",
       "1                   1              0  \n",
       "2                   0              1  \n",
       "3                   0              0  \n",
       "4                   1              0  \n",
       "...               ...            ...  \n",
       "6362615             0              0  \n",
       "6362616             0              1  \n",
       "6362617             0              0  \n",
       "6362618             0              1  \n",
       "6362619             0              0  \n",
       "\n",
       "[6362620 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18e88062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation matrix')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#multivariate analysis\n",
    "#CORRELATION MATRIX\n",
    "#Higher the number more the correlated columns are\n",
    "\n",
    "corr_matrix = X.corr()\n",
    "sns.clustermap(corr_matrix,cmap='coolwarm',annot=True)\n",
    "plt.title(\"Correlation matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f523a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = final_dataset['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce2115fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6354407\n",
       "1       8213\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e05573b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='isFraud', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(final_dataset['isFraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbb79d4",
   "metadata": {},
   "source": [
    "As we can see our target class is imbalance. So, we’ll try to upsample the data so that the minority class matches with the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bc07317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6354407\n",
       "1    6354407\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "over_sample = SMOTE(random_state=0)\n",
    "X,y = over_sample.fit_resample(X,y)\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d803738e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12708814, 11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8918948b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12708814,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f20e5ba",
   "metadata": {},
   "source": [
    "Now, There is balance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35cf8def",
   "metadata": {},
   "source": [
    "# Feature Importance using Chi-square Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b667551d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2, SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37992445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest',\n",
       "       'newbalanceDest'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_best_col = SelectKBest(chi2,k=5)\n",
    "kbest = chi_best_col.fit_transform(X,y)\n",
    "np.array(final_dataset.drop(columns=['isFraud']).columns)[chi_best_col.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1339bbbb",
   "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>features</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amount</td>\n",
       "      <td>6.426345e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>newbalanceOrig</td>\n",
       "      <td>2.695199e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>oldbalanceOrg</td>\n",
       "      <td>1.707693e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>oldbalanceDest</td>\n",
       "      <td>1.277686e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>newbalanceDest</td>\n",
       "      <td>3.484002e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>step</td>\n",
       "      <td>1.658885e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>type_PAYMENT</td>\n",
       "      <td>2.151495e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>type_TRANSFER</td>\n",
       "      <td>1.872174e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>type_CASH_OUT</td>\n",
       "      <td>1.611527e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>type_DEBIT</td>\n",
       "      <td>4.143200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>isFlaggedFraud</td>\n",
       "      <td>8.436000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          features        scores\n",
       "1           amount  6.426345e+12\n",
       "3   newbalanceOrig  2.695199e+12\n",
       "2    oldbalanceOrg  1.707693e+12\n",
       "4   oldbalanceDest  1.277686e+12\n",
       "5   newbalanceDest  3.484002e+09\n",
       "0             step  1.658885e+08\n",
       "9     type_PAYMENT  2.151495e+06\n",
       "10   type_TRANSFER  1.872174e+06\n",
       "7    type_CASH_OUT  1.611527e+05\n",
       "8       type_DEBIT  4.143200e+04\n",
       "6   isFlaggedFraud  8.436000e+03"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(final_dataset.drop(columns=['isFraud']).columns,chi_best_col.scores_)),columns=['features','scores']).sort_values(by='scores',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71eb0a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#select top 5 features\n",
    "X = final_dataset[['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest','newbalanceDest']]\n",
    "y = final_dataset['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e438b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#standindizing columns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled=scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2562a394",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test split(30% randomly selected data points as test data)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X_scaled,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b54c8f17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2af81d9f",
   "metadata": {},
   "source": [
    "# Applying Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "001745f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)\n",
    "pred = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7db6fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1906243,     124],\n",
       "       [   1449,     970]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4123185e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9991759160010604"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4f1753f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00   1906367\n",
      "           1       0.89      0.40      0.55      2419\n",
      "\n",
      "    accuracy                           1.00   1908786\n",
      "   macro avg       0.94      0.70      0.78   1908786\n",
      "weighted avg       1.00      1.00      1.00   1908786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f0f9f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cddb9112",
   "metadata": {},
   "source": [
    "# Check actual accuracy using cross val score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4c782a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cvs = cross_val_score(logmodel,X,y,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c1a2eef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99643307 0.99836954 0.99784664]\n",
      "0.9975497516929624\n"
     ]
    }
   ],
   "source": [
    "print(cvs)\n",
    "print(cvs.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257cca72",
   "metadata": {},
   "source": [
    "Now, Our model accuracy is 99% and it's very good accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2bd833",
   "metadata": {},
   "source": [
    "Conclusion: \n",
    "1. Our Dataset contains no missing values.\n",
    "2. In starting I dropped some unwanted columns such as nameOrig and nameDest. later on, after performing feature engineering, I selected only 5 important columns.\n",
    "3. We got 99% accuracy after performing Logistic Regression."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}