Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c89cd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fcb3dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Data\n",
    "data=pd.read_csv(\"C:\\\\Users\\\\busra\\\\OneDrive\\\\Masaüstü\\\\creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b3592ad",
   "metadata": {},
   "outputs": [
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       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
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       "4  0.502292  0.219422  0.215153   69.99      0  \n",
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   "id": "da5c0292",
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   "outputs": [
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       "            Time         V1         V2        V3        V4        V5  \\\n",
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       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
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       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
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       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
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       "\n",
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b4da9d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1bd00976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dea74be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "360671a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
    "data[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e294ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This dataset highly unbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b1a5b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperating Data\n",
    "legit_data=data[data.Class==0]\n",
    "fraud_data=data[data.Class==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64fc920b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284315, 31)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legit_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e134f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 31)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50098204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legit_data.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3296013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_data.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11c9b9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>94838.202258</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>-0.006271</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>-0.007860</td>\n",
       "      <td>0.005453</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.009637</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>-0.000072</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>88.291022</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80746.806911</td>\n",
       "      <td>-4.771948</td>\n",
       "      <td>3.623778</td>\n",
       "      <td>-7.033281</td>\n",
       "      <td>4.542029</td>\n",
       "      <td>-3.151225</td>\n",
       "      <td>-1.397737</td>\n",
       "      <td>-5.568731</td>\n",
       "      <td>0.570636</td>\n",
       "      <td>-2.581123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372319</td>\n",
       "      <td>0.713588</td>\n",
       "      <td>0.014049</td>\n",
       "      <td>-0.040308</td>\n",
       "      <td>-0.105130</td>\n",
       "      <td>0.041449</td>\n",
       "      <td>0.051648</td>\n",
       "      <td>0.170575</td>\n",
       "      <td>0.075667</td>\n",
       "      <td>122.211321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
       "0      94838.202258  0.008258 -0.006271  0.012171 -0.007860  0.005453   \n",
       "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
       "\n",
       "             V6        V7        V8        V9  ...       V20       V21  \\\n",
       "Class                                          ...                       \n",
       "0      0.002419  0.009637 -0.000987  0.004467  ... -0.000644 -0.001235   \n",
       "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
       "\n",
       "            V22       V23       V24       V25       V26       V27       V28  \\\n",
       "Class                                                                         \n",
       "0     -0.000024  0.000070  0.000182 -0.000072 -0.000089 -0.000295 -0.000131   \n",
       "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0       88.291022  \n",
       "1      122.211321  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare the values \n",
    "data.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8aa2ef4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build sample dataset containing similar distribution of normal transactions and Fraud Transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7953a254",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Number of fraud transactions --> 492"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cca19dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "legit_sample=legit_data.sample(n=492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4001b4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=pd.concat([legit_sample,fraud_data],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1a0138c2",
   "metadata": {},
   "outputs": [
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       "      <td>0.037702</td>\n",
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       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
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       "18752    29740.0 -1.416732 -0.447520  1.759682 -0.452206  1.775143 -1.131712   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
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       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "20277   0.231688  0.406819 -0.345074 -0.040921  0.063872  328.80      0  \n",
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       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4b8649d6",
   "metadata": {},
   "outputs": [
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       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
       "      <td>-2.943548</td>\n",
       "      <td>-2.208002</td>\n",
       "      <td>1.058733</td>\n",
       "      <td>-1.632333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583276</td>\n",
       "      <td>-0.269209</td>\n",
       "      <td>-0.456108</td>\n",
       "      <td>-0.183659</td>\n",
       "      <td>-0.328168</td>\n",
       "      <td>0.606116</td>\n",
       "      <td>0.884876</td>\n",
       "      <td>-0.253700</td>\n",
       "      <td>245.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281674</th>\n",
       "      <td>170348.0</td>\n",
       "      <td>1.991976</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>-2.583441</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>1.151147</td>\n",
       "      <td>-0.096695</td>\n",
       "      <td>0.223050</td>\n",
       "      <td>-0.068384</td>\n",
       "      <td>0.577829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164350</td>\n",
       "      <td>-0.295135</td>\n",
       "      <td>-0.072173</td>\n",
       "      <td>-0.450261</td>\n",
       "      <td>0.313267</td>\n",
       "      <td>-0.289617</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>42.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
       "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
       "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
       "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "279863 -0.882850  0.697211 -2.064945  ...  0.778584 -0.319189  0.639419   \n",
       "280143 -1.413170  0.248525 -1.127396  ...  0.370612  0.028234 -0.145640   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
       "281144 -2.208002  1.058733 -1.632333  ...  0.583276 -0.269209 -0.456108   \n",
       "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "279863 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00      1  \n",
       "280143 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76      1  \n",
       "280149  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89      1  \n",
       "281144 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00      1  \n",
       "281674 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6741e478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 31)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "afaf06e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    492\n",
       "1    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6a653113",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V8</th>\n",
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       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
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       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93209.971545</td>\n",
       "      <td>-0.178956</td>\n",
       "      <td>-0.146277</td>\n",
       "      <td>-0.105295</td>\n",
       "      <td>0.058993</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>-0.079029</td>\n",
       "      <td>0.081549</td>\n",
       "      <td>-0.007332</td>\n",
       "      <td>0.023716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085291</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>0.010807</td>\n",
       "      <td>-0.030302</td>\n",
       "      <td>0.007428</td>\n",
       "      <td>0.003885</td>\n",
       "      <td>0.005989</td>\n",
       "      <td>0.014760</td>\n",
       "      <td>-0.005428</td>\n",
       "      <td>104.249614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80746.806911</td>\n",
       "      <td>-4.771948</td>\n",
       "      <td>3.623778</td>\n",
       "      <td>-7.033281</td>\n",
       "      <td>4.542029</td>\n",
       "      <td>-3.151225</td>\n",
       "      <td>-1.397737</td>\n",
       "      <td>-5.568731</td>\n",
       "      <td>0.570636</td>\n",
       "      <td>-2.581123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372319</td>\n",
       "      <td>0.713588</td>\n",
       "      <td>0.014049</td>\n",
       "      <td>-0.040308</td>\n",
       "      <td>-0.105130</td>\n",
       "      <td>0.041449</td>\n",
       "      <td>0.051648</td>\n",
       "      <td>0.170575</td>\n",
       "      <td>0.075667</td>\n",
       "      <td>122.211321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
       "0      93209.971545 -0.178956 -0.146277 -0.105295  0.058993  0.000040   \n",
       "1      80746.806911 -4.771948  3.623778 -7.033281  4.542029 -3.151225   \n",
       "\n",
       "             V6        V7        V8        V9  ...       V20       V21  \\\n",
       "Class                                          ...                       \n",
       "0     -0.079029  0.081549 -0.007332  0.023716  ... -0.085291  0.003072   \n",
       "1     -1.397737 -5.568731  0.570636 -2.581123  ...  0.372319  0.713588   \n",
       "\n",
       "            V22       V23       V24       V25       V26       V27       V28  \\\n",
       "Class                                                                         \n",
       "0      0.010807 -0.030302  0.007428  0.003885  0.005989  0.014760 -0.005428   \n",
       "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0      104.249614  \n",
       "1      122.211321  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "61f11890",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into target and features\n",
    "X = new_data.drop(columns='Class', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6cfedffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "      <th>20277</th>\n",
       "      <td>30917.0</td>\n",
       "      <td>0.512525</td>\n",
       "      <td>-0.793732</td>\n",
       "      <td>0.015291</td>\n",
       "      <td>1.390617</td>\n",
       "      <td>-0.494510</td>\n",
       "      <td>-0.159396</td>\n",
       "      <td>0.424986</td>\n",
       "      <td>0.003035</td>\n",
       "      <td>-0.285064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440822</td>\n",
       "      <td>0.307370</td>\n",
       "      <td>0.151219</td>\n",
       "      <td>-0.331159</td>\n",
       "      <td>0.231688</td>\n",
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       "      <td>-0.345074</td>\n",
       "      <td>-0.040921</td>\n",
       "      <td>0.063872</td>\n",
       "      <td>328.80</td>\n",
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       "    <tr>\n",
       "      <th>191898</th>\n",
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       "      <td>1.159347</td>\n",
       "      <td>-1.041348</td>\n",
       "      <td>0.438702</td>\n",
       "      <td>0.027504</td>\n",
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       "      <td>0.486801</td>\n",
       "      <td>0.008256</td>\n",
       "      <td>0.030363</td>\n",
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       "      <th>66940</th>\n",
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       "      <td>0.188720</td>\n",
       "      <td>1.408557</td>\n",
       "      <td>2.518961</td>\n",
       "      <td>-0.592347</td>\n",
       "      <td>0.660459</td>\n",
       "      <td>-0.678004</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>-0.103739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151849</td>\n",
       "      <td>-0.058223</td>\n",
       "      <td>-0.054760</td>\n",
       "      <td>-0.005162</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.306423</td>\n",
       "      <td>-0.040206</td>\n",
       "      <td>0.037702</td>\n",
       "      <td>0.021906</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>161638</th>\n",
       "      <td>114382.0</td>\n",
       "      <td>0.043421</td>\n",
       "      <td>1.198305</td>\n",
       "      <td>0.403129</td>\n",
       "      <td>2.874416</td>\n",
       "      <td>1.071152</td>\n",
       "      <td>0.416551</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.091221</td>\n",
       "      <td>-1.029156</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.358295</td>\n",
       "      <td>0.208073</td>\n",
       "      <td>0.615432</td>\n",
       "      <td>-0.005591</td>\n",
       "      <td>0.637580</td>\n",
       "      <td>-0.709490</td>\n",
       "      <td>-0.112627</td>\n",
       "      <td>-0.005945</td>\n",
       "      <td>0.136796</td>\n",
       "      <td>3.94</td>\n",
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       "    <tr>\n",
       "      <th>18752</th>\n",
       "      <td>29740.0</td>\n",
       "      <td>-1.416732</td>\n",
       "      <td>-0.447520</td>\n",
       "      <td>1.759682</td>\n",
       "      <td>-0.452206</td>\n",
       "      <td>1.775143</td>\n",
       "      <td>-1.131712</td>\n",
       "      <td>0.164895</td>\n",
       "      <td>-0.083645</td>\n",
       "      <td>-0.117206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261775</td>\n",
       "      <td>-0.237903</td>\n",
       "      <td>-1.049768</td>\n",
       "      <td>-0.093817</td>\n",
       "      <td>-0.680442</td>\n",
       "      <td>0.340180</td>\n",
       "      <td>0.048564</td>\n",
       "      <td>-0.231139</td>\n",
       "      <td>-0.150943</td>\n",
       "      <td>5.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "20277    30917.0  0.512525 -0.793732  0.015291  1.390617 -0.494510 -0.159396   \n",
       "191898  129448.0 -0.188249  0.483333  1.159347 -1.041348  0.438702  0.027504   \n",
       "66940    52288.0  1.106340  0.188720  1.408557  2.518961 -0.592347  0.660459   \n",
       "161638  114382.0  0.043421  1.198305  0.403129  2.874416  1.071152  0.416551   \n",
       "18752    29740.0 -1.416732 -0.447520  1.759682 -0.452206  1.775143 -1.131712   \n",
       "\n",
       "              V7        V8        V9  ...       V20       V21       V22  \\\n",
       "20277   0.424986  0.003035 -0.285064  ...  0.440822  0.307370  0.151219   \n",
       "191898  0.355494  0.051625  0.272816  ... -0.058968  0.234057  0.815181   \n",
       "66940  -0.678004  0.341853 -0.103739  ... -0.151849 -0.058223 -0.054760   \n",
       "161638  0.783545  0.091221 -1.029156  ... -0.358295  0.208073  0.615432   \n",
       "18752   0.164895 -0.083645 -0.117206  ...  0.261775 -0.237903 -1.049768   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \n",
       "20277  -0.331159  0.231688  0.406819 -0.345074 -0.040921  0.063872  328.80  \n",
       "191898 -0.259151  0.567397  0.038255  0.486801  0.008256  0.030363    5.48  \n",
       "66940  -0.005162  0.000608  0.306423 -0.040206  0.037702  0.021906    0.00  \n",
       "161638 -0.005591  0.637580 -0.709490 -0.112627 -0.005945  0.136796    3.94  \n",
       "18752  -0.093817 -0.680442  0.340180  0.048564 -0.231139 -0.150943    5.49  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "60bae27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=new_data[\"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c287dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20277     0\n",
       "191898    0\n",
       "66940     0\n",
       "161638    0\n",
       "18752     0\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "34b780f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into train and test\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a4c8522d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 30) (787, 30) (197, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a53f75de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984,) (787,) (197,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape, Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5f16e1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "model=LogisticRegression()\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "52bde11b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data 0.9212198221092758\n"
     ]
    }
   ],
   "source": [
    "#Accuracy on Training Data\n",
    "X_train_predict=model.predict(X_train)\n",
    "X_train_score=accuracy_score(X_train_predict,Y_train)\n",
    "print(\"Accuracy on training data {}\".format(X_train_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f7bb429a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data 0.8934010152284264\n"
     ]
    }
   ],
   "source": [
    "#Accuracy on Test Data\n",
    "X_test_predict=model.predict(X_test)\n",
    "X_test_score=accuracy_score(X_test_predict,Y_test)\n",
    "print(\"Accuracy on training data {}\".format(X_test_score))"
   ]
  }
 ],
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