CreditCardFraudDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_data = pd.read_csv('creditcard.csv(1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>V27</th>\n",
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       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
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       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
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       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
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       "      <td>0.817739</td>\n",
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       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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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",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-3.249640</td>\n",
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       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
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       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
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       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "credit_card_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284315, 31)\n",
      "(492, 31)\n"
     ]
    }
   ],
   "source": [
    "legit = credit_card_data[credit_card_data.Class == 0]\n",
    "fraud = credit_card_data[credit_card_data.Class == 1]\n",
    "print(legit.shape)\n",
    "print(fraud.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(credit_card_data['Class'], \n",
    "              hue =credit_card_data['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legit.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Time</th>\n",
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       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
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       "      <th>Class</th>\n",
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       "      <td>-0.007860</td>\n",
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       "      <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",
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       "<p>2 rows × 30 columns</p>\n",
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      ],
      "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",
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       "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "legit_sample = legit.sample(n=492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dataset = pd.concat([legit_sample, fraud], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "            Time         V1         V2        V3        V4        V5  \\\n",
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       "60683    49447.0   1.256033   0.327598  0.296121  0.695885 -0.401513   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
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       "161125 -0.598181 -0.172077 -0.081741  1.387948  ... -0.022539 -0.558454   \n",
       "179589 -1.261684  1.440955 -1.974780  3.175489  ... -6.302237  1.385096   \n",
       "130625  0.128877 -0.388814  0.740883 -0.608889  ...  0.132209  0.068173   \n",
       "60683  -1.078707  0.062467 -0.183081  0.087398  ... -0.291983 -0.860519   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "118975 -0.008515 -1.178462  0.379866 -0.445959 -0.011218 -0.008855   30.00   \n",
       "161125  0.154462 -0.069734 -0.573571 -0.644584 -0.018367  0.013465  275.19   \n",
       "179589  9.706584 -0.459025  3.844345  1.080716  4.956354  0.739841  456.12   \n",
       "130625  0.199860  0.043344 -0.382781  0.064472 -0.792461  0.214311    9.80   \n",
       "60683   0.132844  0.329959  0.204549  0.095934 -0.025472  0.030028    1.98   \n",
       "\n",
       "        Class  \n",
       "118975      0  \n",
       "161125      0  \n",
       "179589      0  \n",
       "130625      0  \n",
       "60683       0  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "new_dataset.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    492\n",
       "0    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "new_dataset['Class'].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
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       "\n",
       "             V6        V7        V8        V9  ...       V20       V21  \\\n",
       "Class                                          ...                       \n",
       "0      0.039224 -0.001300  0.012407  0.019017  ... -0.015931 -0.043196   \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     -0.014295  0.058599 -0.004916 -0.022146 -0.015166  0.023947 -0.005898   \n",
       "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0      105.656728  \n",
       "1      122.211321  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time         V1         V2        V3        V4        V5  \\\n",
      "118975   75282.0   1.391477  -0.505772 -0.417184 -1.096165 -0.010765   \n",
      "161125  113904.0   1.456666  -1.220570 -0.578749  0.685096 -1.041336   \n",
      "179589  124147.0 -19.163828 -18.229512 -2.843465  4.215061  6.327694   \n",
      "130625   79394.0  -2.339830   2.498586  0.263805  2.670226 -0.695142   \n",
      "60683    49447.0   1.256033   0.327598  0.296121  0.695885 -0.401513   \n",
      "...          ...        ...        ...       ...       ...       ...   \n",
      "279863  169142.0  -1.927883   1.125653 -4.518331  1.749293 -1.566487   \n",
      "280143  169347.0   1.378559   1.289381 -5.004247  1.411850  0.442581   \n",
      "280149  169351.0  -0.676143   1.126366 -2.213700  0.468308 -1.120541   \n",
      "281144  169966.0  -3.113832   0.585864 -5.399730  1.817092 -0.840618   \n",
      "281674  170348.0   1.991976   0.158476 -2.583441  0.408670  1.151147   \n",
      "\n",
      "              V6        V7        V8        V9  ...        V20       V21  \\\n",
      "118975  0.155183 -0.354105  0.048728 -1.197452  ...   0.091278 -0.195569   \n",
      "161125 -0.598181 -0.172077 -0.081741  1.387948  ...   0.251824 -0.022539   \n",
      "179589 -1.261684  1.440955 -1.974780  3.175489  ... -16.116707 -6.302237   \n",
      "130625  0.128877 -0.388814  0.740883 -0.608889  ...  -0.002751  0.132209   \n",
      "60683  -1.078707  0.062467 -0.183081  0.087398  ...  -0.103273 -0.291983   \n",
      "...          ...       ...       ...       ...  ...        ...       ...   \n",
      "279863 -2.010494 -0.882850  0.697211 -2.064945  ...   1.252967  0.778584   \n",
      "280143 -1.326536 -1.413170  0.248525 -1.127396  ...   0.226138  0.370612   \n",
      "280149 -0.003346 -2.234739  1.210158 -0.652250  ...   0.247968  0.751826   \n",
      "281144 -2.943548 -2.208002  1.058733 -1.632333  ...   0.306271  0.583276   \n",
      "281674 -0.096695  0.223050 -0.068384  0.577829  ...  -0.017652 -0.164350   \n",
      "\n",
      "             V22       V23       V24       V25       V26       V27       V28  \\\n",
      "118975 -0.758369 -0.008515 -1.178462  0.379866 -0.445959 -0.011218 -0.008855   \n",
      "161125 -0.558454  0.154462 -0.069734 -0.573571 -0.644584 -0.018367  0.013465   \n",
      "179589  1.385096  9.706584 -0.459025  3.844345  1.080716  4.956354  0.739841   \n",
      "130625  0.068173  0.199860  0.043344 -0.382781  0.064472 -0.792461  0.214311   \n",
      "60683  -0.860519  0.132844  0.329959  0.204549  0.095934 -0.025472  0.030028   \n",
      "...          ...       ...       ...       ...       ...       ...       ...   \n",
      "279863 -0.319189  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968   \n",
      "280143  0.028234 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637   \n",
      "280149  0.834108  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   \n",
      "281144 -0.269209 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700   \n",
      "281674 -0.295135 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   \n",
      "\n",
      "        Amount  \n",
      "118975   30.00  \n",
      "161125  275.19  \n",
      "179589  456.12  \n",
      "130625    9.80  \n",
      "60683     1.98  \n",
      "...        ...  \n",
      "279863  390.00  \n",
      "280143    0.76  \n",
      "280149   77.89  \n",
      "281144  245.00  \n",
      "281674   42.53  \n",
      "\n",
      "[984 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "X = new_dataset.drop(columns='Class', axis=1)\n",
    "Y = new_dataset['Class']\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118975    0\n",
      "161125    0\n",
      "179589    0\n",
      "130625    0\n",
      "60683     0\n",
      "         ..\n",
      "279863    1\n",
      "280143    1\n",
      "280149    1\n",
      "281144    1\n",
      "281674    1\n",
      "Name: Class, Length: 984, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 30) (787, 30) (197, 30)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, \n",
    "                                                    Y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    stratify=Y, \n",
    "                                                    random_state=2)\n",
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training data:  0.9390088945362135\n"
     ]
    }
   ],
   "source": [
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
    "print('Accuracy on Training data: ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on Test Data:  0.9289340101522843\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score on Test Data: ', test_data_accuracy)"
   ]
  }
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