12-Credit-Card-Fraud-Detection/Credit_Card_Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90a0ea98",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ab8dd3",
   "metadata": {},
   "source": [
    "### Importing all dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1143a5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c3fde8",
   "metadata": {},
   "source": [
    "### Data Collection and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90ae1207",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "card_dataset = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abf3dc59",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
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       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
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       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
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       "      <td>-0.407193</td>\n",
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       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\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",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "card_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a1254837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset size\n",
    "card_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5464c37",
   "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"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing values check\n",
    "card_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79bdcc57",
   "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": [
    "# detail information of dataset\n",
    "card_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9958c046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label dristribution of class\n",
    "card_dataset[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a141cf2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate legit and fraud transactions\n",
    "legit = card_dataset[card_dataset.Class == 0]\n",
    "fraud = card_dataset[card_dataset.Class == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "318799a8",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistial measure of legit transactions\n",
    "legit.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "589889fc",
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistial measure of fraud transactions\n",
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a2506f76",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</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",
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       "      <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",
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       "</table>\n",
       "<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",
       "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare legit and fraud transactions\n",
    "card_dataset.groupby(\"Class\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600c4a86",
   "metadata": {},
   "source": [
    "### Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84259d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# taking samples as same number of fraud \n",
    "legit_sample = legit.sample(n = 492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "26bb993f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a new dataset with same number of data\n",
    "new_dataset = pd.concat([legit_sample, fraud], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "763b085c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 31)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of new dataset\n",
    "new_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "058c2753",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
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       "      <td>0.308675</td>\n",
       "      <td>-0.832398</td>\n",
       "      <td>0.207965</td>\n",
       "      <td>1.815027</td>\n",
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       "      <td>0.157354</td>\n",
       "      <td>0.437040</td>\n",
       "      <td>-0.072913</td>\n",
       "      <td>-1.396938</td>\n",
       "      <td>-0.235147</td>\n",
       "      <td>0.803162</td>\n",
       "      <td>-0.057411</td>\n",
       "      <td>-0.065478</td>\n",
       "      <td>92.00</td>\n",
       "      <td>0</td>\n",
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       "      <th>213932</th>\n",
       "      <td>139456.0</td>\n",
       "      <td>-1.277138</td>\n",
       "      <td>-0.874462</td>\n",
       "      <td>-2.274415</td>\n",
       "      <td>-1.390290</td>\n",
       "      <td>-1.397960</td>\n",
       "      <td>-0.642320</td>\n",
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       "      <td>-0.441831</td>\n",
       "      <td>-2.011829</td>\n",
       "      <td>-0.363278</td>\n",
       "      <td>-0.017506</td>\n",
       "      <td>0.387895</td>\n",
       "      <td>691.06</td>\n",
       "      <td>0</td>\n",
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       "      <th>251687</th>\n",
       "      <td>155459.0</td>\n",
       "      <td>0.333215</td>\n",
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       "      <td>-1.354412</td>\n",
       "      <td>-1.191390</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.279061</td>\n",
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       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "189720  128533.0  1.914426 -0.059760 -1.889765  0.385481  0.324276 -0.913124   \n",
       "35268    38041.0  0.879737 -1.018706  1.093509  0.603820 -1.108173  1.083966   \n",
       "173370  121458.0  1.915051 -1.142433 -1.038924 -0.968232 -0.492977  0.308675   \n",
       "213932  139456.0 -1.277138 -0.874462 -2.274415 -1.390290 -1.397960 -0.642320   \n",
       "251687  155459.0  0.333215 -0.738123  1.013667 -2.833729 -0.068939 -0.202753   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "189720  0.211482 -0.203588  0.383061  ...  0.264476  0.765599 -0.115800   \n",
       "35268  -1.000103  0.434826 -0.673775  ... -0.194755 -0.221220  0.014386   \n",
       "173370 -0.832398  0.207965  1.815027  ...  0.157354  0.437040 -0.072913   \n",
       "213932  2.844168 -0.001887  0.579779  ...  0.100539 -0.854019  2.124905   \n",
       "251687  0.897208 -1.354412 -1.191390  ... -0.279061  0.395266 -0.250831   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "189720 -0.463005  0.221878 -0.100886 -0.016207 -0.035439   62.48      0  \n",
       "35268  -0.317151  0.046476 -0.369723  0.096559  0.050487  144.00      0  \n",
       "173370 -1.396938 -0.235147  0.803162 -0.057411 -0.065478   92.00      0  \n",
       "213932 -0.441831 -2.011829 -0.363278 -0.017506  0.387895  691.06      0  \n",
       "251687 -0.351469 -0.573505 -0.426285 -1.257627 -1.072651   50.00      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legit at top\n",
    "new_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf259581",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>77.89</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
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       "            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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fraud at end\n",
    "new_dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e02fe9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    492\n",
       "1    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check distribution\n",
    "new_dataset[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6845791b",
   "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>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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92589.617886</td>\n",
       "      <td>-0.070570</td>\n",
       "      <td>-0.007636</td>\n",
       "      <td>-0.020860</td>\n",
       "      <td>0.036153</td>\n",
       "      <td>-0.086780</td>\n",
       "      <td>0.077418</td>\n",
       "      <td>-0.014098</td>\n",
       "      <td>0.121498</td>\n",
       "      <td>0.007404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014907</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>-0.016968</td>\n",
       "      <td>-0.044839</td>\n",
       "      <td>0.020211</td>\n",
       "      <td>-0.020845</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>-0.005121</td>\n",
       "      <td>-0.021020</td>\n",
       "      <td>98.026768</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      92589.617886 -0.070570 -0.007636 -0.020860  0.036153 -0.086780   \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      0.077418 -0.014098  0.121498  0.007404  ...  0.014907  0.006715   \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     -0.016968 -0.044839  0.020211 -0.020845  0.018390 -0.005121 -0.021020   \n",
       "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0       98.026768  \n",
       "1      122.211321  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking data status now\n",
    "new_dataset.groupby(\"Class\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4126df83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split feature and target\n",
    "X = new_dataset.drop([\"Class\"], axis = 1)\n",
    "Y = new_dataset[\"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8cf669bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time        V1        V2        V3        V4        V5        V6  \\\n",
      "189720  128533.0  1.914426 -0.059760 -1.889765  0.385481  0.324276 -0.913124   \n",
      "35268    38041.0  0.879737 -1.018706  1.093509  0.603820 -1.108173  1.083966   \n",
      "173370  121458.0  1.915051 -1.142433 -1.038924 -0.968232 -0.492977  0.308675   \n",
      "213932  139456.0 -1.277138 -0.874462 -2.274415 -1.390290 -1.397960 -0.642320   \n",
      "251687  155459.0  0.333215 -0.738123  1.013667 -2.833729 -0.068939 -0.202753   \n",
      "...          ...       ...       ...       ...       ...       ...       ...   \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  ...       V20       V21       V22  \\\n",
      "189720  0.211482 -0.203588  0.383061  ... -0.086751  0.264476  0.765599   \n",
      "35268  -1.000103  0.434826 -0.673775  ... -0.306186 -0.194755 -0.221220   \n",
      "173370 -0.832398  0.207965  1.815027  ... -0.043981  0.157354  0.437040   \n",
      "213932  2.844168 -0.001887  0.579779  ...  1.170725  0.100539 -0.854019   \n",
      "251687  0.897208 -1.354412 -1.191390  ... -0.159340 -0.279061  0.395266   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "279863 -0.882850  0.697211 -2.064945  ...  1.252967  0.778584 -0.319189   \n",
      "280143 -1.413170  0.248525 -1.127396  ...  0.226138  0.370612  0.028234   \n",
      "280149 -2.234739  1.210158 -0.652250  ...  0.247968  0.751826  0.834108   \n",
      "281144 -2.208002  1.058733 -1.632333  ...  0.306271  0.583276 -0.269209   \n",
      "281674  0.223050 -0.068384  0.577829  ... -0.017652 -0.164350 -0.295135   \n",
      "\n",
      "             V23       V24       V25       V26       V27       V28  Amount  \n",
      "189720 -0.115800 -0.463005  0.221878 -0.100886 -0.016207 -0.035439   62.48  \n",
      "35268   0.014386 -0.317151  0.046476 -0.369723  0.096559  0.050487  144.00  \n",
      "173370 -0.072913 -1.396938 -0.235147  0.803162 -0.057411 -0.065478   92.00  \n",
      "213932  2.124905 -0.441831 -2.011829 -0.363278 -0.017506  0.387895  691.06  \n",
      "251687 -0.250831 -0.351469 -0.573505 -0.426285 -1.257627 -1.072651   50.00  \n",
      "...          ...       ...       ...       ...       ...       ...     ...  \n",
      "279863  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00  \n",
      "280143 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76  \n",
      "280149  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89  \n",
      "281144 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00  \n",
      "281674 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53  \n",
      "\n",
      "[984 rows x 30 columns]\n",
      "189720    0\n",
      "35268     0\n",
      "173370    0\n",
      "213932    0\n",
      "251687    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(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c105cd",
   "metadata": {},
   "source": [
    "### Split Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4e0377d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 52,
   "id": "e8423b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  (984, 30)\n",
      "Train data:  (787, 30)\n",
      "Test data:  (197, 30)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total data: \", X.shape)\n",
    "print(\"Train data: \", X_train.shape)\n",
    "print(\"Test data: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fd3ace84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time         V1        V2         V3        V4         V5  \\\n",
      "100623   67571.0  -0.758469 -0.045410  -0.168438 -1.313275  -1.901763   \n",
      "39993    40049.0   0.997536 -0.483349   0.639525  0.078657  -0.520133   \n",
      "21851    31896.0  -1.196331  1.807690   0.063990 -0.138481  -0.107184   \n",
      "176371  122736.0  -1.692029 -3.147795  -1.576210 -0.989689  -3.345302   \n",
      "197651  132113.0   1.836617 -0.972652  -1.318563 -0.129708  -0.761210   \n",
      "...          ...        ...       ...        ...       ...        ...   \n",
      "196789  131693.0  -0.702591  0.324183   1.596207 -0.863510   0.620760   \n",
      "161135  113913.0   1.864616  0.242107  -0.183291  4.028989  -0.005995   \n",
      "223618  143456.0  -2.006582  3.676577  -5.463811  7.232058  -1.627859   \n",
      "43061    41353.0 -15.020981  8.075240 -16.298091  5.664820 -11.918153   \n",
      "29283    35396.0   1.294767 -1.560889   0.212874 -1.601118  -1.347939   \n",
      "\n",
      "              V6         V7        V8        V9  ...       V20       V21  \\\n",
      "100623  0.739433   3.071892 -0.483422  0.618203  ... -0.032500  0.042619   \n",
      "39993   0.542609  -0.514634  0.319370  0.374146  ...  0.000076  0.074358   \n",
      "21851  -0.538460   0.018700  0.808529 -0.585721  ...  0.096389 -0.266759   \n",
      "176371  0.773560   4.665649 -0.654322 -1.430945  ...  2.535888  0.839357   \n",
      "197651 -1.471096   0.249034 -0.564969 -0.843176  ... -0.223372 -0.383698   \n",
      "...          ...        ...       ...       ...  ...       ...       ...   \n",
      "196789 -0.031285   0.125338  0.298423 -0.009708  ... -0.023525 -0.000339   \n",
      "161135  0.453585  -0.307622  0.173418 -0.124106  ... -0.380116  0.094753   \n",
      "223618 -0.996755  -4.299833  2.268867 -3.651067  ...  0.474414  0.713907   \n",
      "43061  -4.246957 -14.716668  9.435084 -6.795398  ... -0.995787  2.525115   \n",
      "29283   0.224842  -1.216302  0.049378 -1.783357  ... -0.017727 -0.009820   \n",
      "\n",
      "             V22       V23       V24       V25       V26       V27       V28  \\\n",
      "100623  0.397224  0.072229 -0.242276  0.560916 -0.540955  0.150606 -0.117140   \n",
      "39993   0.237735 -0.002663 -0.194618  0.063025  1.048755 -0.040685  0.002527   \n",
      "21851  -0.853203 -0.035240 -0.620670  0.090816  0.139021  0.114530  0.013392   \n",
      "176371  0.345803  2.915971 -0.164691 -0.670892 -0.559512 -0.173259  0.338650   \n",
      "197651 -0.818044  0.194463  0.489407 -0.295991  0.430722 -0.092084 -0.028503   \n",
      "...          ...       ...       ...       ...       ...       ...       ...   \n",
      "196789 -0.159329 -0.002705  0.652726 -0.335830 -0.664455  0.128934  0.149891   \n",
      "161135  0.478547  0.179081  1.063898  0.024310  0.085026 -0.011115 -0.039143   \n",
      "223618 -0.063868  0.167947 -0.449864  0.023702  0.536905  0.485864 -0.042393   \n",
      "43061  -0.832074 -0.186117  0.429781  0.697103  0.056031 -1.310888 -0.707403   \n",
      "29283   0.069223 -0.267759 -0.840626  0.418213 -0.061459  0.022565  0.027025   \n",
      "\n",
      "         Amount  \n",
      "100623   549.06  \n",
      "39993     70.00  \n",
      "21851      9.12  \n",
      "176371  1199.00  \n",
      "197651   173.97  \n",
      "...         ...  \n",
      "196789     1.18  \n",
      "161135     1.51  \n",
      "223618     1.00  \n",
      "43061     34.12  \n",
      "29283    138.00  \n",
      "\n",
      "[787 rows x 30 columns]\n",
      "100623    1\n",
      "39993     0\n",
      "21851     0\n",
      "176371    0\n",
      "197651    0\n",
      "         ..\n",
      "196789    0\n",
      "161135    0\n",
      "223618    1\n",
      "43061     1\n",
      "29283     0\n",
      "Name: Class, Length: 787, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16816f28",
   "metadata": {},
   "source": [
    "### Model training with Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8c44a985",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(max_iter = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bfba0c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59d482a",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb7baa22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.9491740787801779\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
    "print(\"Accuracy on training data: \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "046b1cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.9543147208121827\n"
     ]
    }
   ],
   "source": [
    "# accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print(\"Accuracy on training data: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa89cd05",
   "metadata": {},
   "source": [
    "### Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "88054605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.53761000e+05  1.14625927e+00  1.40345824e+00 -4.15914819e+00\n",
      "  2.66010706e+00 -3.23216827e-01 -1.83607089e+00 -1.62374006e+00\n",
      "  2.59562498e-01 -1.13204415e+00 -3.35647371e+00  3.64647784e+00\n",
      " -3.00268416e+00 -6.47500580e-01 -5.94500331e+00  1.74650037e-01\n",
      " -1.47563951e+00 -3.08227353e+00  2.24739755e-01 -3.00930779e-01\n",
      "  2.84830656e-01  5.64449516e-01  4.45743912e-01 -1.41136197e-01\n",
      " -2.65517116e-01  3.62259920e-01 -4.16062098e-01  5.07369853e-01\n",
      "  2.43744114e-01  5.13700000e+01]\n"
     ]
    }
   ],
   "source": [
    "sample = X_test.iloc[0].values\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8c6862c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4cbe4aa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "It's a Fraud transaction\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = sample\n",
    "\n",
    "# reshape the numpy array as we're predicting for one instance\n",
    "input_data_reshaped = input_data.reshape(1,-1)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0] == 0):\n",
    "    print(\"It's a Legit transaction\")\n",
    "else:\n",
    "    print(\"It's a Fraud transaction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d696dfa1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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