Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\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,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_data = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
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       "      <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",
       "      <th>3</th>\n",
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       "      <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>-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",
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       "      <td>-0.270533</td>\n",
       "      <td>0.817739</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",
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       "      <td>69.99</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",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
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       "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]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>24.79</td>\n",
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       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
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       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
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       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
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       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
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       "      <td>10.00</td>\n",
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       "      <th>284806</th>\n",
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       "      <td>-0.189733</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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "credit_card_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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      " 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": [
    "credit_card_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "credit_card_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This Dataset is very Unbalanced. So separating data for further analysis.\n",
    "\n",
    "#0 --- Normal Transaction\n",
    "#1 --- Fraud Transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = credit_card_data[credit_card_data.Class == 0]\n",
    "fraud = credit_card_data[credit_card_data.Class == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284315, 31)\n"
     ]
    }
   ],
   "source": [
    "print(normal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(492, 31)\n"
     ]
    }
   ],
   "source": [
    "print(fraud.shape)"
   ]
  },
  {
   "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": [
    "normal.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": {
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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",
       "      <th>V6</th>\n",
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       "      <th>V8</th>\n",
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </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      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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare the values for both transaction\n",
    "\n",
    "credit_card_data.groupby(\"Class\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Under-Sampling\n",
    "#Build a sample dataset containing similar distribution of normal transactions and fraud transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_sample = normal.sample(n = 492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Concatenation of two DataFrames\n",
    "\n",
    "new_credit_card_data = pd.concat([normal_sample, fraud], axis = 0) #axis = 0 means the dataset added one by one, all the 492 values wiil be added below the normal_sample.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
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       "      <th>V9</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",
       "    <tr>\n",
       "      <th>31526</th>\n",
       "      <td>36398.0</td>\n",
       "      <td>0.700918</td>\n",
       "      <td>-2.068451</td>\n",
       "      <td>0.514509</td>\n",
       "      <td>-0.202706</td>\n",
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       "      <td>-0.917818</td>\n",
       "      <td>0.212001</td>\n",
       "      <td>0.438227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361093</td>\n",
       "      <td>0.626203</td>\n",
       "      <td>-0.518362</td>\n",
       "      <td>-0.675317</td>\n",
       "      <td>0.526183</td>\n",
       "      <td>0.057294</td>\n",
       "      <td>0.015174</td>\n",
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       "      <td>343.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71710</th>\n",
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       "      <td>2.521979</td>\n",
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       "      <td>1.375512</td>\n",
       "      <td>-0.187984</td>\n",
       "      <td>0.480556</td>\n",
       "      <td>1.256914</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.252804</td>\n",
       "      <td>-0.030281</td>\n",
       "      <td>-0.299326</td>\n",
       "      <td>-0.602380</td>\n",
       "      <td>0.289772</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>-0.078057</td>\n",
       "      <td>51.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241397</th>\n",
       "      <td>151009.0</td>\n",
       "      <td>-0.260854</td>\n",
       "      <td>0.049315</td>\n",
       "      <td>1.709005</td>\n",
       "      <td>-0.762225</td>\n",
       "      <td>-0.077438</td>\n",
       "      <td>0.472883</td>\n",
       "      <td>-0.091778</td>\n",
       "      <td>0.114072</td>\n",
       "      <td>0.952890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.416559</td>\n",
       "      <td>1.447428</td>\n",
       "      <td>0.072094</td>\n",
       "      <td>0.782202</td>\n",
       "      <td>-1.282285</td>\n",
       "      <td>-0.551094</td>\n",
       "      <td>0.019923</td>\n",
       "      <td>0.065769</td>\n",
       "      <td>29.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105959</th>\n",
       "      <td>69762.0</td>\n",
       "      <td>-1.694846</td>\n",
       "      <td>-0.070450</td>\n",
       "      <td>2.498739</td>\n",
       "      <td>-0.753366</td>\n",
       "      <td>-0.863360</td>\n",
       "      <td>-1.012253</td>\n",
       "      <td>-0.551375</td>\n",
       "      <td>0.597930</td>\n",
       "      <td>0.038814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.392733</td>\n",
       "      <td>0.611578</td>\n",
       "      <td>-0.132730</td>\n",
       "      <td>0.787621</td>\n",
       "      <td>0.076628</td>\n",
       "      <td>0.982997</td>\n",
       "      <td>-0.142838</td>\n",
       "      <td>-0.103320</td>\n",
       "      <td>47.25</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>244461</th>\n",
       "      <td>152363.0</td>\n",
       "      <td>0.072480</td>\n",
       "      <td>0.901467</td>\n",
       "      <td>-0.345040</td>\n",
       "      <td>-0.611263</td>\n",
       "      <td>0.856270</td>\n",
       "      <td>-0.623219</td>\n",
       "      <td>0.808004</td>\n",
       "      <td>0.073966</td>\n",
       "      <td>-0.196154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.287370</td>\n",
       "      <td>-0.764153</td>\n",
       "      <td>0.116986</td>\n",
       "      <td>0.639043</td>\n",
       "      <td>-0.493192</td>\n",
       "      <td>0.089237</td>\n",
       "      <td>0.212141</td>\n",
       "      <td>0.074481</td>\n",
       "      <td>4.49</td>\n",
       "      <td>0</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",
       "31526    36398.0  0.700918 -2.068451  0.514509 -0.202706 -1.462429  0.922577   \n",
       "71710    54416.0 -0.459345 -0.164999  2.521979  0.342960 -0.583564  1.375512   \n",
       "241397  151009.0 -0.260854  0.049315  1.709005 -0.762225 -0.077438  0.472883   \n",
       "105959   69762.0 -1.694846 -0.070450  2.498739 -0.753366 -0.863360 -1.012253   \n",
       "244461  152363.0  0.072480  0.901467 -0.345040 -0.611263  0.856270 -0.623219   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "31526  -0.917818  0.212001  0.438227  ...  0.361093  0.626203 -0.518362   \n",
       "71710  -0.187984  0.480556  1.256914  ... -0.076314  0.252804 -0.030281   \n",
       "241397 -0.091778  0.114072  0.952890  ...  0.416559  1.447428  0.072094   \n",
       "105959 -0.551375  0.597930  0.038814  ...  0.392733  0.611578 -0.132730   \n",
       "244461  0.808004  0.073966 -0.196154  ... -0.287370 -0.764153  0.116986   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "31526  -0.675317  0.526183  0.057294  0.015174  0.066703  343.00      0  \n",
       "71710  -0.299326 -0.602380  0.289772  0.007035 -0.078057   51.99      0  \n",
       "241397  0.782202 -1.282285 -0.551094  0.019923  0.065769   29.95      0  \n",
       "105959  0.787621  0.076628  0.982997 -0.142838 -0.103320   47.25      0  \n",
       "244461  0.639043 -0.493192  0.089237  0.212141  0.074481    4.49      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</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",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>279863</th>\n",
       "      <td>169142.0</td>\n",
       "      <td>-1.927883</td>\n",
       "      <td>1.125653</td>\n",
       "      <td>-4.518331</td>\n",
       "      <td>1.749293</td>\n",
       "      <td>-1.566487</td>\n",
       "      <td>-2.010494</td>\n",
       "      <td>-0.882850</td>\n",
       "      <td>0.697211</td>\n",
       "      <td>-2.064945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>-0.319189</td>\n",
       "      <td>0.639419</td>\n",
       "      <td>-0.294885</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.788395</td>\n",
       "      <td>0.292680</td>\n",
       "      <td>0.147968</td>\n",
       "      <td>390.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280143</th>\n",
       "      <td>169347.0</td>\n",
       "      <td>1.378559</td>\n",
       "      <td>1.289381</td>\n",
       "      <td>-5.004247</td>\n",
       "      <td>1.411850</td>\n",
       "      <td>0.442581</td>\n",
       "      <td>-1.326536</td>\n",
       "      <td>-1.413170</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>-1.127396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370612</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.145640</td>\n",
       "      <td>-0.081049</td>\n",
       "      <td>0.521875</td>\n",
       "      <td>0.739467</td>\n",
       "      <td>0.389152</td>\n",
       "      <td>0.186637</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>169351.0</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281144</th>\n",
       "      <td>169966.0</td>\n",
       "      <td>-3.113832</td>\n",
       "      <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 31)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>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",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88102.433943</td>\n",
       "      <td>-2.411700</td>\n",
       "      <td>1.873143</td>\n",
       "      <td>-3.518229</td>\n",
       "      <td>2.202471</td>\n",
       "      <td>-1.523072</td>\n",
       "      <td>-0.664004</td>\n",
       "      <td>-2.798437</td>\n",
       "      <td>0.271245</td>\n",
       "      <td>-1.287499</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377609</td>\n",
       "      <td>0.033534</td>\n",
       "      <td>-0.026499</td>\n",
       "      <td>-0.031188</td>\n",
       "      <td>0.024674</td>\n",
       "      <td>0.026264</td>\n",
       "      <td>0.091291</td>\n",
       "      <td>0.035041</td>\n",
       "      <td>97.616748</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47759.682658</td>\n",
       "      <td>5.494088</td>\n",
       "      <td>3.649416</td>\n",
       "      <td>6.222884</td>\n",
       "      <td>3.268343</td>\n",
       "      <td>4.241326</td>\n",
       "      <td>1.810392</td>\n",
       "      <td>5.852132</td>\n",
       "      <td>4.907613</td>\n",
       "      <td>2.323658</td>\n",
       "      <td>...</td>\n",
       "      <td>2.805013</td>\n",
       "      <td>1.164989</td>\n",
       "      <td>1.180704</td>\n",
       "      <td>0.549147</td>\n",
       "      <td>0.685077</td>\n",
       "      <td>0.461337</td>\n",
       "      <td>1.012222</td>\n",
       "      <td>0.436095</td>\n",
       "      <td>209.980853</td>\n",
       "      <td>0.500254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>284.000000</td>\n",
       "      <td>-30.552380</td>\n",
       "      <td>-10.743893</td>\n",
       "      <td>-31.103685</td>\n",
       "      <td>-4.678263</td>\n",
       "      <td>-22.105532</td>\n",
       "      <td>-7.040097</td>\n",
       "      <td>-43.557242</td>\n",
       "      <td>-41.044261</td>\n",
       "      <td>-13.434066</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.797604</td>\n",
       "      <td>-8.887017</td>\n",
       "      <td>-19.254328</td>\n",
       "      <td>-2.028024</td>\n",
       "      <td>-4.781606</td>\n",
       "      <td>-1.154776</td>\n",
       "      <td>-7.263482</td>\n",
       "      <td>-4.092509</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46221.750000</td>\n",
       "      <td>-2.877893</td>\n",
       "      <td>-0.122196</td>\n",
       "      <td>-5.138122</td>\n",
       "      <td>-0.271986</td>\n",
       "      <td>-1.797937</td>\n",
       "      <td>-1.599839</td>\n",
       "      <td>-3.107826</td>\n",
       "      <td>-0.203583</td>\n",
       "      <td>-2.331368</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.162628</td>\n",
       "      <td>-0.518126</td>\n",
       "      <td>-0.245961</td>\n",
       "      <td>-0.379574</td>\n",
       "      <td>-0.311284</td>\n",
       "      <td>-0.285344</td>\n",
       "      <td>-0.061971</td>\n",
       "      <td>-0.065917</td>\n",
       "      <td>1.175000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>81349.500000</td>\n",
       "      <td>-0.805152</td>\n",
       "      <td>1.001542</td>\n",
       "      <td>-1.361701</td>\n",
       "      <td>1.305164</td>\n",
       "      <td>-0.436688</td>\n",
       "      <td>-0.639502</td>\n",
       "      <td>-0.652167</td>\n",
       "      <td>0.161997</td>\n",
       "      <td>-0.723439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167445</td>\n",
       "      <td>0.057660</td>\n",
       "      <td>-0.036405</td>\n",
       "      <td>0.015381</td>\n",
       "      <td>0.041208</td>\n",
       "      <td>-0.014000</td>\n",
       "      <td>0.062277</td>\n",
       "      <td>0.033048</td>\n",
       "      <td>18.550000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>134158.250000</td>\n",
       "      <td>0.917444</td>\n",
       "      <td>2.814266</td>\n",
       "      <td>0.325456</td>\n",
       "      <td>4.250632</td>\n",
       "      <td>0.494931</td>\n",
       "      <td>0.100707</td>\n",
       "      <td>0.266252</td>\n",
       "      <td>0.911796</td>\n",
       "      <td>0.165322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655130</td>\n",
       "      <td>0.585016</td>\n",
       "      <td>0.179409</td>\n",
       "      <td>0.382316</td>\n",
       "      <td>0.389562</td>\n",
       "      <td>0.329931</td>\n",
       "      <td>0.457144</td>\n",
       "      <td>0.221292</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>171983.000000</td>\n",
       "      <td>2.360449</td>\n",
       "      <td>22.057729</td>\n",
       "      <td>3.203999</td>\n",
       "      <td>12.114672</td>\n",
       "      <td>11.095089</td>\n",
       "      <td>6.474115</td>\n",
       "      <td>5.802537</td>\n",
       "      <td>20.007208</td>\n",
       "      <td>4.606710</td>\n",
       "      <td>...</td>\n",
       "      <td>27.202839</td>\n",
       "      <td>8.361985</td>\n",
       "      <td>5.933406</td>\n",
       "      <td>1.091435</td>\n",
       "      <td>2.378822</td>\n",
       "      <td>2.745261</td>\n",
       "      <td>3.052358</td>\n",
       "      <td>1.779364</td>\n",
       "      <td>2125.870000</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     984.000000  984.000000  984.000000  984.000000  984.000000   \n",
       "mean    88102.433943   -2.411700    1.873143   -3.518229    2.202471   \n",
       "std     47759.682658    5.494088    3.649416    6.222884    3.268343   \n",
       "min       284.000000  -30.552380  -10.743893  -31.103685   -4.678263   \n",
       "25%     46221.750000   -2.877893   -0.122196   -5.138122   -0.271986   \n",
       "50%     81349.500000   -0.805152    1.001542   -1.361701    1.305164   \n",
       "75%    134158.250000    0.917444    2.814266    0.325456    4.250632   \n",
       "max    171983.000000    2.360449   22.057729    3.203999   12.114672   \n",
       "\n",
       "               V5          V6          V7          V8          V9  ...  \\\n",
       "count  984.000000  984.000000  984.000000  984.000000  984.000000  ...   \n",
       "mean    -1.523072   -0.664004   -2.798437    0.271245   -1.287499  ...   \n",
       "std      4.241326    1.810392    5.852132    4.907613    2.323658  ...   \n",
       "min    -22.105532   -7.040097  -43.557242  -41.044261  -13.434066  ...   \n",
       "25%     -1.797937   -1.599839   -3.107826   -0.203583   -2.331368  ...   \n",
       "50%     -0.436688   -0.639502   -0.652167    0.161997   -0.723439  ...   \n",
       "75%      0.494931    0.100707    0.266252    0.911796    0.165322  ...   \n",
       "max     11.095089    6.474115    5.802537   20.007208    4.606710  ...   \n",
       "\n",
       "              V21         V22         V23         V24         V25         V26  \\\n",
       "count  984.000000  984.000000  984.000000  984.000000  984.000000  984.000000   \n",
       "mean     0.377609    0.033534   -0.026499   -0.031188    0.024674    0.026264   \n",
       "std      2.805013    1.164989    1.180704    0.549147    0.685077    0.461337   \n",
       "min    -22.797604   -8.887017  -19.254328   -2.028024   -4.781606   -1.154776   \n",
       "25%     -0.162628   -0.518126   -0.245961   -0.379574   -0.311284   -0.285344   \n",
       "50%      0.167445    0.057660   -0.036405    0.015381    0.041208   -0.014000   \n",
       "75%      0.655130    0.585016    0.179409    0.382316    0.389562    0.329931   \n",
       "max     27.202839    8.361985    5.933406    1.091435    2.378822    2.745261   \n",
       "\n",
       "              V27         V28       Amount       Class  \n",
       "count  984.000000  984.000000   984.000000  984.000000  \n",
       "mean     0.091291    0.035041    97.616748    0.500000  \n",
       "std      1.012222    0.436095   209.980853    0.500254  \n",
       "min     -7.263482   -4.092509     0.000000    0.000000  \n",
       "25%     -0.061971   -0.065917     1.175000    0.000000  \n",
       "50%      0.062277    0.033048    18.550000    0.500000  \n",
       "75%      0.457144    0.221292    99.990000    1.000000  \n",
       "max      3.052358    1.779364  2125.870000    1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    492\n",
       "0    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>95458.060976</td>\n",
       "      <td>-0.051451</td>\n",
       "      <td>0.122507</td>\n",
       "      <td>-0.003178</td>\n",
       "      <td>-0.137087</td>\n",
       "      <td>0.105080</td>\n",
       "      <td>0.069729</td>\n",
       "      <td>-0.028144</td>\n",
       "      <td>-0.028145</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037233</td>\n",
       "      <td>0.041629</td>\n",
       "      <td>0.053018</td>\n",
       "      <td>-0.012691</td>\n",
       "      <td>0.042754</td>\n",
       "      <td>0.007899</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.012007</td>\n",
       "      <td>-0.005585</td>\n",
       "      <td>73.022175</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      95458.060976 -0.051451  0.122507 -0.003178 -0.137087  0.105080   \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      0.069729 -0.028144 -0.028145  0.006125  ...  0.037233  0.041629   \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      0.053018 -0.012691  0.042754  0.007899  0.000881  0.012007 -0.005585   \n",
       "1      0.014049 -0.040308 -0.105130  0.041449  0.051648  0.170575  0.075667   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0       73.022175  \n",
       "1      122.211321  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_credit_card_data.groupby(\"Class\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_credit_card_data.drop(columns = \"Class\", axis = 1)\n",
    "Y = new_credit_card_data[\"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time        V1        V2        V3        V4        V5        V6  \\\n",
      "31526    36398.0  0.700918 -2.068451  0.514509 -0.202706 -1.462429  0.922577   \n",
      "71710    54416.0 -0.459345 -0.164999  2.521979  0.342960 -0.583564  1.375512   \n",
      "241397  151009.0 -0.260854  0.049315  1.709005 -0.762225 -0.077438  0.472883   \n",
      "105959   69762.0 -1.694846 -0.070450  2.498739 -0.753366 -0.863360 -1.012253   \n",
      "244461  152363.0  0.072480  0.901467 -0.345040 -0.611263  0.856270 -0.623219   \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",
      "31526  -0.917818  0.212001  0.438227  ...  0.705276  0.361093  0.626203   \n",
      "71710  -0.187984  0.480556  1.256914  ... -0.043261 -0.076314  0.252804   \n",
      "241397 -0.091778  0.114072  0.952890  ... -0.052440  0.416559  1.447428   \n",
      "105959 -0.551375  0.597930  0.038814  ...  0.105220  0.392733  0.611578   \n",
      "244461  0.808004  0.073966 -0.196154  ... -0.014298 -0.287370 -0.764153   \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",
      "31526  -0.518362 -0.675317  0.526183  0.057294  0.015174  0.066703  343.00  \n",
      "71710  -0.030281 -0.299326 -0.602380  0.289772  0.007035 -0.078057   51.99  \n",
      "241397  0.072094  0.782202 -1.282285 -0.551094  0.019923  0.065769   29.95  \n",
      "105959 -0.132730  0.787621  0.076628  0.982997 -0.142838 -0.103320   47.25  \n",
      "244461  0.116986  0.639043 -0.493192  0.089237  0.212141  0.074481    4.49  \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"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31526     0\n",
      "71710     0\n",
      "241397    0\n",
      "105959    0\n",
      "244461    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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify = Y, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(787, 30) (197, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training Data:  0.9123252858958069\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on Training Data: \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test Data:  0.9035532994923858\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy on Test Data: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "This Transaction is Normal.\n"
     ]
    }
   ],
   "source": [
    "credit_card_details = (2,-0.425965884412454,0.960523044882985,1.14110934232219,-0.168252079760302,0.42098688077219,-0.0297275516639742,0.476200948720027,0.260314333074874,-0.56867137571251,-0.371407196834471,1.34126198001957,0.359893837038039,-0.358090652573631,-0.137133700217612,0.517616806555742,0.401725895589603,-0.0581328233640131,0.0686531494425432,-0.0331937877876282,0.0849676720682049,-0.208253514656728,-0.559824796253248,-0.0263976679795373,-0.371426583174346,-0.232793816737034,0.105914779097957,0.253844224739337,0.0810802569229443,3.67)\n",
    "credit_card_values_as_numpy_array = np.asarray(credit_card_details)\n",
    "credit_card_details_reshaped = credit_card_values_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "prediction = model.predict(credit_card_details_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0] == 0):\n",
    "    print(\"This Transaction is Normal.\")\n",
    "else:\n",
    "    print(\"This Transaction is Fraud.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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