creditCard.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Credit Card Fraud Detection</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading or importing the modules for the Preprocessing\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sea"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data PreProcessing</h1>"
   ]
  },
  {
   "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",
       "      <th>V3</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "      <th>Class</th>\n",
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       "      <td>0.133558</td>\n",
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       "      <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",
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       "      <td>0.125895</td>\n",
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       "      <td>0.014724</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\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",
       "      <td>0.005274</td>\n",
       "      <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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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
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       "      <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",
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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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.035030</td>\n",
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       "      <td>24.79</td>\n",
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       "      <td>172788.0</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",
       "      <td>...</td>\n",
       "      <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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       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <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",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</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",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
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       "      <th>284806</th>\n",
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       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
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       "      <td>-0.649617</td>\n",
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       "      <td>0</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": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
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      " 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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Transactions: 284807\n",
      "Genuine Cases: 284315\n",
      "Fraud Cases: 492\n"
     ]
    }
   ],
   "source": [
    "not_fraud = len(df[df.Class == 0])\n",
    "fraud = len(df[df.Class == 1])\n",
    "print(\"Total Transactions: \" + str(not_fraud + fraud))\n",
    "print(\"Genuine Cases: \" + str(not_fraud))\n",
    "print(\"Fraud Cases: \" + str(fraud))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Fraudulant Cases: 0.1727485630620034 %\n"
     ]
    }
   ],
   "source": [
    "frauded = (fraud/(fraud + not_fraud))*100\n",
    "print(\"Percentage of Fraudulant Cases: \" + str(frauded) + \" %\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Exploration and Visualization</h1>\n",
    "<p>From the above we cam infer that there are 0.17 % of fraud cases in the dataset. Now we look at the distribution of the two named features in the dataset. For Time, it is clear that there was a particular duration in the day when most of the transactions took place:</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rowle\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\Users\\rowle\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Graphs for better visualization and understanding\n",
    "# Pie Chart\n",
    "data_df = df.copy()\n",
    "data_df[\" \"] = np.where(data_df[\"Class\"] == 1, \"Fraud\",\"Genuine\")\n",
    "data_df[\" \"].value_counts().plot(kind = \"pie\")\n",
    "\n",
    "# Plotting the named features i.e., Time and Amount\n",
    "x, y = plt.subplots(1, 2, figsize=(18,4), sharex = True)\n",
    "\n",
    "amount_value = df['Amount'].values # values\n",
    "time_value = df['Time'].values # values\n",
    "\n",
    "sea.distplot(amount_value, hist=False, color=\"m\", kde_kws={\"shade\": True}, ax=y[0]).set_title('Distribution of Amount')\n",
    "sea.distplot(time_value, hist=False, color=\"m\", kde_kws={\"shade\": True}, ax=y[1]).set_title('Distribution of Time')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of the feature - Amount\n",
      "-------------------------------\n",
      "count    284807.000000\n",
      "mean         88.349619\n",
      "std         250.120109\n",
      "min           0.000000\n",
      "25%           5.600000\n",
      "50%          22.000000\n",
      "75%          77.165000\n",
      "max       25691.160000\n",
      "Name: Amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Summary of the feature - Amount\" + \"\\n-------------------------------\")\n",
    "print(df[\"Amount\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Normalized_amount'] = scalar.fit_transform(df['Amount'].values.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>V10</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>Class</th>\n",
       "      <th>Normalized_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</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>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <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>-0.166974</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>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.207643</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>0</td>\n",
       "      <td>1.160686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <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>0</td>\n",
       "      <td>0.140534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <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>0</td>\n",
       "      <td>-0.073403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Class  Normalized_amount  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053      0           0.244964  \n",
       "1  0.167170  0.125895 -0.008983  0.014724      0          -0.342475  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752      0           1.160686  \n",
       "3  0.647376 -0.221929  0.062723  0.061458      0           0.140534  \n",
       "4 -0.206010  0.502292  0.219422  0.215153      0          -0.073403  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"Amount\",\"Time\"],inplace=True,axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For any Independent Variable\n",
    "x = df.drop([\"Class\"],axis=1)\n",
    "y = df[\"Class\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Feature Selection(Test Train Splits)</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_Train size: (199364, 29)\n",
      "X_Test size: (85443, 29)\n",
      "Y_Train size: (199364,)\n",
      "Y_Test size: (85443,)\n"
     ]
    }
   ],
   "source": [
    "# x_train is the training independent variable\n",
    "# x_test is the testing independent variable\n",
    "# y_train is the training dependent variable\n",
    "# y_test is the testing dependent variable\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3, random_state=1)\n",
    "print(\"X_Train size: \" + str(x_train.shape))\n",
    "print(\"X_Test size: \" + str(x_test.shape))\n",
    "print(\"Y_Train size: \" + str(y_train.shape))\n",
    "print(\"Y_Test size: \" + str(y_test.shape))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Making the Model(Random Forest Classifier)</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.94967405170698"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creation of the Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "RF = RandomForestClassifier(n_estimators = 100)\n",
    "RF.fit(x_train,y_train)\n",
    "prediction_RF = RF.predict(x_test)\n",
    "RF_score = RF.score(x_test,y_test)*100\n",
    "RF_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is:  0.9994967405170698\n",
      "The precision is:  0.896551724137931\n",
      "The recall is:  0.7703703703703704\n",
      "The F1 score is:  0.8286852589641435\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy is: \", accuracy_score(y_test, prediction_RF)) \n",
    "print(\"The precision is: \", precision_score(y_test, prediction_RF))\n",
    "print(\"The recall is: \", recall_score(y_test, prediction_RF))\n",
    "print(\"The F1 score is: \", f1_score(y_test, prediction_RF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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