Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cf602d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b186d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\khursheed alam\\Downloads\\Data Science Intern\\creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92858761",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ddee561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13e52b8e",
   "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>0</th>\n",
       "      <td>0.0</td>\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>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <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>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <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>...</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>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c2d8d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>V9</th>\n",
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       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
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       "      <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",
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <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",
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       "    <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "102a8c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90705d50",
   "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": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3048f75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_distribution = df['Class'].value_counts()\n",
    "\n",
    "# Plot the distribution of classes using a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(class_distribution, labels=class_distribution.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2963b297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-Fraudulent Transactions: 284315 (0.9983%)\n",
      "Fraudulent Transactions: 492 (0.0017%)\n"
     ]
    }
   ],
   "source": [
    "# Calculate total number of transactions\n",
    "total_transactions = df.shape[0]\n",
    "\n",
    "# Extract counts for non-fraudulent and fraudulent transactions\n",
    "non_fraud = class_distribution[0]\n",
    "fraud = class_distribution[1]\n",
    "\n",
    "# Print the distribution of non-fraudulent and fraudulent transactions with percentages\n",
    "print(f'Non-Fraudulent Transactions: {non_fraud} ({(non_fraud / total_transactions):.4f}%)')\n",
    "print(f'Fraudulent Transactions: {fraud} ({(fraud / total_transactions):.4f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ee82cf",
   "metadata": {},
   "source": [
    "### Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc60ea1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Heatmap')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.corr()\n",
    "\n",
    "# Plot a heatmap of the correlation matrix\n",
    "sns.heatmap(correlation_matrix, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Correlation Heatmap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f49ca21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time vs. Amount')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of 'Time' vs. 'Amount', colored by 'Class'\n",
    "sns.scatterplot(x='Time', y='Amount', hue='Class', data=df)\n",
    "plt.title('Time vs. Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bf648e",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b540578",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Time' column from the DataFrame as it is not relevant for the prediction model\n",
    "data = df.drop(['Time'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16faca64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target variable\n",
    "x = data.drop('Class', axis=1)\n",
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecb0a4e",
   "metadata": {},
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686b2892",
   "metadata": {},
   "source": [
    "**SMOTE (Synthetic Minority Over-sampling Technique) is a popular technique used in machine learning to address the problem of imbalanced datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85bd0a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "654402af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the SMOTE algorithm with a fixed random seed for reproducibility\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Apply SMOTE to resample the data and balance the class distribution\n",
    "x_smote, y_smote = smote.fit_resample(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75866309",
   "metadata": {},
   "source": [
    "### StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d422c8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f63d583",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Standardize the features by scaling them to zero mean and unit variance\n",
    "x_standardized = scaler.fit_transform(x_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68750d29",
   "metadata": {},
   "source": [
    "**Splitting the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abf11caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea004b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the standardized data into training and testing sets with a 70:30 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_standardized, y_smote, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b32c867",
   "metadata": {},
   "source": [
    "## Predictive Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374bf296",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22d55526",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8f56dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=150)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Logistic Regression model with a maximum of 150 iterations for convergence\n",
    "logr = LogisticRegression(max_iter=150)\n",
    "\n",
    "# Fit the Logistic Regression model to the training data\n",
    "logr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3329424f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the class labels for the test data using the trained Logistic Regression model\n",
    "lr_pred = logr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "424460aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96     85149\n",
      "           1       0.98      0.94      0.96     85440\n",
      "\n",
      "    accuracy                           0.96    170589\n",
      "   macro avg       0.96      0.96      0.96    170589\n",
      "weighted avg       0.96      0.96      0.96    170589\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "     \n",
    "# Display the classification report to show precision, recall, f1-score, and support for each class\n",
    "print(classification_report(y_test, lr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8cf3076e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix\n",
    "cm = confusion_matrix(y_test, lr_pred)\n",
    "\n",
    "# Plot the confusion matrix as a heatmap\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Non-Fraud', 'Fraud'],\n",
    "            yticklabels=['Non-Fraud', 'Fraud'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1d40973c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9598\n",
      "Precision: 0.9810\n",
      "Recall: 0.9379\n",
      "F1 Score: 0.9590\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, lr_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "# Calculate precision\n",
    "precision = precision_score(y_test, lr_pred)\n",
    "print(f'Precision: {precision:.4f}')\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, lr_pred)\n",
    "print(f'Recall: {recall:.4f}')\n",
    "\n",
    "# Calculate F1 score\n",
    "f1 = f1_score(y_test, lr_pred)\n",
    "print(f'F1 Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c68dfe",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de55fdc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee1a5e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Decision Tree model\n",
    "dtree = DecisionTreeClassifier()\n",
    "\n",
    "# Fit the Decision Tree model to the training data\n",
    "dtree = dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3adecb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the class labels for the test data using the trained Decision Tree model\n",
    "dt_pred = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5deb03b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85149\n",
      "           1       1.00      1.00      1.00     85440\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display the classification report to show precision, recall, f1-score, and support for each class\n",
    "print(classification_report(y_test, dt_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "98abf005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix\n",
    "cm = confusion_matrix(y_test, dt_pred)\n",
    "\n",
    "# Plot the confusion matrix as a heatmap\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Non-Fraud', 'Fraud'],\n",
    "            yticklabels=['Non-Fraud', 'Fraud'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bdf16e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9980\n",
      "Precision: 0.9972\n",
      "Recall: 0.9989\n",
      "F1 Score: 0.9980\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, dt_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "# Calculate precision\n",
    "precision = precision_score(y_test, dt_pred)\n",
    "print(f'Precision: {precision:.4f}')\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, dt_pred)\n",
    "print(f'Recall: {recall:.4f}')\n",
    "\n",
    "# Calculate F1 score\n",
    "f1 = f1_score(y_test, dt_pred)\n",
    "print(f'F1 Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64929e3",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9bd9fdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "81a0c687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Random Forest model\n",
    "regressor = RandomForestClassifier()\n",
    "\n",
    "# Fit the Random Forest model to the training data\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d16f98f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the class labels for the test data using the trained Random Forest model\n",
    "rf_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d536a921",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85149\n",
      "           1       1.00      1.00      1.00     85440\n",
      "\n",
      "    accuracy                           1.00    170589\n",
      "   macro avg       1.00      1.00      1.00    170589\n",
      "weighted avg       1.00      1.00      1.00    170589\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display the classification report to show precision, recall, f1-score, and support for each class\n",
    "print(classification_report(y_test, rf_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "178ce2f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix\n",
    "cm = confusion_matrix(y_test, rf_pred)\n",
    "\n",
    "# Plot the confusion matrix as a heatmap\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Non-Fraud', 'Fraud'],\n",
    "            yticklabels=['Non-Fraud', 'Fraud'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cb55c3ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9999\n",
      "Precision: 0.9998\n",
      "Recall: 1.0000\n",
      "F1 Score: 0.9999\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, rf_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "# Calculate precision\n",
    "precision = precision_score(y_test, rf_pred)\n",
    "print(f'Precision: {precision:.4f}')\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, rf_pred)\n",
    "print(f'Recall: {recall:.4f}')\n",
    "\n",
    "# Calculate F1 score\n",
    "f1 = f1_score(y_test, rf_pred)\n",
    "print(f'F1 Score: {f1:.4f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}