credit card fraud.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c2e98d6-85f9-4c80-b007-a0334e85ea6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee26c17b-d647-4b96-a6bb-6e738b8c62af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16ed67a4-b7b4-4e93-9f25-b05c24c3abb6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30737db1-5c74-41cf-99ab-9917eacf3122",
   "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": 5,
   "id": "439ab163-8800-4e9f-814d-1cb9cbf52b97",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <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>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-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  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-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   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-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   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-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   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-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": "4e6fcd71-9f6f-4f6d-8d4a-1f9b03319645",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017304750013189597\n",
      "Fraud Cases: 492\n",
      "Valid Transactions: 284315\n"
     ]
    }
   ],
   "source": [
    "# Determine number of fraud cases in dataset \n",
    "fraud = df[df['Class'] == 1] \n",
    "valid = df[df['Class'] == 0] \n",
    "outlierFraction = len(fraud)/float(len(valid)) \n",
    "print(outlierFraction) \n",
    "print('Fraud Cases: {}'.format(len(df[df['Class'] == 1]))) \n",
    "print('Valid Transactions: {}'.format(len(df[df['Class'] == 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86a10c5d-293e-4230-ba40-913d254f625d",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(“Amount details of the fraudulent transaction”) \n",
    "fraud.Amount.describe() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8beece94-cec2-4b66-8977-6c622cd686aa",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(“details of valid transaction”) \n",
    "valid.Amount.describe() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f8ef6151-fcbc-43ce-90af-9288e3957e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix \n",
    "corrmat = df.corr() \n",
    "fig = plt.figure(figsize = (12, 9)) \n",
    "sns.heatmap(corrmat, vmax = .8, square = True) \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed0bb748-a489-44e4-9219-6f21f1ee688e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n",
      "(284807,)\n"
     ]
    }
   ],
   "source": [
    "# dividing the X and the Y from the dataset \n",
    "X = df.drop(['Class'], axis = 1) \n",
    "Y = df[\"Class\"] \n",
    "print(X.shape) \n",
    "print(Y.shape) \n",
    "# getting just the values for the sake of processing \n",
    "# (its a numpy array with no columns) \n",
    "xData = X.values \n",
    "yData = Y.values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46393957-3e3a-44b3-8d46-3b363c4608f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Scikit-learn to split data into training and testing sets \n",
    "from sklearn.model_selection import train_test_split \n",
    "# Split the data into training and testing sets \n",
    "xTrain, xTest, yTrain, yTest = train_test_split(xData, yData, test_size = 0.2, random_state = 42) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4f70e8c-e900-4ed5-8e11-9ed05e2ef3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the Random Forest Classifier (RANDOM FOREST) \n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "# random forest model creation \n",
    "rfc = RandomForestClassifier() \n",
    "rfc.fit(xTrain, yTrain) \n",
    "# predictions \n",
    "yPred = rfc.predict(xTest) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c96f18f-e634-4032-aa7e-6af5469eb70f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model used is Random Forest classifier\n",
      "The accuracy is 0.9995435553526912\n",
      "The precision is 0.9615384615384616\n",
      "The recall is 0.7653061224489796\n",
      "The F1-Score is 0.8522727272727273\n",
      "The Matthews correlation coefficient is0.8576194535617819\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the classifier \n",
    "# printing every score of the classifier \n",
    "# scoring in anything \n",
    "from sklearn.metrics import classification_report, accuracy_score \n",
    "from sklearn.metrics import precision_score, recall_score \n",
    "from sklearn.metrics import f1_score, matthews_corrcoef \n",
    "from sklearn.metrics import confusion_matrix \n",
    "\n",
    "n_outliers = len(fraud) \n",
    "n_errors = (yPred != yTest).sum() \n",
    "print(\"The model used is Random Forest classifier\") \n",
    "\n",
    "acc = accuracy_score(yTest, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "\n",
    "prec = precision_score(yTest, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "\n",
    "rec = recall_score(yTest, yPred) \n",
    "print(\"The recall is {}\".format(rec)) \n",
    "\n",
    "f1 = f1_score(yTest, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "\n",
    "MCC = matthews_corrcoef(yTest, yPred) \n",
    "print(\"The Matthews correlation coefficient is{}\".format(MCC)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80e89c92-2251-484f-a748-e0422cf18ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# printing the confusion matrix \n",
    "LABELS = ['Normal', 'Fraud'] \n",
    "conf_matrix = confusion_matrix(yTest, yPred) \n",
    "plt.figure(figsize =(12, 12)) \n",
    "sns.heatmap(conf_matrix, xticklabels = LABELS, \n",
    "\t\t\tyticklabels = LABELS, annot = True, fmt =\"d\"); \n",
    "plt.title(\"Confusion matrix\") \n",
    "plt.ylabel('True class') \n",
    "plt.xlabel('Predicted class') \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17a252fd-ae8a-4421-b7c4-5b5482a163fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}