credit-card-fraud-detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca0c0607",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "papermill": {
     "duration": 0.01106,
     "end_time": "2024-02-26T18:01:17.496922",
     "exception": false,
     "start_time": "2024-02-26T18:01:17.485862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Credit Card Fraud Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "79fb8020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:17.518911Z",
     "iopub.status.busy": "2024-02-26T18:01:17.518552Z",
     "iopub.status.idle": "2024-02-26T18:01:19.964439Z",
     "shell.execute_reply": "2024-02-26T18:01:19.963127Z"
    },
    "papermill": {
     "duration": 2.460679,
     "end_time": "2024-02-26T18:01:19.967888",
     "exception": false,
     "start_time": "2024-02-26T18:01:17.507209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7af04b0",
   "metadata": {
    "papermill": {
     "duration": 0.009406,
     "end_time": "2024-02-26T18:01:19.987171",
     "exception": false,
     "start_time": "2024-02-26T18:01:19.977765",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34b600ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:20.011750Z",
     "iopub.status.busy": "2024-02-26T18:01:20.010252Z",
     "iopub.status.idle": "2024-02-26T18:01:24.992429Z",
     "shell.execute_reply": "2024-02-26T18:01:24.991016Z"
    },
    "papermill": {
     "duration": 4.999377,
     "end_time": "2024-02-26T18:01:24.996210",
     "exception": false,
     "start_time": "2024-02-26T18:01:19.996833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"/kaggle/input/creditcardfraud/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb385c6",
   "metadata": {
    "papermill": {
     "duration": 0.009205,
     "end_time": "2024-02-26T18:01:25.015403",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.006198",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Exploring dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8b284a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:25.036210Z",
     "iopub.status.busy": "2024-02-26T18:01:25.035719Z",
     "iopub.status.idle": "2024-02-26T18:01:25.082703Z",
     "shell.execute_reply": "2024-02-26T18:01:25.081343Z"
    },
    "papermill": {
     "duration": 0.06106,
     "end_time": "2024-02-26T18:01:25.085799",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.024739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c28f1545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:25.109576Z",
     "iopub.status.busy": "2024-02-26T18:01:25.109008Z",
     "iopub.status.idle": "2024-02-26T18:01:25.160225Z",
     "shell.execute_reply": "2024-02-26T18:01:25.158813Z"
    },
    "papermill": {
     "duration": 0.066894,
     "end_time": "2024-02-26T18:01:25.163538",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.096644",
     "status": "completed"
    },
    "tags": []
   },
   "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": "c062fe3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:25.187814Z",
     "iopub.status.busy": "2024-02-26T18:01:25.186285Z",
     "iopub.status.idle": "2024-02-26T18:01:25.214103Z",
     "shell.execute_reply": "2024-02-26T18:01:25.212851Z"
    },
    "papermill": {
     "duration": 0.042812,
     "end_time": "2024-02-26T18:01:25.216637",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.173825",
     "status": "completed"
    },
    "tags": []
   },
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f47a475",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:25.239440Z",
     "iopub.status.busy": "2024-02-26T18:01:25.238936Z",
     "iopub.status.idle": "2024-02-26T18:01:25.753849Z",
     "shell.execute_reply": "2024-02-26T18:01:25.752675Z"
    },
    "papermill": {
     "duration": 0.529737,
     "end_time": "2024-02-26T18:01:25.756796",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.227059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>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>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>...</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "      <td>284807.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.860</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>88.350</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.146</td>\n",
       "      <td>1.959</td>\n",
       "      <td>1.651</td>\n",
       "      <td>1.516</td>\n",
       "      <td>1.416</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.332</td>\n",
       "      <td>1.237</td>\n",
       "      <td>1.194</td>\n",
       "      <td>1.099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.330</td>\n",
       "      <td>250.120</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-56.408</td>\n",
       "      <td>-72.716</td>\n",
       "      <td>-48.326</td>\n",
       "      <td>-5.683</td>\n",
       "      <td>-113.743</td>\n",
       "      <td>-26.161</td>\n",
       "      <td>-43.557</td>\n",
       "      <td>-73.217</td>\n",
       "      <td>-13.434</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.830</td>\n",
       "      <td>-10.933</td>\n",
       "      <td>-44.808</td>\n",
       "      <td>-2.837</td>\n",
       "      <td>-10.295</td>\n",
       "      <td>-2.605</td>\n",
       "      <td>-22.566</td>\n",
       "      <td>-15.430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500</td>\n",
       "      <td>-0.920</td>\n",
       "      <td>-0.599</td>\n",
       "      <td>-0.890</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>-0.692</td>\n",
       "      <td>-0.768</td>\n",
       "      <td>-0.554</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>-0.643</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>-0.542</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.317</td>\n",
       "      <td>-0.327</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>5.600</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.180</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>-0.274</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.011</td>\n",
       "      <td>22.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500</td>\n",
       "      <td>1.316</td>\n",
       "      <td>0.804</td>\n",
       "      <td>1.027</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.078</td>\n",
       "      <td>77.165</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000</td>\n",
       "      <td>2.455</td>\n",
       "      <td>22.058</td>\n",
       "      <td>9.383</td>\n",
       "      <td>16.875</td>\n",
       "      <td>34.802</td>\n",
       "      <td>73.302</td>\n",
       "      <td>120.589</td>\n",
       "      <td>20.007</td>\n",
       "      <td>15.595</td>\n",
       "      <td>...</td>\n",
       "      <td>27.203</td>\n",
       "      <td>10.503</td>\n",
       "      <td>22.528</td>\n",
       "      <td>4.585</td>\n",
       "      <td>7.520</td>\n",
       "      <td>3.517</td>\n",
       "      <td>31.612</td>\n",
       "      <td>33.848</td>\n",
       "      <td>25691.160</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Time          V1          V2          V3          V4          V5  \\\n",
       "count  284807.000  284807.000  284807.000  284807.000  284807.000  284807.000   \n",
       "mean    94813.860       0.000       0.000      -0.000       0.000       0.000   \n",
       "std     47488.146       1.959       1.651       1.516       1.416       1.380   \n",
       "min         0.000     -56.408     -72.716     -48.326      -5.683    -113.743   \n",
       "25%     54201.500      -0.920      -0.599      -0.890      -0.849      -0.692   \n",
       "50%     84692.000       0.018       0.065       0.180      -0.020      -0.054   \n",
       "75%    139320.500       1.316       0.804       1.027       0.743       0.612   \n",
       "max    172792.000       2.455      22.058       9.383      16.875      34.802   \n",
       "\n",
       "               V6          V7          V8          V9  ...         V21  \\\n",
       "count  284807.000  284807.000  284807.000  284807.000  ...  284807.000   \n",
       "mean        0.000      -0.000       0.000      -0.000  ...       0.000   \n",
       "std         1.332       1.237       1.194       1.099  ...       0.735   \n",
       "min       -26.161     -43.557     -73.217     -13.434  ...     -34.830   \n",
       "25%        -0.768      -0.554      -0.209      -0.643  ...      -0.228   \n",
       "50%        -0.274       0.040       0.022      -0.051  ...      -0.029   \n",
       "75%         0.399       0.570       0.327       0.597  ...       0.186   \n",
       "max        73.302     120.589      20.007      15.595  ...      27.203   \n",
       "\n",
       "              V22         V23         V24         V25         V26         V27  \\\n",
       "count  284807.000  284807.000  284807.000  284807.000  284807.000  284807.000   \n",
       "mean       -0.000       0.000       0.000       0.000       0.000      -0.000   \n",
       "std         0.726       0.624       0.606       0.521       0.482       0.404   \n",
       "min       -10.933     -44.808      -2.837     -10.295      -2.605     -22.566   \n",
       "25%        -0.542      -0.162      -0.355      -0.317      -0.327      -0.071   \n",
       "50%         0.007      -0.011       0.041       0.017      -0.052       0.001   \n",
       "75%         0.529       0.148       0.440       0.351       0.241       0.091   \n",
       "max        10.503      22.528       4.585       7.520       3.517      31.612   \n",
       "\n",
       "              V28      Amount       Class  \n",
       "count  284807.000  284807.000  284807.000  \n",
       "mean       -0.000      88.350       0.002  \n",
       "std         0.330     250.120       0.042  \n",
       "min       -15.430       0.000       0.000  \n",
       "25%        -0.053       5.600       0.000  \n",
       "50%         0.011      22.000       0.000  \n",
       "75%         0.078      77.165       0.000  \n",
       "max        33.848   25691.160       1.000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d793b78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:25.781263Z",
     "iopub.status.busy": "2024-02-26T18:01:25.780864Z",
     "iopub.status.idle": "2024-02-26T18:01:26.620721Z",
     "shell.execute_reply": "2024-02-26T18:01:26.619628Z"
    },
    "papermill": {
     "duration": 0.85585,
     "end_time": "2024-02-26T18:01:26.623913",
     "exception": false,
     "start_time": "2024-02-26T18:01:25.768063",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1081"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805595b9",
   "metadata": {
    "papermill": {
     "duration": 0.010824,
     "end_time": "2024-02-26T18:01:26.645838",
     "exception": false,
     "start_time": "2024-02-26T18:01:26.635014",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "since there are duplicated values, so dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6538c974",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:26.669547Z",
     "iopub.status.busy": "2024-02-26T18:01:26.669111Z",
     "iopub.status.idle": "2024-02-26T18:01:27.764059Z",
     "shell.execute_reply": "2024-02-26T18:01:27.763169Z"
    },
    "papermill": {
     "duration": 1.110132,
     "end_time": "2024-02-26T18:01:27.766922",
     "exception": false,
     "start_time": "2024-02-26T18:01:26.656790",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates(df,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c82bb6e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:27.792371Z",
     "iopub.status.busy": "2024-02-26T18:01:27.791744Z",
     "iopub.status.idle": "2024-02-26T18:01:27.798315Z",
     "shell.execute_reply": "2024-02-26T18:01:27.797190Z"
    },
    "papermill": {
     "duration": 0.022951,
     "end_time": "2024-02-26T18:01:27.801463",
     "exception": false,
     "start_time": "2024-02-26T18:01:27.778512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283726, 31)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2e51af",
   "metadata": {
    "papermill": {
     "duration": 0.012291,
     "end_time": "2024-02-26T18:01:27.826152",
     "exception": false,
     "start_time": "2024-02-26T18:01:27.813861",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27764499",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:27.854308Z",
     "iopub.status.busy": "2024-02-26T18:01:27.853325Z",
     "iopub.status.idle": "2024-02-26T18:01:28.201403Z",
     "shell.execute_reply": "2024-02-26T18:01:28.199886Z"
    },
    "papermill": {
     "duration": 0.364088,
     "end_time": "2024-02-26T18:01:28.204547",
     "exception": false,
     "start_time": "2024-02-26T18:01:27.840459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Class\"].value_counts().plot(kind= \"pie\",autopct='%1.3f%%', shadow = True)\n",
    "plt.show()\n",
    "# 0 means no fraud and 1 is fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d2c330",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:28.230673Z",
     "iopub.status.busy": "2024-02-26T18:01:28.230170Z",
     "iopub.status.idle": "2024-02-26T18:01:28.476538Z",
     "shell.execute_reply": "2024-02-26T18:01:28.475678Z"
    },
    "papermill": {
     "duration": 0.262425,
     "end_time": "2024-02-26T18:01:28.479130",
     "exception": false,
     "start_time": "2024-02-26T18:01:28.216705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist (data= df, x=\"Amount\", color = 'blue')\n",
    "plt.title(\"where is the distributing in Amount column\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "541d01f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:28.506693Z",
     "iopub.status.busy": "2024-02-26T18:01:28.505750Z",
     "iopub.status.idle": "2024-02-26T18:01:28.556661Z",
     "shell.execute_reply": "2024-02-26T18:01:28.555452Z"
    },
    "papermill": {
     "duration": 0.067867,
     "end_time": "2024-02-26T18:01:28.559389",
     "exception": false,
     "start_time": "2024-02-26T18:01:28.491522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274771    25691.16\n",
       "58465     19656.53\n",
       "151296    18910.00\n",
       "46841     12910.93\n",
       "54018     11898.09\n",
       "169457    11789.84\n",
       "284249    10199.44\n",
       "227921    10000.00\n",
       "74699      8790.26\n",
       "245474     8787.00\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Amount\"].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13d27e0",
   "metadata": {
    "papermill": {
     "duration": 0.01183,
     "end_time": "2024-02-26T18:01:28.584107",
     "exception": false,
     "start_time": "2024-02-26T18:01:28.572277",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Building ML Model -> Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f239be8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:28.611941Z",
     "iopub.status.busy": "2024-02-26T18:01:28.610489Z",
     "iopub.status.idle": "2024-02-26T18:01:29.139266Z",
     "shell.execute_reply": "2024-02-26T18:01:29.137914Z"
    },
    "papermill": {
     "duration": 0.545981,
     "end_time": "2024-02-26T18:01:29.142368",
     "exception": false,
     "start_time": "2024-02-26T18:01:28.596387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import  classification_report\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import  KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import  MLPClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9e02144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:29.169284Z",
     "iopub.status.busy": "2024-02-26T18:01:29.168867Z",
     "iopub.status.idle": "2024-02-26T18:01:29.302699Z",
     "shell.execute_reply": "2024-02-26T18:01:29.301136Z"
    },
    "papermill": {
     "duration": 0.151056,
     "end_time": "2024-02-26T18:01:29.305676",
     "exception": false,
     "start_time": "2024-02-26T18:01:29.154620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train :  (212794, 30)\n",
      "X Test  :  (70932, 30)\n",
      "Y Train :  (212794,)\n",
      "Y Test  :  (70932,)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=\"Class\")           \n",
    "y = df[\"Class\"]    # y = class\n",
    "\n",
    "# split the data train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"X Train : \", X_train.shape)\n",
    "print(\"X Test  : \", X_test.shape)\n",
    "print(\"Y Train : \", y_train.shape)\n",
    "print(\"Y Test  : \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d3bdce7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:29.334125Z",
     "iopub.status.busy": "2024-02-26T18:01:29.333724Z",
     "iopub.status.idle": "2024-02-26T18:01:29.340423Z",
     "shell.execute_reply": "2024-02-26T18:01:29.339045Z"
    },
    "papermill": {
     "duration": 0.024272,
     "end_time": "2024-02-26T18:01:29.343259",
     "exception": false,
     "start_time": "2024-02-26T18:01:29.318987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "LR = LogisticRegression()\n",
    "DTR = DecisionTreeClassifier()\n",
    "RFR = RandomForestClassifier()\n",
    "KNR = KNeighborsClassifier()\n",
    "MLP = MLPClassifier()\n",
    "SVC=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c6c292aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:01:29.371607Z",
     "iopub.status.busy": "2024-02-26T18:01:29.371207Z",
     "iopub.status.idle": "2024-02-26T18:09:43.301611Z",
     "shell.execute_reply": "2024-02-26T18:09:43.299814Z"
    },
    "papermill": {
     "duration": 493.959656,
     "end_time": "2024-02-26T18:09:43.315894",
     "exception": false,
     "start_time": "2024-02-26T18:01:29.356238",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.77      0.64      0.70       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.89      0.82      0.85     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "DecisionTreeClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.72      0.74      0.73       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.86      0.87      0.87     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "RandomForestClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.97      0.74      0.84       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.98      0.87      0.92     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "KNeighborsClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.80      0.04      0.07       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.90      0.52      0.53     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.00      0.00      0.00       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.50      0.50      0.50     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "MLPClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.52      0.76      0.62       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.76      0.88      0.81     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n"
     ]
    }
   ],
   "source": [
    "li = [LR,DTR,RFR,KNR,SVC,MLP]\n",
    "\n",
    "for i in li:\n",
    "    i.fit(X_train,y_train)\n",
    "    ypred = i.predict(X_test)\n",
    "    print(i,\":\",classification_report(y_test,ypred))\n",
    "    print(\"********************************************\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "211fb4c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.347860Z",
     "iopub.status.busy": "2024-02-26T18:09:43.347337Z",
     "iopub.status.idle": "2024-02-26T18:09:43.370162Z",
     "shell.execute_reply": "2024-02-26T18:09:43.368933Z"
    },
    "papermill": {
     "duration": 0.042959,
     "end_time": "2024-02-26T18:09:43.373073",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.330114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectPercentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1963eb05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.401756Z",
     "iopub.status.busy": "2024-02-26T18:09:43.401331Z",
     "iopub.status.idle": "2024-02-26T18:09:43.406501Z",
     "shell.execute_reply": "2024-02-26T18:09:43.405329Z"
    },
    "papermill": {
     "duration": 0.022588,
     "end_time": "2024-02-26T18:09:43.409045",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.386457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "F_50 = SelectPercentile( percentile= 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3e68941",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.438994Z",
     "iopub.status.busy": "2024-02-26T18:09:43.438584Z",
     "iopub.status.idle": "2024-02-26T18:09:43.599223Z",
     "shell.execute_reply": "2024-02-26T18:09:43.598088Z"
    },
    "papermill": {
     "duration": 0.17894,
     "end_time": "2024-02-26T18:09:43.602383",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.423443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_X = F_50.fit_transform(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8257ffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.631735Z",
     "iopub.status.busy": "2024-02-26T18:09:43.631273Z",
     "iopub.status.idle": "2024-02-26T18:09:43.660727Z",
     "shell.execute_reply": "2024-02-26T18:09:43.659559Z"
    },
    "papermill": {
     "duration": 0.047653,
     "end_time": "2024-02-26T18:09:43.663774",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.616121",
     "status": "completed"
    },
    "tags": []
   },
   "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>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V14</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</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>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</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>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</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>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</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>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</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>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283721</th>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>-1.593105</td>\n",
       "      <td>2.711941</td>\n",
       "      <td>4.626942</td>\n",
       "      <td>1.107641</td>\n",
       "      <td>1.991691</td>\n",
       "      <td>0.510632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283722</th>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.150189</td>\n",
       "      <td>0.915802</td>\n",
       "      <td>-0.675143</td>\n",
       "      <td>-0.711757</td>\n",
       "      <td>-0.025693</td>\n",
       "      <td>-1.221179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283723</th>\n",
       "      <td>1.919565</td>\n",
       "      <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>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>0.411614</td>\n",
       "      <td>0.063119</td>\n",
       "      <td>-0.510602</td>\n",
       "      <td>0.140716</td>\n",
       "      <td>0.313502</td>\n",
       "      <td>0.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283724</th>\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>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>-1.933849</td>\n",
       "      <td>-0.962886</td>\n",
       "      <td>0.449624</td>\n",
       "      <td>-0.608577</td>\n",
       "      <td>0.509928</td>\n",
       "      <td>1.113981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283725</th>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-1.040458</td>\n",
       "      <td>-0.031513</td>\n",
       "      <td>-0.084316</td>\n",
       "      <td>-0.302620</td>\n",
       "      <td>-0.660377</td>\n",
       "      <td>0.167430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>283726 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               V1         V2        V3        V4        V5        V6  \\\n",
       "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "...           ...        ...       ...       ...       ...       ...   \n",
       "283721 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
       "283722  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
       "283723   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
       "283724  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
       "283725  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
       "\n",
       "              V7        V9       V10       V11       V12       V14       V16  \\\n",
       "0       0.239599  0.363787  0.090794 -0.551600 -0.617801 -0.311169 -0.470401   \n",
       "1      -0.078803 -0.255425 -0.166974  1.612727  1.065235 -0.143772  0.463917   \n",
       "2       0.791461 -1.514654  0.207643  0.624501  0.066084 -0.165946 -2.890083   \n",
       "3       0.237609 -1.387024 -0.054952 -0.226487  0.178228 -0.287924 -1.059647   \n",
       "4       0.592941  0.817739  0.753074 -0.822843  0.538196 -1.119670 -0.451449   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "283721 -4.918215  1.914428  4.356170 -1.593105  2.711941  4.626942  1.107641   \n",
       "283722  0.024330  0.584800 -0.975926 -0.150189  0.915802 -0.675143 -0.711757   \n",
       "283723 -0.296827  0.432454 -0.484782  0.411614  0.063119 -0.510602  0.140716   \n",
       "283724 -0.686180  0.392087 -0.399126 -1.933849 -0.962886  0.449624 -0.608577   \n",
       "283725  1.577006  0.486180 -0.915427 -1.040458 -0.031513 -0.084316 -0.302620   \n",
       "\n",
       "             V17       V18  \n",
       "0       0.207971  0.025791  \n",
       "1      -0.114805 -0.183361  \n",
       "2       1.109969 -0.121359  \n",
       "3      -0.684093  1.965775  \n",
       "4      -0.237033 -0.038195  \n",
       "...          ...       ...  \n",
       "283721  1.991691  0.510632  \n",
       "283722 -0.025693 -1.221179  \n",
       "283723  0.313502  0.395652  \n",
       "283724  0.509928  1.113981  \n",
       "283725 -0.660377  0.167430  \n",
       "\n",
       "[283726 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_x =pd.DataFrame(set_X,columns= [ i for i,j in zip (X.columns, F_50.get_support()) if j == True ])\n",
    "new_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6465c1e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.696193Z",
     "iopub.status.busy": "2024-02-26T18:09:43.695470Z",
     "iopub.status.idle": "2024-02-26T18:09:43.703580Z",
     "shell.execute_reply": "2024-02-26T18:09:43.702370Z"
    },
    "papermill": {
     "duration": 0.027623,
     "end_time": "2024-02-26T18:09:43.706280",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.678657",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "284802    0\n",
       "284803    0\n",
       "284804    0\n",
       "284805    0\n",
       "284806    0\n",
       "Name: Class, Length: 283726, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56a68d8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.736975Z",
     "iopub.status.busy": "2024-02-26T18:09:43.736540Z",
     "iopub.status.idle": "2024-02-26T18:09:43.794821Z",
     "shell.execute_reply": "2024-02-26T18:09:43.793146Z"
    },
    "papermill": {
     "duration": 0.076887,
     "end_time": "2024-02-26T18:09:43.797552",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.720665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Train :  (212794, 15)\n",
      "X Test  :  (70932, 15)\n",
      "Y Train :  (212794,)\n",
      "Y Test  :  (70932,)\n"
     ]
    }
   ],
   "source": [
    "X = new_x      \n",
    "y = df[\"Class\"]    # y = class\n",
    "\n",
    "# split the data train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"X Train : \", X_train.shape)\n",
    "print(\"X Test  : \", X_test.shape)\n",
    "print(\"Y Train : \", y_train.shape)\n",
    "print(\"Y Test  : \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca56b029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-26T18:09:43.828985Z",
     "iopub.status.busy": "2024-02-26T18:09:43.828186Z",
     "iopub.status.idle": "2024-02-26T18:19:55.358983Z",
     "shell.execute_reply": "2024-02-26T18:19:55.357638Z"
    },
    "papermill": {
     "duration": 611.562375,
     "end_time": "2024-02-26T18:19:55.374537",
     "exception": false,
     "start_time": "2024-02-26T18:09:43.812162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.90      0.54      0.67       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.95      0.77      0.83     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "DecisionTreeClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.72      0.67      0.69       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.86      0.83      0.85     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "RandomForestClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.94      0.73      0.82       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.97      0.87      0.91     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "KNeighborsClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.92      0.76      0.83       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.96      0.88      0.92     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "SVC() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.97      0.67      0.79       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.99      0.83      0.90     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n",
      "MLPClassifier() :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     70820\n",
      "           1       0.90      0.76      0.83       112\n",
      "\n",
      "    accuracy                           1.00     70932\n",
      "   macro avg       0.95      0.88      0.91     70932\n",
      "weighted avg       1.00      1.00      1.00     70932\n",
      "\n",
      "********************************************\n"
     ]
    }
   ],
   "source": [
    "li = [LR,DTR,RFR,KNR,SVC,MLP]\n",
    "\n",
    "for i in li:\n",
    "    i.fit(X_train,y_train)\n",
    "    ypred = i.predict(X_test)\n",
    "    print(i,\":\",classification_report(y_test,ypred))\n",
    "    print(\"********************************************\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d99f402",
   "metadata": {
    "papermill": {
     "duration": 0.015286,
     "end_time": "2024-02-26T18:19:55.405361",
     "exception": false,
     "start_time": "2024-02-26T18:19:55.390075",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 310,
     "sourceId": 23498,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30646,
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 1122.000012,
   "end_time": "2024-02-26T18:19:56.447631",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-02-26T18:01:14.447619",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}