creditcard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns \n",
    "from sklearn.metrics import classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </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",
       "  </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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time     V1     V2     V3     V4     V5     V6     V7     V8     V9  \\\n",
       "0       False  False  False  False  False  False  False  False  False  False   \n",
       "1       False  False  False  False  False  False  False  False  False  False   \n",
       "2       False  False  False  False  False  False  False  False  False  False   \n",
       "3       False  False  False  False  False  False  False  False  False  False   \n",
       "4       False  False  False  False  False  False  False  False  False  False   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "284802  False  False  False  False  False  False  False  False  False  False   \n",
       "284803  False  False  False  False  False  False  False  False  False  False   \n",
       "284804  False  False  False  False  False  False  False  False  False  False   \n",
       "284805  False  False  False  False  False  False  False  False  False  False   \n",
       "284806  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "        ...    V21    V22    V23    V24    V25    V26    V27    V28  Amount  \\\n",
       "0       ...  False  False  False  False  False  False  False  False   False   \n",
       "1       ...  False  False  False  False  False  False  False  False   False   \n",
       "2       ...  False  False  False  False  False  False  False  False   False   \n",
       "3       ...  False  False  False  False  False  False  False  False   False   \n",
       "4       ...  False  False  False  False  False  False  False  False   False   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...     ...   \n",
       "284802  ...  False  False  False  False  False  False  False  False   False   \n",
       "284803  ...  False  False  False  False  False  False  False  False   False   \n",
       "284804  ...  False  False  False  False  False  False  False  False   False   \n",
       "284805  ...  False  False  False  False  False  False  False  False   False   \n",
       "284806  ...  False  False  False  False  False  False  False  False   False   \n",
       "\n",
       "        Class  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  \n",
       "...       ...  \n",
       "284802  False  \n",
       "284803  False  \n",
       "284804  False  \n",
       "284805  False  \n",
       "284806  False  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
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       "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dev\\AppData\\Local\\Temp\\ipykernel_14664\\3562822510.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  count_classes = pd.value_counts(df['Class'],sort=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(df['Class'],sort=True)\n",
    "count_classes.plot(kind= 'bar' , rot=0)\n",
    "plt.title('Transaction Class Distriibution')\n",
    "plt.xticks(range(2), ['Normal' , 'Fraud'])\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fraud = df[df['Class']==1] \n",
    "Normal = df[df['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(492, 31) (284315, 31)\n"
     ]
    }
   ],
   "source": [
    "print(fraud.shape , Normal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normal.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1 , ax2 )= plt.subplots(2,1,sharex=True)\n",
    "f.suptitle('Time of Transaction VS Amonut by Class')\n",
    "ax1.scatter(fraud.Time , fraud.Amount)\n",
    "ax1.set_title('FRAUD')\n",
    "ax2.scatter(Normal.Time , Normal.Amount)\n",
    "ax2.set_title('NORMAL')\n",
    "plt.xlabel('Time(in seconds)')\n",
    "plt.ylabel('Amount')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicate rows if any\n",
    "df.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features (X) and target (y)\n",
    "X = df.drop(['Class'], axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the data (optional, but can be beneficial for some models)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle class imbalance using SMOTE (Synthetic Minority Over-sampling Technique)\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train Logistic Regression model\n",
    "log_reg = LogisticRegression(max_iter=1000)\n",
    "log_reg.fit(X_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "y_pred = log_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model:\n",
      "Accuracy Score: 0.9750643217143059\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99     56656\n",
      "           1       0.05      0.89      0.10        90\n",
      "\n",
      "    accuracy                           0.98     56746\n",
      "   macro avg       0.53      0.93      0.54     56746\n",
      "weighted avg       1.00      0.98      0.99     56746\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "print(\"Logistic Regression Model:\")\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, min_samples_leaf=5, min_samples_split=10,\n",
       "                       n_estimators=50, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, min_samples_leaf=5, min_samples_split=10,\n",
       "                       n_estimators=50, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_leaf=5, min_samples_split=10,\n",
       "                       n_estimators=50, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train Random Forest Classifier with optimizations\n",
    "rf_clf = RandomForestClassifier(n_estimators=50, max_depth=10, min_samples_split=10, min_samples_leaf=5, random_state=42, n_jobs=-1)\n",
    "rf_clf.fit(X_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dev\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predictions\n",
    "y_pred_rf = rf_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Model:\n",
      "Accuracy Score: 0.9993479716632009\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56656\n",
      "           1       0.80      0.78      0.79        90\n",
      "\n",
      "    accuracy                           1.00     56746\n",
      "   macro avg       0.90      0.89      0.90     56746\n",
      "weighted avg       1.00      1.00      1.00     56746\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "print(\"Random Forest Classifier Model:\")\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred_rf))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display confusion matrix as a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['Predicted Normal', 'Predicted Fraud'],\n",
    "            yticklabels=['Actual Normal', 'Actual Fraud'])\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}