credit card fraud.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee4af9f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dea9868f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:\\DATASETS\\creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc379133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7321259",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28e0a47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77508b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Time'}>, <Axes: title={'center': 'V1'}>,\n",
       "        <Axes: title={'center': 'V2'}>, <Axes: title={'center': 'V3'}>,\n",
       "        <Axes: title={'center': 'V4'}>, <Axes: title={'center': 'V5'}>],\n",
       "       [<Axes: title={'center': 'V6'}>, <Axes: title={'center': 'V7'}>,\n",
       "        <Axes: title={'center': 'V8'}>, <Axes: title={'center': 'V9'}>,\n",
       "        <Axes: title={'center': 'V10'}>, <Axes: title={'center': 'V11'}>],\n",
       "       [<Axes: title={'center': 'V12'}>, <Axes: title={'center': 'V13'}>,\n",
       "        <Axes: title={'center': 'V14'}>, <Axes: title={'center': 'V15'}>,\n",
       "        <Axes: title={'center': 'V16'}>, <Axes: title={'center': 'V17'}>],\n",
       "       [<Axes: title={'center': 'V18'}>, <Axes: title={'center': 'V19'}>,\n",
       "        <Axes: title={'center': 'V20'}>, <Axes: title={'center': 'V21'}>,\n",
       "        <Axes: title={'center': 'V22'}>, <Axes: title={'center': 'V23'}>],\n",
       "       [<Axes: title={'center': 'V24'}>, <Axes: title={'center': 'V25'}>,\n",
       "        <Axes: title={'center': 'V26'}>, <Axes: title={'center': 'V27'}>,\n",
       "        <Axes: title={'center': 'V28'}>,\n",
       "        <Axes: title={'center': 'Amount'}>],\n",
       "       [<Axes: title={'center': 'Class'}>, <Axes: >, <Axes: >, <Axes: >,\n",
       "        <Axes: >, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=30, figsize=(30, 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0edb1711",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing the data\n",
    "\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "087ec3cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.000000</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>1.783274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</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>-0.269825</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000006</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>4.983721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000006</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>1.418291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000012</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>0.670579</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>0.999965</td>\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>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>-0.296653</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>0.999971</td>\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>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>0.038986</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>0.999977</td>\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>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>0.641096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>0.999977</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>-0.167680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>1.000000</td>\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>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>2.724796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0       0.000000  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1       0.000000   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2       0.000006  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3       0.000006  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4       0.000012  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  0.999965 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  0.999971  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  0.999977   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  0.999977  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  1.000000  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28    Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  1.783274   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724 -0.269825   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  4.983721   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  1.418291   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153  0.670579   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731 -0.296653   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527  0.038986   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561  0.641096   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533 -0.167680   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  2.724796   \n",
       "\n",
       "        Class  \n",
       "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",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.copy()\n",
    "new_df['Amount'] = RobustScaler().fit_transform(new_df['Amount'].to_numpy().reshape(-1,1))\n",
    "time = new_df['Time']\n",
    "new_df['Time'] = (time - time.min()) / (time.max() - time.min())\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0185006f",
   "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",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>169876</th>\n",
       "      <td>0.693938</td>\n",
       "      <td>-0.611712</td>\n",
       "      <td>-0.769705</td>\n",
       "      <td>-0.149759</td>\n",
       "      <td>-0.224877</td>\n",
       "      <td>2.028577</td>\n",
       "      <td>-2.019887</td>\n",
       "      <td>0.292491</td>\n",
       "      <td>-0.523020</td>\n",
       "      <td>0.358468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075208</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.380739</td>\n",
       "      <td>0.023440</td>\n",
       "      <td>-2.220686</td>\n",
       "      <td>-0.201146</td>\n",
       "      <td>0.066501</td>\n",
       "      <td>0.221180</td>\n",
       "      <td>-0.282401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127467</th>\n",
       "      <td>0.453377</td>\n",
       "      <td>-0.814682</td>\n",
       "      <td>1.319219</td>\n",
       "      <td>1.329415</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>-0.284871</td>\n",
       "      <td>-0.653985</td>\n",
       "      <td>0.321552</td>\n",
       "      <td>0.435975</td>\n",
       "      <td>-0.704298</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128619</td>\n",
       "      <td>-0.368565</td>\n",
       "      <td>0.090660</td>\n",
       "      <td>0.401147</td>\n",
       "      <td>-0.261034</td>\n",
       "      <td>0.080621</td>\n",
       "      <td>0.162427</td>\n",
       "      <td>0.059456</td>\n",
       "      <td>-0.279746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137900</th>\n",
       "      <td>0.476770</td>\n",
       "      <td>-0.318193</td>\n",
       "      <td>1.118618</td>\n",
       "      <td>0.969864</td>\n",
       "      <td>-0.127052</td>\n",
       "      <td>0.569563</td>\n",
       "      <td>-0.532484</td>\n",
       "      <td>0.706252</td>\n",
       "      <td>-0.064966</td>\n",
       "      <td>-0.463271</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305402</td>\n",
       "      <td>-0.774704</td>\n",
       "      <td>-0.123884</td>\n",
       "      <td>-0.495687</td>\n",
       "      <td>-0.018148</td>\n",
       "      <td>0.121679</td>\n",
       "      <td>0.249050</td>\n",
       "      <td>0.092516</td>\n",
       "      <td>-0.294977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21513</th>\n",
       "      <td>0.183556</td>\n",
       "      <td>-1.328271</td>\n",
       "      <td>1.018378</td>\n",
       "      <td>1.775426</td>\n",
       "      <td>-1.574193</td>\n",
       "      <td>-0.117696</td>\n",
       "      <td>-0.457733</td>\n",
       "      <td>0.681867</td>\n",
       "      <td>-0.031641</td>\n",
       "      <td>0.383872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.220815</td>\n",
       "      <td>-0.419013</td>\n",
       "      <td>-0.239197</td>\n",
       "      <td>0.009967</td>\n",
       "      <td>0.232829</td>\n",
       "      <td>0.814177</td>\n",
       "      <td>0.098797</td>\n",
       "      <td>-0.004273</td>\n",
       "      <td>-0.084119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134700</th>\n",
       "      <td>0.468326</td>\n",
       "      <td>1.276712</td>\n",
       "      <td>0.617120</td>\n",
       "      <td>-0.578014</td>\n",
       "      <td>0.879173</td>\n",
       "      <td>0.061706</td>\n",
       "      <td>-1.472002</td>\n",
       "      <td>0.373692</td>\n",
       "      <td>-0.287204</td>\n",
       "      <td>-0.084482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160161</td>\n",
       "      <td>-0.430404</td>\n",
       "      <td>-0.076738</td>\n",
       "      <td>0.258708</td>\n",
       "      <td>0.552170</td>\n",
       "      <td>0.370701</td>\n",
       "      <td>-0.034255</td>\n",
       "      <td>0.041709</td>\n",
       "      <td>-0.296793</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21440</th>\n",
       "      <td>0.183261</td>\n",
       "      <td>-2.986845</td>\n",
       "      <td>-8.663978</td>\n",
       "      <td>-1.910863</td>\n",
       "      <td>0.664058</td>\n",
       "      <td>-3.934875</td>\n",
       "      <td>0.861269</td>\n",
       "      <td>1.647511</td>\n",
       "      <td>-0.480963</td>\n",
       "      <td>-1.546866</td>\n",
       "      <td>...</td>\n",
       "      <td>1.252092</td>\n",
       "      <td>-0.993085</td>\n",
       "      <td>-2.173147</td>\n",
       "      <td>0.145570</td>\n",
       "      <td>-0.235062</td>\n",
       "      <td>-0.227411</td>\n",
       "      <td>-0.382702</td>\n",
       "      <td>0.404045</td>\n",
       "      <td>32.002515</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117583</th>\n",
       "      <td>0.432480</td>\n",
       "      <td>0.937083</td>\n",
       "      <td>-0.849673</td>\n",
       "      <td>0.524186</td>\n",
       "      <td>-0.020031</td>\n",
       "      <td>-0.606327</td>\n",
       "      <td>0.692302</td>\n",
       "      <td>-0.463724</td>\n",
       "      <td>0.148857</td>\n",
       "      <td>0.785062</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.143322</td>\n",
       "      <td>-0.479981</td>\n",
       "      <td>-0.237902</td>\n",
       "      <td>-0.715247</td>\n",
       "      <td>0.251418</td>\n",
       "      <td>0.975406</td>\n",
       "      <td>-0.060168</td>\n",
       "      <td>0.023771</td>\n",
       "      <td>2.086495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73349</th>\n",
       "      <td>0.318852</td>\n",
       "      <td>-1.149963</td>\n",
       "      <td>1.696462</td>\n",
       "      <td>1.637114</td>\n",
       "      <td>2.658991</td>\n",
       "      <td>-0.021502</td>\n",
       "      <td>0.192287</td>\n",
       "      <td>0.205204</td>\n",
       "      <td>0.588754</td>\n",
       "      <td>-1.187820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025147</td>\n",
       "      <td>0.086506</td>\n",
       "      <td>-0.262748</td>\n",
       "      <td>0.321538</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.210343</td>\n",
       "      <td>-0.162047</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>-0.201495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267336</th>\n",
       "      <td>0.941757</td>\n",
       "      <td>1.754554</td>\n",
       "      <td>-0.699398</td>\n",
       "      <td>-0.076332</td>\n",
       "      <td>0.443915</td>\n",
       "      <td>-0.672082</td>\n",
       "      <td>0.389061</td>\n",
       "      <td>-0.807534</td>\n",
       "      <td>0.202915</td>\n",
       "      <td>0.858635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141950</td>\n",
       "      <td>0.358412</td>\n",
       "      <td>0.259748</td>\n",
       "      <td>0.746839</td>\n",
       "      <td>-0.560808</td>\n",
       "      <td>0.104636</td>\n",
       "      <td>-0.005853</td>\n",
       "      <td>-0.019622</td>\n",
       "      <td>1.017257</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128037</th>\n",
       "      <td>0.454743</td>\n",
       "      <td>-0.707635</td>\n",
       "      <td>0.493302</td>\n",
       "      <td>2.648089</td>\n",
       "      <td>1.064807</td>\n",
       "      <td>-0.680271</td>\n",
       "      <td>1.183838</td>\n",
       "      <td>0.169413</td>\n",
       "      <td>0.074553</td>\n",
       "      <td>1.247988</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102350</td>\n",
       "      <td>0.323975</td>\n",
       "      <td>-0.172601</td>\n",
       "      <td>0.126965</td>\n",
       "      <td>-0.001998</td>\n",
       "      <td>-0.398741</td>\n",
       "      <td>-0.385589</td>\n",
       "      <td>-0.205589</td>\n",
       "      <td>0.500245</td>\n",
       "      <td>0</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  \\\n",
       "169876  0.693938 -0.611712 -0.769705 -0.149759 -0.224877  2.028577 -2.019887   \n",
       "127467  0.453377 -0.814682  1.319219  1.329415  0.027273 -0.284871 -0.653985   \n",
       "137900  0.476770 -0.318193  1.118618  0.969864 -0.127052  0.569563 -0.532484   \n",
       "21513   0.183556 -1.328271  1.018378  1.775426 -1.574193 -0.117696 -0.457733   \n",
       "134700  0.468326  1.276712  0.617120 -0.578014  0.879173  0.061706 -1.472002   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "21440   0.183261 -2.986845 -8.663978 -1.910863  0.664058 -3.934875  0.861269   \n",
       "117583  0.432480  0.937083 -0.849673  0.524186 -0.020031 -0.606327  0.692302   \n",
       "73349   0.318852 -1.149963  1.696462  1.637114  2.658991 -0.021502  0.192287   \n",
       "267336  0.941757  1.754554 -0.699398 -0.076332  0.443915 -0.672082  0.389061   \n",
       "128037  0.454743 -0.707635  0.493302  2.648089  1.064807 -0.680271  1.183838   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "169876  0.292491 -0.523020  0.358468  ... -0.075208  0.045536  0.380739   \n",
       "127467  0.321552  0.435975 -0.704298  ... -0.128619 -0.368565  0.090660   \n",
       "137900  0.706252 -0.064966 -0.463271  ... -0.305402 -0.774704 -0.123884   \n",
       "21513   0.681867 -0.031641  0.383872  ... -0.220815 -0.419013 -0.239197   \n",
       "134700  0.373692 -0.287204 -0.084482  ... -0.160161 -0.430404 -0.076738   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "21440   1.647511 -0.480963 -1.546866  ...  1.252092 -0.993085 -2.173147   \n",
       "117583 -0.463724  0.148857  0.785062  ... -0.143322 -0.479981 -0.237902   \n",
       "73349   0.205204  0.588754 -1.187820  ...  0.025147  0.086506 -0.262748   \n",
       "267336 -0.807534  0.202915  0.858635  ...  0.141950  0.358412  0.259748   \n",
       "128037  0.169413  0.074553  1.247988  ... -0.102350  0.323975 -0.172601   \n",
       "\n",
       "             V24       V25       V26       V27       V28     Amount  Class  \n",
       "169876  0.023440 -2.220686 -0.201146  0.066501  0.221180  -0.282401      0  \n",
       "127467  0.401147 -0.261034  0.080621  0.162427  0.059456  -0.279746      0  \n",
       "137900 -0.495687 -0.018148  0.121679  0.249050  0.092516  -0.294977      0  \n",
       "21513   0.009967  0.232829  0.814177  0.098797 -0.004273  -0.084119      0  \n",
       "134700  0.258708  0.552170  0.370701 -0.034255  0.041709  -0.296793      0  \n",
       "...          ...       ...       ...       ...       ...        ...    ...  \n",
       "21440   0.145570 -0.235062 -0.227411 -0.382702  0.404045  32.002515      0  \n",
       "117583 -0.715247  0.251418  0.975406 -0.060168  0.023771   2.086495      0  \n",
       "73349   0.321538  0.341667  0.210343 -0.162047  0.031193  -0.201495      0  \n",
       "267336  0.746839 -0.560808  0.104636 -0.005853 -0.019622   1.017257      0  \n",
       "128037  0.126965 -0.001998 -0.398741 -0.385589 -0.205589   0.500245      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.sample(frac=1, random_state=1)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eda41ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_val_split(train, test, val):\n",
    "    train_np, test_np,val_np = train.to_numpy(), test.to_numpy(), val.to_numpy()\n",
    "    x_train, y_train = train_np[:, :-1], train_np[:, -1]\n",
    "    x_test, y_test = test_np[:, :-1], test_np[:, -1]\n",
    "    x_val, y_val = val_np[:, :-1], val_np[:, -1]\n",
    "    return x_train, y_train, x_test, y_test, x_val, y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0550c424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe to keep record of each model score\n",
    "dfs = []\n",
    "def store_score(scores,model):\n",
    "    del scores['macro avg']\n",
    "    del scores['weighted avg']\n",
    "    \n",
    "    not_fraud_df = pd.DataFrame.from_dict([scores['0.0']])\n",
    "    not_fraud_df.columns = ['precision_nf','recall_nf','f1_score_nf','support_nf']   # not_fraud instances precision, recall, and f1_score\n",
    "    \n",
    "    fraud_df = pd.DataFrame.from_dict([scores['1.0']])\n",
    "    fraud_df.columns = ['precision_fr','recall_fr','f1_score_fr','support_fr']  # fraud instances precision, recall, and f1_score\n",
    "    \n",
    "    acc_df = pd.DataFrame.from_dict([scores['accuracy']])\n",
    "    acc_df.columns = ['accuracy']                                     # accuracy of each model\n",
    "    \n",
    "    name = pd.DataFrame([str(model)], columns =['Model_name'])    # storing name of each model \n",
    "    \n",
    "    frames = [name,not_fraud_df, fraud_df, acc_df]  \n",
    "    result = pd.concat(frames,axis=1)\n",
    "\n",
    "    # Append the DataFrame to the list\n",
    "    dfs.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86291a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def report(y_val,y_pred,model):\n",
    "    res = classification_report(y_val, y_pred, target_names=[\"Not Fraud\",\"Fraud\"])\n",
    "    metric = classification_report(y_val, y_pred, output_dict=True)\n",
    "    store_score(metric,model)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c4a98c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f43f0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logReg(train_x, train_y, val_x, val_y,model):\n",
    "    logistic_model = LogisticRegression()\n",
    "    logistic_model.fit(train_x, train_y)\n",
    "    score = report(val_y, logistic_model.predict(val_x),model)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd61f17d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_net_predictions(model, x):\n",
    "    return (model.predict(x).flatten() > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "134403f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    239589\n",
       " 1       411\n",
       " Name: Class, dtype: int64,\n",
       " 0    21955\n",
       " 1       45\n",
       " Name: Class, dtype: int64,\n",
       " 0    22771\n",
       " 1       36\n",
       " Name: Class, dtype: int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test, val = new_df[:240000], new_df[240000:262000], new_df[262000:]\n",
    "train['Class'].value_counts(), test['Class'].value_counts(), val['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9763adf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((240000, 30), (240000,), (22000, 30), (22000,), (22807, 30), (22807,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, y_train, x_test, y_test, x_val, y_val = train_test_val_split(train, test,val)\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape, x_val.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f305c80d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.73      0.53      0.61        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.87      0.76      0.81     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chenn\\Documents\\python_jupyter\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: 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"
     ]
    }
   ],
   "source": [
    "print(logReg(x_train, y_train, x_val, y_val,'Logistic Regression Normal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e223a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random forest\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd36233b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RanFor(train_x, train_y, val_x,val_y,model):\n",
    "    rf = RandomForestClassifier(max_depth=2, n_jobs=1)\n",
    "    rf.fit(train_x, train_y)\n",
    "    score = report(val_y, rf.predict(val_x),model)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b4bc3ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.77      0.47      0.59        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.89      0.74      0.79     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(RanFor(x_train,y_train, x_val, y_val, 'Random Forest Normal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ce0946ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e7f048c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm(train_x, train_y, val_x, val_y,model):\n",
    "    svc = LinearSVC(class_weight='balanced')\n",
    "    svc.fit(train_x, train_y)\n",
    "    score = report(val_y, svc.predict(val_x),model)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc6badc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chenn\\Documents\\python_jupyter\\Lib\\site-packages\\sklearn\\svm\\_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.44      0.75      0.56        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.72      0.87      0.78     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chenn\\Documents\\python_jupyter\\Lib\\site-packages\\sklearn\\svm\\_base.py:1242: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(svm(x_train, y_train, x_val, y_val,'Support Vector Machine Normal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee6c5d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    284315\n",
       " Name: Class, dtype: int64,\n",
       " 1    492\n",
       " Name: Class, dtype: int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_frauds = new_df.query('Class == 0')\n",
    "frauds = new_df.query('Class == 1')\n",
    "not_frauds['Class'].value_counts(), frauds['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22a08df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    492\n",
       "0    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = pd.concat([frauds, not_frauds.sample(len(frauds), random_state=1)])\n",
    "balanced_df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bdc49ef9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>18372</th>\n",
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       "      <td>0.107049</td>\n",
       "      <td>0.175922</td>\n",
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       "      <td>-0.075890</td>\n",
       "      <td>-0.667909</td>\n",
       "      <td>-0.642848</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>0.488410</td>\n",
       "      <td>0.292345</td>\n",
       "      <td>-0.307413</td>\n",
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       "      <td>0.933932</td>\n",
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       "      <td>0.868046</td>\n",
       "      <td>-0.505279</td>\n",
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       "      <td>0.223098</td>\n",
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       "      <td>2.313731</td>\n",
       "      <td>0.252330</td>\n",
       "      <td>0.307219</td>\n",
       "      <td>0.859051</td>\n",
       "      <td>0.184033</td>\n",
       "      <td>-0.308269</td>\n",
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       "      <th>276071</th>\n",
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       "      <td>-1.082511</td>\n",
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       "      <td>-2.840126</td>\n",
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       "      <td>-2.282725</td>\n",
       "      <td>-0.292885</td>\n",
       "      <td>...</td>\n",
       "      <td>1.774460</td>\n",
       "      <td>-0.771390</td>\n",
       "      <td>0.065727</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>-0.057578</td>\n",
       "      <td>0.242652</td>\n",
       "      <td>-0.268649</td>\n",
       "      <td>-0.743713</td>\n",
       "      <td>1.443443</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>156988</th>\n",
       "      <td>0.632535</td>\n",
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       "      <td>2.809299</td>\n",
       "      <td>-5.825406</td>\n",
       "      <td>5.835566</td>\n",
       "      <td>0.512320</td>\n",
       "      <td>-0.615622</td>\n",
       "      <td>-2.916576</td>\n",
       "      <td>0.776710</td>\n",
       "      <td>-1.878832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284841</td>\n",
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       "      <td>-0.083995</td>\n",
       "      <td>-0.651442</td>\n",
       "      <td>0.454594</td>\n",
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       "      <td>0.756953</td>\n",
       "      <td>0.383869</td>\n",
       "      <td>-0.307413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "18372   0.170309 -1.762593  0.256143  1.683125 -1.279233 -1.902762  1.004210   \n",
       "96341   0.380388  1.227614 -0.668974 -0.271785 -0.589440 -0.604795 -0.350285   \n",
       "248296  0.890522 -0.613696  3.698772 -5.534941  5.620486  1.649263 -2.335145   \n",
       "264328  0.933932 -0.011624  0.640413  0.868046 -0.505279  0.261938  0.223098   \n",
       "208904  0.794730 -0.679341  1.217389 -0.316778 -1.086725  0.855349 -0.980760   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "81557   0.341393 -4.502731 -3.876484  1.341248  0.113400  0.189428 -0.560985   \n",
       "276071  0.965803  2.091900 -0.757459 -1.192258 -0.755458 -0.620324 -0.322077   \n",
       "175971  0.709373  1.972989  0.157281 -1.715078  1.207451  0.681612 -0.615282   \n",
       "27738   0.200727 -2.439237  2.591458 -2.840126  1.286244 -1.777016 -1.436139   \n",
       "156988  0.632535  0.745153  2.809299 -5.825406  5.835566  0.512320 -0.615622   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "18372  -1.009748 -2.432546  0.458860  ...  2.493579  0.320829 -0.535481   \n",
       "96341  -0.486365 -0.010809 -0.794944  ... -0.026055 -0.295255 -0.180459   \n",
       "248296 -0.907188  0.706362 -3.747646  ...  0.319261 -0.471379 -0.075890   \n",
       "264328  0.239049  0.150877  0.225142  ...  0.069401  0.268024  0.261459   \n",
       "208904  0.970589  0.133116 -0.357671  ... -0.083048 -0.137032 -0.238920   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "81557  -0.140478  0.684651  0.475363  ... -0.140218  0.049411  2.313731   \n",
       "276071 -1.082511  0.117200 -0.140927  ...  0.288253  0.831939  0.142007   \n",
       "175971  0.601791 -0.291935 -0.132265  ...  0.098640  0.467533 -0.078973   \n",
       "27738  -2.206056 -2.282725 -0.292885  ...  1.774460 -0.771390  0.065727   \n",
       "156988 -2.916576  0.776710 -1.878832  ...  0.284841 -0.874383 -0.083995   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount  Class  \n",
       "18372   0.499401 -0.915196 -0.423434  0.107049  0.175922  2.906449      0  \n",
       "96341  -0.436539  0.494649 -0.283738 -0.001128  0.035075  1.062111      1  \n",
       "248296 -0.667909 -0.642848  0.070600  0.488410  0.292345 -0.307413      1  \n",
       "264328  0.683742 -1.567901 -0.816674  0.185781  0.283021 -0.272619      0  \n",
       "208904 -0.617244  0.039020 -0.081848  0.234633  0.128382 -0.307273      0  \n",
       "...          ...       ...       ...       ...       ...       ...    ...  \n",
       "81557   0.252330  0.307219  0.859051  0.184033 -0.308269  4.227625      0  \n",
       "276071  0.592615 -0.196143 -0.136676  0.020182 -0.015470 -0.028645      1  \n",
       "175971 -0.371882  0.486038 -0.490665 -0.018374 -0.070911  0.075735      0  \n",
       "27738   0.103916 -0.057578  0.242652 -0.268649 -0.743713  1.443443      1  \n",
       "156988 -0.651442  0.454594  0.050376  0.756953  0.383869 -0.307413      1  \n",
       "\n",
       "[984 rows x 31 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = balanced_df.sample(frac=1, random_state=1)\n",
    "balanced_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f04a7655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700, 30), (700,), (142, 30), (142,), (142, 30), (142,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df_np  = balanced_df.to_numpy()\n",
    "\n",
    "x_train_b, y_train_b = balanced_df_np[:700, :-1], balanced_df_np[:700, -1]\n",
    "x_test_b, y_test_b = balanced_df_np[700:842, :-1], balanced_df_np[700:842, -1]\n",
    "x_val_b, y_val_b = balanced_df_np[842:, :-1], balanced_df_np[842:, -1]\n",
    "x_train_b.shape, y_train_b.shape, x_test_b.shape, y_test_b.shape, x_val_b.shape, y_val_b.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e0cb538f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0    353\n",
       " 0.0    347\n",
       " dtype: int64,\n",
       " 0.0    73\n",
       " 1.0    69\n",
       " dtype: int64,\n",
       " 0.0    72\n",
       " 1.0    70\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train_b).value_counts(), pd.Series(y_test_b).value_counts(), pd.Series(y_val_b).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5be19b1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.96      0.93      0.94        72\n",
      "       Fraud       0.93      0.96      0.94        70\n",
      "\n",
      "    accuracy                           0.94       142\n",
      "   macro avg       0.94      0.94      0.94       142\n",
      "weighted avg       0.94      0.94      0.94       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(logReg(x_train_b, y_train_b,x_val_b,y_val_b, model ='Logistic Regression Balanced'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "31b1b0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.92      0.99      0.95        72\n",
      "       Fraud       0.98      0.91      0.95        70\n",
      "\n",
      "    accuracy                           0.95       142\n",
      "   macro avg       0.95      0.95      0.95       142\n",
      "weighted avg       0.95      0.95      0.95       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(RanFor(x_train_b, y_train_b, x_val_b, y_val_b, model ='Random Forest Balanced'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff0ae63d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.96      0.93      0.94        72\n",
      "       Fraud       0.93      0.96      0.94        70\n",
      "\n",
      "    accuracy                           0.94       142\n",
      "   macro avg       0.94      0.94      0.94       142\n",
      "weighted avg       0.94      0.94      0.94       142\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chenn\\Documents\\python_jupyter\\Lib\\site-packages\\sklearn\\svm\\_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "C:\\Users\\chenn\\Documents\\python_jupyter\\Lib\\site-packages\\sklearn\\svm\\_base.py:1242: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "print(svm(x_train_b, y_train_b, x_val_b, y_val_b, model='Support Vector Machine Balanced'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc535aca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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