model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Workflow\n",
    "# Data cleaning\n",
    "# Pre-processing\n",
    "# Data analysis and feature engineering\n",
    "# Train test split\n",
    "# Classification model\n",
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Credit Card Fraud Detection\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajayc\\AppData\\Local\\Temp\\ipykernel_9576\\3935749965.py:1: DtypeWarning: Columns (14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  cred_data = pd.read_csv(\"creditcard.csv\")\n"
     ]
    }
   ],
   "source": [
    "cred_data = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
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       "      <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",
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       "      <th>0</th>\n",
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       "      <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",
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       "      <td>0.128539</td>\n",
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       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0.0</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>0.0</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>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
       "1  0.125895 -0.008983  0.014724    2.69    0.0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66    0.0  \n",
       "3 -0.221929  0.062723  0.061458  123.50    0.0  \n",
       "4  0.502292  0.219422  0.215153   69.99    0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cred_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
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      " 2   V2      284807 non-null  float64\n",
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      " 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  object \n",
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      " 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   284806 non-null  float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 67.4+ MB\n"
     ]
    }
   ],
   "source": [
    "cred_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "      <th>V24</th>\n",
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       "      <th>V26</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284806.000000</td>\n",
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       "    <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>0.001724</td>\n",
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       "    <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>0.041485</td>\n",
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       "    <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",
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       "    <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 × 30 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  284806.000000  \n",
       "mean        0.001724  \n",
       "std         0.041485  \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 30 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no null values\n",
    "cred_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cred_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    284315\n",
       "1.0       491\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1- fraudulent\n",
    "# 0- legitimate\n",
    "cred_data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is an unbalanced dataset\n",
    "legit = cred_data[cred_data.Class==0]\n",
    "fraud = cred_data[cred_data.Class==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284315, 31)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.shape\n",
    "legit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legit.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     491.000000\n",
       "mean      121.382831\n",
       "std       256.285732\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.210000\n",
       "75%       105.350000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>V20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>94838.202258</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>-0.006271</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>-0.007860</td>\n",
       "      <td>0.005453</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.009637</td>\n",
       "      <td>-0.000987</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>-0.001235</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>-0.000072</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>88.291022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>80910.299389</td>\n",
       "      <td>-4.775469</td>\n",
       "      <td>3.637589</td>\n",
       "      <td>-7.049822</td>\n",
       "      <td>4.546618</td>\n",
       "      <td>-3.160412</td>\n",
       "      <td>-1.398415</td>\n",
       "      <td>-5.580736</td>\n",
       "      <td>0.571936</td>\n",
       "      <td>-2.585828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368796</td>\n",
       "      <td>0.713694</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>-0.043192</td>\n",
       "      <td>-0.104746</td>\n",
       "      <td>0.040963</td>\n",
       "      <td>0.052049</td>\n",
       "      <td>0.171437</td>\n",
       "      <td>0.075749</td>\n",
       "      <td>121.382831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
       "0.0    94838.202258  0.008258 -0.006271  0.012171 -0.007860  0.005453   \n",
       "1.0    80910.299389 -4.775469  3.637589 -7.049822  4.546618 -3.160412   \n",
       "\n",
       "             V6        V7        V8        V9  ...       V20       V21  \\\n",
       "Class                                          ...                       \n",
       "0.0    0.002419  0.009637 -0.000987  0.004467  ... -0.000644 -0.001235   \n",
       "1.0   -1.398415 -5.580736  0.571936 -2.585828  ...  0.368796  0.713694   \n",
       "\n",
       "            V22       V23       V24       V25       V26       V27       V28  \\\n",
       "Class                                                                         \n",
       "0.0   -0.000024  0.000070  0.000182 -0.000072 -0.000089 -0.000295 -0.000131   \n",
       "1.0    0.013191 -0.043192 -0.104746  0.040963  0.052049  0.171437  0.075749   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0.0     88.291022  \n",
       "1.0    121.382831  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cred_data.groupby('Class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Under sampling\n",
    "# build a sample dataset from original one containing similar distributions of normal and fraudulent transactions(492-lower)\n",
    "# nature of the dataset must not change\n",
    "legit_sample = legit.sample(n=492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cred = pd.concat([legit_sample, fraud], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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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>9925</th>\n",
       "      <td>14676.0</td>\n",
       "      <td>-0.539207</td>\n",
       "      <td>1.850881</td>\n",
       "      <td>1.844133</td>\n",
       "      <td>2.575076</td>\n",
       "      <td>0.778838</td>\n",
       "      <td>0.413409</td>\n",
       "      <td>0.897155</td>\n",
       "      <td>-0.457086</td>\n",
       "      <td>0.455518</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.556639</td>\n",
       "      <td>-0.749412</td>\n",
       "      <td>-0.159468</td>\n",
       "      <td>-0.534834</td>\n",
       "      <td>-0.249242</td>\n",
       "      <td>-0.077365</td>\n",
       "      <td>0.242465</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61872</th>\n",
       "      <td>50023.0</td>\n",
       "      <td>1.080003</td>\n",
       "      <td>-0.211661</td>\n",
       "      <td>0.355779</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>-0.754964</td>\n",
       "      <td>-1.015379</td>\n",
       "      <td>0.050696</td>\n",
       "      <td>-0.126164</td>\n",
       "      <td>0.331306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225515</td>\n",
       "      <td>-0.961650</td>\n",
       "      <td>0.080562</td>\n",
       "      <td>0.352700</td>\n",
       "      <td>0.096796</td>\n",
       "      <td>0.195744</td>\n",
       "      <td>-0.067392</td>\n",
       "      <td>0.030069</td>\n",
       "      <td>96.95</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158326</th>\n",
       "      <td>111129.0</td>\n",
       "      <td>1.785079</td>\n",
       "      <td>-1.266953</td>\n",
       "      <td>-1.370129</td>\n",
       "      <td>-0.697482</td>\n",
       "      <td>-0.505496</td>\n",
       "      <td>-0.552706</td>\n",
       "      <td>-0.287268</td>\n",
       "      <td>-0.264093</td>\n",
       "      <td>0.542070</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040963</td>\n",
       "      <td>-0.451850</td>\n",
       "      <td>0.160842</td>\n",
       "      <td>0.686095</td>\n",
       "      <td>-0.335508</td>\n",
       "      <td>-0.541883</td>\n",
       "      <td>-0.109893</td>\n",
       "      <td>-0.030670</td>\n",
       "      <td>217.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216310</th>\n",
       "      <td>140428.0</td>\n",
       "      <td>-1.007862</td>\n",
       "      <td>1.494068</td>\n",
       "      <td>-1.297659</td>\n",
       "      <td>-0.618695</td>\n",
       "      <td>0.599099</td>\n",
       "      <td>-0.789186</td>\n",
       "      <td>0.523312</td>\n",
       "      <td>0.302053</td>\n",
       "      <td>0.146108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319477</td>\n",
       "      <td>1.179096</td>\n",
       "      <td>-0.056740</td>\n",
       "      <td>-0.959357</td>\n",
       "      <td>-0.554058</td>\n",
       "      <td>-0.197686</td>\n",
       "      <td>-0.053524</td>\n",
       "      <td>0.039124</td>\n",
       "      <td>5.31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223875</th>\n",
       "      <td>143563.0</td>\n",
       "      <td>-2.175488</td>\n",
       "      <td>1.612577</td>\n",
       "      <td>-1.314710</td>\n",
       "      <td>-0.908361</td>\n",
       "      <td>0.550577</td>\n",
       "      <td>0.332167</td>\n",
       "      <td>0.226124</td>\n",
       "      <td>0.771806</td>\n",
       "      <td>-0.127208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020839</td>\n",
       "      <td>-0.047486</td>\n",
       "      <td>-0.042204</td>\n",
       "      <td>-0.409097</td>\n",
       "      <td>-0.210212</td>\n",
       "      <td>0.332468</td>\n",
       "      <td>-0.404002</td>\n",
       "      <td>0.251878</td>\n",
       "      <td>56.84</td>\n",
       "      <td>0.0</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  \\\n",
       "9925     14676.0 -0.539207  1.850881  1.844133  2.575076  0.778838  0.413409   \n",
       "61872    50023.0  1.080003 -0.211661  0.355779  0.625397 -0.754964 -1.015379   \n",
       "158326  111129.0  1.785079 -1.266953 -1.370129 -0.697482 -0.505496 -0.552706   \n",
       "216310  140428.0 -1.007862  1.494068 -1.297659 -0.618695  0.599099 -0.789186   \n",
       "223875  143563.0 -2.175488  1.612577 -1.314710 -0.908361  0.550577  0.332167   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "9925    0.897155 -0.457086  0.455518  ... -0.556639 -0.749412 -0.159468   \n",
       "61872   0.050696 -0.126164  0.331306  ... -0.225515 -0.961650  0.080562   \n",
       "158326 -0.287268 -0.264093  0.542070  ... -0.040963 -0.451850  0.160842   \n",
       "216310  0.523312  0.302053  0.146108  ...  0.319477  1.179096 -0.056740   \n",
       "223875  0.226124  0.771806 -0.127208  ... -0.020839 -0.047486 -0.042204   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "9925   -0.534834 -0.249242 -0.077365  0.242465 -0.136679    2.00    0.0  \n",
       "61872   0.352700  0.096796  0.195744 -0.067392  0.030069   96.95    0.0  \n",
       "158326  0.686095 -0.335508 -0.541883 -0.109893 -0.030670  217.03    0.0  \n",
       "216310 -0.959357 -0.554058 -0.197686 -0.053524  0.039124    5.31    0.0  \n",
       "223875 -0.409097 -0.210212  0.332468 -0.404002  0.251878   56.84    0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    492\n",
       "1.0    491\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cred['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>98787.016260</td>\n",
       "      <td>0.117746</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>-0.116270</td>\n",
       "      <td>-0.018500</td>\n",
       "      <td>0.115171</td>\n",
       "      <td>0.037284</td>\n",
       "      <td>0.026237</td>\n",
       "      <td>-0.008526</td>\n",
       "      <td>-0.028639</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011054</td>\n",
       "      <td>0.025147</td>\n",
       "      <td>0.022668</td>\n",
       "      <td>-0.007789</td>\n",
       "      <td>-0.010474</td>\n",
       "      <td>-0.027598</td>\n",
       "      <td>-0.010258</td>\n",
       "      <td>0.004610</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>82.855894</td>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>80910.299389</td>\n",
       "      <td>-4.775469</td>\n",
       "      <td>3.637589</td>\n",
       "      <td>-7.049822</td>\n",
       "      <td>4.546618</td>\n",
       "      <td>-3.160412</td>\n",
       "      <td>-1.398415</td>\n",
       "      <td>-5.580736</td>\n",
       "      <td>0.571936</td>\n",
       "      <td>-2.585828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368796</td>\n",
       "      <td>0.713694</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>-0.043192</td>\n",
       "      <td>-0.104746</td>\n",
       "      <td>0.040963</td>\n",
       "      <td>0.052049</td>\n",
       "      <td>0.171437</td>\n",
       "      <td>0.075749</td>\n",
       "      <td>121.382831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time        V1        V2        V3        V4        V5  \\\n",
       "Class                                                                   \n",
       "0.0    98787.016260  0.117746  0.012588 -0.116270 -0.018500  0.115171   \n",
       "1.0    80910.299389 -4.775469  3.637589 -7.049822  4.546618 -3.160412   \n",
       "\n",
       "             V6        V7        V8        V9  ...       V20       V21  \\\n",
       "Class                                          ...                       \n",
       "0.0    0.037284  0.026237 -0.008526 -0.028639  ... -0.011054  0.025147   \n",
       "1.0   -1.398415 -5.580736  0.571936 -2.585828  ...  0.368796  0.713694   \n",
       "\n",
       "            V22       V23       V24       V25       V26       V27       V28  \\\n",
       "Class                                                                         \n",
       "0.0    0.022668 -0.007789 -0.010474 -0.027598 -0.010258  0.004610 -0.000213   \n",
       "1.0    0.013191 -0.043192 -0.104746  0.040963  0.052049  0.171437  0.075749   \n",
       "\n",
       "           Amount  \n",
       "Class              \n",
       "0.0     82.855894  \n",
       "1.0    121.382831  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cred.groupby('Class').mean()\n",
    "# mean values are not changed so we got a good sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>9925</th>\n",
       "      <td>14676.0</td>\n",
       "      <td>-0.539207</td>\n",
       "      <td>1.850881</td>\n",
       "      <td>1.844133</td>\n",
       "      <td>2.575076</td>\n",
       "      <td>0.778838</td>\n",
       "      <td>0.413409</td>\n",
       "      <td>0.897155</td>\n",
       "      <td>-0.457086</td>\n",
       "      <td>0.455518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695723</td>\n",
       "      <td>-0.556639</td>\n",
       "      <td>-0.749412</td>\n",
       "      <td>-0.159468</td>\n",
       "      <td>-0.534834</td>\n",
       "      <td>-0.249242</td>\n",
       "      <td>-0.077365</td>\n",
       "      <td>0.242465</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>2.00</td>\n",
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       "    <tr>\n",
       "      <th>61872</th>\n",
       "      <td>50023.0</td>\n",
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       "      <td>-0.211661</td>\n",
       "      <td>0.355779</td>\n",
       "      <td>0.625397</td>\n",
       "      <td>-0.754964</td>\n",
       "      <td>-1.015379</td>\n",
       "      <td>0.050696</td>\n",
       "      <td>-0.126164</td>\n",
       "      <td>0.331306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013083</td>\n",
       "      <td>-0.225515</td>\n",
       "      <td>-0.961650</td>\n",
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       "      <td>-0.287268</td>\n",
       "      <td>-0.264093</td>\n",
       "      <td>0.542070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.348697</td>\n",
       "      <td>-0.040963</td>\n",
       "      <td>-0.451850</td>\n",
       "      <td>0.160842</td>\n",
       "      <td>0.686095</td>\n",
       "      <td>-0.335508</td>\n",
       "      <td>-0.541883</td>\n",
       "      <td>-0.109893</td>\n",
       "      <td>-0.030670</td>\n",
       "      <td>217.03</td>\n",
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       "      <th>216310</th>\n",
       "      <td>140428.0</td>\n",
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       "      <td>1.494068</td>\n",
       "      <td>-1.297659</td>\n",
       "      <td>-0.618695</td>\n",
       "      <td>0.599099</td>\n",
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       "      <td>0.523312</td>\n",
       "      <td>0.302053</td>\n",
       "      <td>0.146108</td>\n",
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       "      <td>-0.072615</td>\n",
       "      <td>0.319477</td>\n",
       "      <td>1.179096</td>\n",
       "      <td>-0.056740</td>\n",
       "      <td>-0.959357</td>\n",
       "      <td>-0.554058</td>\n",
       "      <td>-0.197686</td>\n",
       "      <td>-0.053524</td>\n",
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       "      <th>223875</th>\n",
       "      <td>143563.0</td>\n",
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       "      <td>-1.314710</td>\n",
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       "      <td>0.550577</td>\n",
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       "      <td>0.771806</td>\n",
       "      <td>-0.127208</td>\n",
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       "      <td>-0.218799</td>\n",
       "      <td>-0.020839</td>\n",
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       "      <td>0.251878</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>279863</th>\n",
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       "      <td>1.125653</td>\n",
       "      <td>-4.518331</td>\n",
       "      <td>1.749293</td>\n",
       "      <td>-1.566487</td>\n",
       "      <td>-2.010494</td>\n",
       "      <td>-0.882850</td>\n",
       "      <td>0.697211</td>\n",
       "      <td>-2.064945</td>\n",
       "      <td>...</td>\n",
       "      <td>1.252967</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>-0.319189</td>\n",
       "      <td>0.639419</td>\n",
       "      <td>-0.294885</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.788395</td>\n",
       "      <td>0.292680</td>\n",
       "      <td>0.147968</td>\n",
       "      <td>390.00</td>\n",
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       "    <tr>\n",
       "      <th>280143</th>\n",
       "      <td>169347.0</td>\n",
       "      <td>1.378559</td>\n",
       "      <td>1.289381</td>\n",
       "      <td>-5.004247</td>\n",
       "      <td>1.411850</td>\n",
       "      <td>0.442581</td>\n",
       "      <td>-1.326536</td>\n",
       "      <td>-1.413170</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>-1.127396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226138</td>\n",
       "      <td>0.370612</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.145640</td>\n",
       "      <td>-0.081049</td>\n",
       "      <td>0.521875</td>\n",
       "      <td>0.739467</td>\n",
       "      <td>0.389152</td>\n",
       "      <td>0.186637</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>169351.0</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247968</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281144</th>\n",
       "      <td>169966.0</td>\n",
       "      <td>-3.113832</td>\n",
       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
       "      <td>-2.943548</td>\n",
       "      <td>-2.208002</td>\n",
       "      <td>1.058733</td>\n",
       "      <td>-1.632333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.306271</td>\n",
       "      <td>0.583276</td>\n",
       "      <td>-0.269209</td>\n",
       "      <td>-0.456108</td>\n",
       "      <td>-0.183659</td>\n",
       "      <td>-0.328168</td>\n",
       "      <td>0.606116</td>\n",
       "      <td>0.884876</td>\n",
       "      <td>-0.253700</td>\n",
       "      <td>245.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281674</th>\n",
       "      <td>170348.0</td>\n",
       "      <td>1.991976</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>-2.583441</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>1.151147</td>\n",
       "      <td>-0.096695</td>\n",
       "      <td>0.223050</td>\n",
       "      <td>-0.068384</td>\n",
       "      <td>0.577829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017652</td>\n",
       "      <td>-0.164350</td>\n",
       "      <td>-0.295135</td>\n",
       "      <td>-0.072173</td>\n",
       "      <td>-0.450261</td>\n",
       "      <td>0.313267</td>\n",
       "      <td>-0.289617</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>42.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>983 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "9925     14676.0 -0.539207  1.850881  1.844133  2.575076  0.778838  0.413409   \n",
       "61872    50023.0  1.080003 -0.211661  0.355779  0.625397 -0.754964 -1.015379   \n",
       "158326  111129.0  1.785079 -1.266953 -1.370129 -0.697482 -0.505496 -0.552706   \n",
       "216310  140428.0 -1.007862  1.494068 -1.297659 -0.618695  0.599099 -0.789186   \n",
       "223875  143563.0 -2.175488  1.612577 -1.314710 -0.908361  0.550577  0.332167   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
       "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
       "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
       "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
       "\n",
       "              V7        V8        V9  ...       V20       V21       V22  \\\n",
       "9925    0.897155 -0.457086  0.455518  ...  0.695723 -0.556639 -0.749412   \n",
       "61872   0.050696 -0.126164  0.331306  ... -0.013083 -0.225515 -0.961650   \n",
       "158326 -0.287268 -0.264093  0.542070  ...  0.348697 -0.040963 -0.451850   \n",
       "216310  0.523312  0.302053  0.146108  ... -0.072615  0.319477  1.179096   \n",
       "223875  0.226124  0.771806 -0.127208  ... -0.218799 -0.020839 -0.047486   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "279863 -0.882850  0.697211 -2.064945  ...  1.252967  0.778584 -0.319189   \n",
       "280143 -1.413170  0.248525 -1.127396  ...  0.226138  0.370612  0.028234   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.247968  0.751826  0.834108   \n",
       "281144 -2.208002  1.058733 -1.632333  ...  0.306271  0.583276 -0.269209   \n",
       "281674  0.223050 -0.068384  0.577829  ... -0.017652 -0.164350 -0.295135   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \n",
       "9925   -0.159468 -0.534834 -0.249242 -0.077365  0.242465 -0.136679    2.00  \n",
       "61872   0.080562  0.352700  0.096796  0.195744 -0.067392  0.030069   96.95  \n",
       "158326  0.160842  0.686095 -0.335508 -0.541883 -0.109893 -0.030670  217.03  \n",
       "216310 -0.056740 -0.959357 -0.554058 -0.197686 -0.053524  0.039124    5.31  \n",
       "223875 -0.042204 -0.409097 -0.210212  0.332468 -0.404002  0.251878   56.84  \n",
       "...          ...       ...       ...       ...       ...       ...     ...  \n",
       "279863  0.639419 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00  \n",
       "280143 -0.145640 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76  \n",
       "280149  0.190944  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89  \n",
       "281144 -0.456108 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00  \n",
       "281674 -0.072173 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53  \n",
       "\n",
       "[983 rows x 30 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = new_cred.drop(columns='Class', axis=1)\n",
    "Y = new_cred['Class']\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9925      0.0\n",
       "61872     0.0\n",
       "158326    0.0\n",
       "216310    0.0\n",
       "223875    0.0\n",
       "         ... \n",
       "279863    1.0\n",
       "280143    1.0\n",
       "280149    1.0\n",
       "281144    1.0\n",
       "281674    1.0\n",
       "Name: Class, Length: 983, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(983, 30) (786, 30) (197, 30)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)\n",
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajayc\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model training\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9402035623409669"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pred = model.predict(X_train)\n",
    "train_acc = accuracy_score(X_train_pred, Y_train)\n",
    "train_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9238578680203046"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_pred = model.predict(X_test)\n",
    "test_acc = accuracy_score(X_test_pred, Y_test)\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Class: 1\n",
      "Prediction: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajayc\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "check_data = (472,-3.0435406239976,-3.15730712090228,1.08846277997285,2.2886436183814,1.35980512966107,-1.06482252298131,0.325574266158614,-0.0677936531906277,-0.270952836226548,-0.838586564582682,-0.414575448285725,-0.503140859566824,0.676501544635863,-1.69202893305906,2.00063483909015,0.666779695901966,0.599717413841732,1.72532100745514,0.283344830149495,2.10233879259444,0.661695924845707,0.435477208966341,1.37596574254306,-0.293803152734021,0.279798031841214,-0.145361714815161,-0.252773122530705,0.0357642251788156,529)\n",
    "\n",
    "check_data_np = np.asarray(check_data)\n",
    "\n",
    "# reshape the numpy array\n",
    "check_data_reshaped = check_data_np.reshape(1, -1)\n",
    "\n",
    "# original class =  1\n",
    "check = model.predict(check_data_reshaped)\n",
    "print(\"Original Class: 1\")\n",
    "print(f\"Prediction: {check[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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