{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ce42f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('./Data/creditcard.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc3a59a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68699b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit Card Fraud Detection Data - rows :  284807  columns :  31\n"
     ]
    }
   ],
   "source": [
    "print('Credit Card Fraud Detection Data - rows : ',data.shape[0], ' columns : ', data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab0082a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</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>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>2.040130e-15</td>\n",
       "      <td>-1.698953e-15</td>\n",
       "      <td>-1.893285e-16</td>\n",
       "      <td>-3.147640e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.473120e-16</td>\n",
       "      <td>8.042109e-16</td>\n",
       "      <td>5.282512e-16</td>\n",
       "      <td>4.456271e-15</td>\n",
       "      <td>1.426896e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-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  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-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  -1.552103e-15  2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-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   ...  1.473120e-16  8.042109e-16  5.282512e-16  4.456271e-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   1.426896e-15  1.701640e-15 -3.662252e-16 -1.217809e-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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2335c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 284315, 1: 492})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 종속 변수의 분포 확인\n",
    "\n",
    "from collections import Counter\n",
    "Counter(data.Class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2295d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot') #Using ggplot2 style visuals\n",
    "f, ax = plt.subplots(figsize = (11, 15)) # graph size\n",
    "\n",
    "ax.set_facecolor('#fafafa') # graph color\n",
    "ax.set(xlim = (-5, 5)) # X axis range\n",
    "plt.ylabel('Variables')\n",
    "plt.title('Overview Data Set')\n",
    "\n",
    "ax = sns.boxplot(data = data.drop(columns=['Class']), # V1 ~ V28 확인\n",
    "                orient='h',\n",
    "                palette='Set2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98f5045d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.5 for `bw_method`, but please see the docs for the new parameters and update your code.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x2016 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = data.drop(['Time', 'Amount'], axis=1)\n",
    "var = data1.columns.values[:-1] # V1 ~ V28\n",
    "\n",
    "i = 0\n",
    "t0 = data.loc[data['Class'] == 0] # Class : 0인 행만 추출\n",
    "t1 = data.loc[data['Class'] == 1] # Class : 1인 행만 추출\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure()\n",
    "fig, ax = plt.subplots(8, 4, figsize=(16, 28))\n",
    "\n",
    "for feature in var:\n",
    "    i += 1\n",
    "    plt.subplot(7, 4, i) # 28개의 그래프\n",
    "    sns.kdeplot(t0[feature], bw = 0.5, label = \"Class = 0\")\n",
    "    sns.kdeplot(t1[feature], bw = 0.5, label = \"Class = 1\")\n",
    "    plt.xlabel(feature, fontsize = 12) # 라벨 속성값\n",
    "    locs, labels = plt.xticks()\n",
    "    plt.tick_params(axis = 'both', which = 'major', labelsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4374adb7",
   "metadata": {},
   "source": [
    "**각 변수 별 그래프를 타겟변수에 대해서 그려보았을 때 차이가 있는 변수들은 다음과 같이 정의할 수 있다.**\n",
    "\n",
    "- 1) 타겟 변수에 대해 분포 차이가 많이 나는 변수 : V4, V11\n",
    "- 2) 타겟 변수에 대해 분포 차이가 비교적 많이 존재하는 변수 : V12, V14, V18\n",
    "- 3) 타겟 변수에 대해 분포 차이가 비교적 적게 존재하는 변수 : V1, V2, V3, V10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b509e1c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X : (284807, 30)\n",
      "Shape of y : (284807, 1)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(data.iloc[:, data.columns != 'Class'])\n",
    "y = np.array(data.iloc[:, data.columns == 'Class'])\n",
    "\n",
    "print('Shape of X : {}'.format(X.shape))\n",
    "print('Shape of y : {}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15642252",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "770d84ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_evaluation(label, predict):\n",
    "    cf_matrix = confusion_matrix(label, predict)\n",
    "    Accuracy = (cf_matrix[0][0] + cf_matrix[1][1]) / sum(sum(cf_matrix))\n",
    "    Precision = cf_matrix[1][1] / (cf_matrix[1][1] + cf_matrix[0][1])\n",
    "    Recall = cf_matrix[1][1] / (cf_matrix[1][1] + cf_matrix[1][0])\n",
    "    F1_Score = (2 * Recall * Precision) / (Recall + Precision)\n",
    "    print(\"Model Evaluation with Label : 1\")\n",
    "    print(\"Accuracy : \", Accuracy)\n",
    "    print(\"Precision : \", Precision)\n",
    "    print(\"Recall : \", Recall)\n",
    "    print(\"F1-Score : \", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7762a44b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011879 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 227845, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score -0.001718\n",
      "[LightGBM] [Info] Start training from score -6.367713\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9991924440855307\n",
      "Precision :  0.9365079365079365\n",
      "Recall :  0.5841584158415841\n",
      "F1-Score :  0.7195121951219512\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import lightgbm as lgb\n",
    "lgb_dtrain = lgb.Dataset(data = pd.DataFrame(X_train), label = pd.DataFrame(y_train)) # 학습 데이터를 LightGBM 모델에 맞게 변환\n",
    "lgb_param = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'objective': 'multiclass', # 목적 함수\n",
    "            'num_class': len(set(pd.DataFrame(y_train))) + 1} # 파라미터 추가, Label must be in [0, num_class) -> num_class보다 1 커야한다.\n",
    "lgb_model = lgb.train(params = lgb_param, train_set = lgb_dtrain) # 학습 진행\n",
    "lgb_model_predict = np.argmax(lgb_model.predict(X_test), axis = 1) # 평가 데이터 예측, Softmax의 결과값 중 가장 큰 값의 Label로 예측\n",
    "model_evaluation(y_test, lgb_model_predict) # 모델 분류 결과 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dbc219a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99871391, 0.00128609],\n",
       "       [0.9989149 , 0.0010851 ],\n",
       "       [0.99132356, 0.00867644],\n",
       "       ...,\n",
       "       [0.9989149 , 0.0010851 ],\n",
       "       [0.9989149 , 0.0010851 ],\n",
       "       [0.9989149 , 0.0010851 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83c9beb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Number of positive: 391, number of negative: 227454\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010327 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 227845, number of used features: 30\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.001716 -> initscore=-6.365996\n",
      "[LightGBM] [Info] Start training from score -6.365996\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9991924440855307\n",
      "Precision :  0.9365079365079365\n",
      "Recall :  0.5841584158415841\n",
      "F1-Score :  0.7195121951219512\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import lightgbm as lgb\n",
    "lgb_dtrain = lgb.Dataset(data = pd.DataFrame(X_train), label = pd.DataFrame(y_train)) # 학습 데이터를 LightGBM 모델에 맞게 변환\n",
    "lgb_param = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'objective': 'binary'} # 파라미터 추가, Label must be in [0, num_class) -> num_class보다 1 커야한다.\n",
    "lgb_model = lgb.train(params = lgb_param, train_set = lgb_dtrain) # 학습 진행\n",
    "pred= np.repeat(0, len(y_test))\n",
    "pred[lgb_model.predict(X_test) >0.5] =1\n",
    "model_evaluation(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44cc38f",
   "metadata": {},
   "source": [
    "# SMOTE 를 이용해서 OverSampling 을 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa03d55c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (227845, 30)\n",
      "Number transactions y_train dataset:  (227845, 1)\n",
      "Number transactions X_test dataset:  (56962, 30)\n",
      "Number transactions y_test dataset:  (56962, 1)\n"
     ]
    }
   ],
   "source": [
    "# 기존의 X_train, y_train, X_test, y_test의 형태 확인\n",
    "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
    "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
    "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
    "print(\"Number transactions y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c290899d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': [391]\n",
      "Before OverSampling, counts of label '0': [227454] \n",
      "\n",
      "After OverSampling, counts of label '1': 68236\n",
      "After OverSampling, counts of label '0': 227454\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train == 1))) # y_train 중 레이블 값이 1인 데이터의 개수\n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train == 0))) # y_train 중 레이블 값이 0 인 데이터의 개수\n",
    "\n",
    "sm = SMOTE(random_state = 42, sampling_strategy=0.3) # SMOTE 알고리즘\n",
    "X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel()) # Over Sampling 진행\n",
    "\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80d10c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013819 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 295690, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score -0.262364\n",
      "[LightGBM] [Info] Start training from score -1.466339\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9990519995786665\n",
      "Precision :  0.688\n",
      "Recall :  0.8514851485148515\n",
      "F1-Score :  0.7610619469026548\n"
     ]
    }
   ],
   "source": [
    "lgb_dtrain2 = lgb.Dataset(data = pd.DataFrame(X_train_res), label = pd.DataFrame(y_train_res)) # 학습 데이터를 LightGBM 모델에 맞게 변환\n",
    "lgb_param2 = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'objective': 'multiclass', # 목적 함수\n",
    "            'num_class': len(set(pd.DataFrame(y_train_res))) + 1} # 파라미터 추가, Label must be in [0, num_class) -> num_class보다 1 커야한다.\n",
    "lgb_model2 = lgb.train(params = lgb_param2, train_set = lgb_dtrain2) # 학습 진행\n",
    "lgb_model2_predict = np.argmax(lgb_model2.predict(X_test), axis = 1) # 평가 데이터 예측, Softmax의 결과값 중 가장 큰 값의 Label로 예측\n",
    "model_evaluation(y_test, lgb_model2_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8dfebf89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '1': [391]\n",
      "Before OverSampling, counts of label '0': [227454] \n",
      "\n",
      "After OverSampling, counts of label '1': 136472\n",
      "After OverSampling, counts of label '0': 227454\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train == 1))) # y_train 중 레이블 값이 1인 데이터의 개수\n",
    "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train == 0))) # y_train 중 레이블 값이 0 인 데이터의 개수\n",
    "\n",
    "sm2 = SMOTE(random_state = 42, sampling_strategy=0.6) # SMOTE 알고리즘, 비율 60%\n",
    "X_train_res2, y_train_res2 = sm2.fit_resample(X_train, y_train.ravel()) # Over Sampling 진행\n",
    "\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res2==1)))\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res2==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7d5e6c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018074 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 363926, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score -0.470003\n",
      "[LightGBM] [Info] Start training from score -0.980831\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9966819985253327\n",
      "Precision :  0.3333333333333333\n",
      "Recall :  0.8712871287128713\n",
      "F1-Score :  0.4821917808219177\n"
     ]
    }
   ],
   "source": [
    "lgb_dtrain3 = lgb.Dataset(data = pd.DataFrame(X_train_res2), label = pd.DataFrame(y_train_res2)) # 학습 데이터를 LightGBM 모델에 맞게 변환\n",
    "lgb_param3 = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'objective': 'multiclass', # 목적 함수\n",
    "            'num_class': len(set(pd.DataFrame(y_train_res2))) + 1} # 파라미터 추가, Label must be in [0, num_class) -> num_class보다 1 커야한다.\n",
    "lgb_model3 = lgb.train(params = lgb_param3, train_set = lgb_dtrain3) # 학습 진행\n",
    "lgb_model3_predict = np.argmax(lgb_model3.predict(X_test), axis = 1) # 평가 데이터 예측, Softmax의 결과값 중 가장 큰 값의 Label로 예측\n",
    "model_evaluation(y_test, lgb_model3_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe48a34",
   "metadata": {},
   "source": [
    "30% 의 비율로 SMOTE 진행한 경우 가장 적당했다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4306071",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BLSM (Borderline SMOTE)\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "sm4 = BorderlineSMOTE(random_state = 42, sampling_strategy = 0.6) # BLSM 알고리즘 적용\n",
    "X_train_res4, y_train_res4 = sm4.fit_resample(X_train, y_train.ravel()) # Over Sampling 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9db6628a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.016092 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 363926, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score -0.470003\n",
      "[LightGBM] [Info] Start training from score -0.980831\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9988939995084443\n",
      "Precision :  0.6461538461538462\n",
      "Recall :  0.8316831683168316\n",
      "F1-Score :  0.7272727272727273\n"
     ]
    }
   ],
   "source": [
    "lgb_dtrain5 = lgb.Dataset(data = pd.DataFrame(X_train_res4), label = pd.DataFrame(y_train_res4)) # 학습 데이터를 LightGBM 모델에 맞게 변환\n",
    "lgb_param5 = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'objective': 'multiclass', # 목적 함수\n",
    "            'num_class': len(set(pd.DataFrame(y_train_res4))) + 1} # 파라미터 추가, Label must be in [0, num_class) -> num_class보다 1 커야한다.\n",
    "lgb_model5 = lgb.train(params = lgb_param5, train_set = lgb_dtrain5) # 학습 진행\n",
    "lgb_model5_predict = np.argmax(lgb_model5.predict(X_test), axis = 1) # 평가 데이터 예측, Softmax의 결과값 중 가장 큰 값의 Label로 예측\n",
    "model_evaluation(y_test, lgb_model5_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b192f2c",
   "metadata": {},
   "source": [
    "BLSM 보다 기본 SMOTE 의 성능이 더 좋았다. 이후 여러 모델에 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "22743179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.991871774165233\n",
      "Precision :  0.16605166051660517\n",
      "Recall :  0.8910891089108911\n",
      "F1-Score :  0.2799377916018662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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": [
    "# BLSM을 이용해서 Oversampling 한 학습 데이터 셋 : X_train_res2, y_train_res2\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_model = LogisticRegression(C = 1e+10) \n",
    "# sklearn 의 Logistic Regression은 기본적으로 Ridge 정규화가 포함되어 있기 때문에, \n",
    "# 정규화 텀을 억제하는 C를 크게 적용한다 (C:Inverse of regularization strength)\n",
    "lr_model.fit(X_train_res, y_train_res) # 로지스틱 회귀 모형 학습\n",
    "lr_predict = lr_model.predict(X_test) # 학습 결과를 바탕으로 검증 데이터를 예측\n",
    "model_evaluation(y_test, lr_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b39ec10c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9916435518415786\n",
      "Precision :  0.16216216216216217\n",
      "Recall :  0.8910891089108911\n",
      "F1-Score :  0.27439024390243905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alstj\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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": [
    "# 릿지 로지스틱 회귀모형 학습\n",
    "ridge_model = LogisticRegression(penalty = 'l2') # Default = LogisticRegression()\n",
    "ridge_model.fit(X_train_res, y_train_res) # 릿지 로지스틱 회귀 모형 학습\n",
    "ridge_predict = ridge_model.predict(X_test) # 학습 결과를 바탕으로 검증 데이터를 예측\n",
    "model_evaluation(y_test, ridge_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc4d7ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9992626663389628\n",
      "Precision :  0.7565217391304347\n",
      "Recall :  0.8613861386138614\n",
      "F1-Score :  0.8055555555555555\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random_forest_model = RandomForestClassifier(n_estimators = 50, # 50번 추정\n",
    "                                             max_depth = 10, # 트리 최대 깊이 10\n",
    "                                             random_state = 42) # 시드값 고정\n",
    "rf_model = random_forest_model.fit(X_train_res, y_train_res) # 학습 진행\n",
    "rf_predict = rf_model.predict(X_test) # 평가 데이터 예측\n",
    "model_evaluation(y_test, rf_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "beeb8b4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "49:\tlearn: 0.9851500\ttotal: 15.4s\tremaining: 0us\n",
      "Model Evaluation with Label : 1\n",
      "Accuracy :  0.9979459990871107\n",
      "Precision :  0.4583333333333333\n",
      "Recall :  0.8712871287128713\n",
      "F1-Score :  0.6006825938566552\n"
     ]
    }
   ],
   "source": [
    "import catboost as cb\n",
    "cb_dtrain = cb.Pool(data = X_train_res, label = y_train_res) # 학습 데이터를 Catboost 모델에 맞게 변환\n",
    "cb_param = {'max_depth': 10, # 트리 깊이\n",
    "            'learning_rate': 0.01, # Step Size\n",
    "            'n_estimators': 50, # Number of trees, 트리 생성 개수\n",
    "            'eval_metric': 'Accuracy', # 평가 척도\n",
    "            'loss_function': 'MultiClass'} # 손실 함수, 목적 함수\n",
    "cb_model = cb.train(pool = cb_dtrain, params = cb_param) # 학습 진행\n",
    "cb_model_predict = np.argmax(cb_model.predict(X_test), axis = 1) # 평가 데이터 예측, Softmax의 결과값 중 가장 큰 값의 Label로 예측, 인덱스의 순서를 맞추기 위해 +1\n",
    "model_evaluation(y_test, cb_model_predict) # 모델 분류 평가 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d66746b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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