week2_logistic_regression_assignment1_최혜빈.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression 과제 -Classificatino Metrics / 최혜빈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#데이터 불러오기\n",
    "data = pd.read_csv('sampled_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>136330</td>\n",
       "      <td>2.108286</td>\n",
       "      <td>-0.020359</td>\n",
       "      <td>-2.234273</td>\n",
       "      <td>-0.124080</td>\n",
       "      <td>0.559843</td>\n",
       "      <td>-1.315913</td>\n",
       "      <td>0.631887</td>\n",
       "      <td>-0.385490</td>\n",
       "      <td>0.150628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.162940</td>\n",
       "      <td>0.519705</td>\n",
       "      <td>-0.091751</td>\n",
       "      <td>-0.379542</td>\n",
       "      <td>0.418822</td>\n",
       "      <td>0.248646</td>\n",
       "      <td>-0.098857</td>\n",
       "      <td>-0.094773</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>116819</td>\n",
       "      <td>2.080143</td>\n",
       "      <td>-0.075408</td>\n",
       "      <td>-1.359381</td>\n",
       "      <td>0.261263</td>\n",
       "      <td>0.232201</td>\n",
       "      <td>-0.797886</td>\n",
       "      <td>0.233487</td>\n",
       "      <td>-0.330165</td>\n",
       "      <td>0.493154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.317765</td>\n",
       "      <td>-0.784150</td>\n",
       "      <td>0.233078</td>\n",
       "      <td>-0.636937</td>\n",
       "      <td>-0.191137</td>\n",
       "      <td>0.234986</td>\n",
       "      <td>-0.070648</td>\n",
       "      <td>-0.062794</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>27917</td>\n",
       "      <td>1.152650</td>\n",
       "      <td>0.204938</td>\n",
       "      <td>0.441832</td>\n",
       "      <td>1.211595</td>\n",
       "      <td>-0.254895</td>\n",
       "      <td>-0.383380</td>\n",
       "      <td>-0.004286</td>\n",
       "      <td>-0.019590</td>\n",
       "      <td>0.085094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066674</td>\n",
       "      <td>0.209828</td>\n",
       "      <td>-0.054351</td>\n",
       "      <td>0.058103</td>\n",
       "      <td>0.515330</td>\n",
       "      <td>-0.299402</td>\n",
       "      <td>0.032551</td>\n",
       "      <td>0.022865</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>97443</td>\n",
       "      <td>-0.389692</td>\n",
       "      <td>0.410148</td>\n",
       "      <td>0.616187</td>\n",
       "      <td>-0.686644</td>\n",
       "      <td>1.040312</td>\n",
       "      <td>-0.322199</td>\n",
       "      <td>0.523625</td>\n",
       "      <td>-0.037453</td>\n",
       "      <td>1.319237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116692</td>\n",
       "      <td>0.685267</td>\n",
       "      <td>-0.182303</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>-0.242738</td>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.115370</td>\n",
       "      <td>0.176041</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17920</td>\n",
       "      <td>-1.306986</td>\n",
       "      <td>0.183306</td>\n",
       "      <td>3.164803</td>\n",
       "      <td>0.810135</td>\n",
       "      <td>1.529576</td>\n",
       "      <td>2.388322</td>\n",
       "      <td>0.551279</td>\n",
       "      <td>-0.054401</td>\n",
       "      <td>1.675481</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.203993</td>\n",
       "      <td>0.556523</td>\n",
       "      <td>0.146513</td>\n",
       "      <td>-1.128693</td>\n",
       "      <td>-0.633623</td>\n",
       "      <td>-0.454903</td>\n",
       "      <td>-0.826329</td>\n",
       "      <td>-0.826186</td>\n",
       "      <td>4.95</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  \\\n",
       "0  136330  2.108286 -0.020359 -2.234273 -0.124080  0.559843 -1.315913   \n",
       "1  116819  2.080143 -0.075408 -1.359381  0.261263  0.232201 -0.797886   \n",
       "2   27917  1.152650  0.204938  0.441832  1.211595 -0.254895 -0.383380   \n",
       "3   97443 -0.389692  0.410148  0.616187 -0.686644  1.040312 -0.322199   \n",
       "4   17920 -1.306986  0.183306  3.164803  0.810135  1.529576  2.388322   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0  0.631887 -0.385490  0.150628  ...  0.162940  0.519705 -0.091751 -0.379542   \n",
       "1  0.233487 -0.330165  0.493154  ... -0.317765 -0.784150  0.233078 -0.636937   \n",
       "2 -0.004286 -0.019590  0.085094  ...  0.066674  0.209828 -0.054351  0.058103   \n",
       "3  0.523625 -0.037453  1.319237  ...  0.116692  0.685267 -0.182303  0.767857   \n",
       "4  0.551279 -0.054401  1.675481  ... -0.203993  0.556523  0.146513 -1.128693   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.418822  0.248646 -0.098857 -0.094773    7.70      0  \n",
       "1 -0.191137  0.234986 -0.070648 -0.062794   17.99      0  \n",
       "2  0.515330 -0.299402  0.032551  0.022865   21.00      0  \n",
       "3 -0.242738  0.009678  0.115370  0.176041    6.60      0  \n",
       "4 -0.633623 -0.454903 -0.826329 -0.826186    4.95      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "#scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.875992</td>\n",
       "      <td>1.102273</td>\n",
       "      <td>-0.013837</td>\n",
       "      <td>-1.519427</td>\n",
       "      <td>-0.092494</td>\n",
       "      <td>0.415984</td>\n",
       "      <td>-0.996862</td>\n",
       "      <td>0.529260</td>\n",
       "      <td>-0.325542</td>\n",
       "      <td>0.140034</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.389289</td>\n",
       "      <td>0.217877</td>\n",
       "      <td>0.717147</td>\n",
       "      <td>-0.171055</td>\n",
       "      <td>-0.622575</td>\n",
       "      <td>0.811479</td>\n",
       "      <td>0.520065</td>\n",
       "      <td>-0.244661</td>\n",
       "      <td>-0.298899</td>\n",
       "      <td>-0.341627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.465992</td>\n",
       "      <td>1.087628</td>\n",
       "      <td>-0.047477</td>\n",
       "      <td>-0.930583</td>\n",
       "      <td>0.178984</td>\n",
       "      <td>0.173824</td>\n",
       "      <td>-0.604853</td>\n",
       "      <td>0.194623</td>\n",
       "      <td>-0.278678</td>\n",
       "      <td>0.453561</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174391</td>\n",
       "      <td>-0.431114</td>\n",
       "      <td>-1.088996</td>\n",
       "      <td>0.399380</td>\n",
       "      <td>-1.044356</td>\n",
       "      <td>-0.370659</td>\n",
       "      <td>0.491879</td>\n",
       "      <td>-0.173318</td>\n",
       "      <td>-0.193811</td>\n",
       "      <td>-0.297983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.402175</td>\n",
       "      <td>0.604964</td>\n",
       "      <td>0.123842</td>\n",
       "      <td>0.281721</td>\n",
       "      <td>0.848501</td>\n",
       "      <td>-0.186188</td>\n",
       "      <td>-0.291182</td>\n",
       "      <td>-0.005096</td>\n",
       "      <td>-0.015599</td>\n",
       "      <td>0.080048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225029</td>\n",
       "      <td>0.087910</td>\n",
       "      <td>0.287896</td>\n",
       "      <td>-0.105378</td>\n",
       "      <td>0.094572</td>\n",
       "      <td>0.998518</td>\n",
       "      <td>-0.610760</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>-0.285216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058829</td>\n",
       "      <td>-0.197667</td>\n",
       "      <td>0.249246</td>\n",
       "      <td>0.399070</td>\n",
       "      <td>-0.488826</td>\n",
       "      <td>0.771099</td>\n",
       "      <td>-0.244885</td>\n",
       "      <td>0.438324</td>\n",
       "      <td>-0.030730</td>\n",
       "      <td>1.209704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125580</td>\n",
       "      <td>0.155438</td>\n",
       "      <td>0.946489</td>\n",
       "      <td>-0.330075</td>\n",
       "      <td>1.257612</td>\n",
       "      <td>-0.470666</td>\n",
       "      <td>0.026986</td>\n",
       "      <td>0.297138</td>\n",
       "      <td>0.591042</td>\n",
       "      <td>-0.346292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.612250</td>\n",
       "      <td>-0.675024</td>\n",
       "      <td>0.110623</td>\n",
       "      <td>2.114412</td>\n",
       "      <td>0.565669</td>\n",
       "      <td>1.132714</td>\n",
       "      <td>1.806258</td>\n",
       "      <td>0.461553</td>\n",
       "      <td>-0.045086</td>\n",
       "      <td>1.535788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.682098</td>\n",
       "      <td>-0.277512</td>\n",
       "      <td>0.768150</td>\n",
       "      <td>0.247363</td>\n",
       "      <td>-1.850173</td>\n",
       "      <td>-1.228224</td>\n",
       "      <td>-0.931617</td>\n",
       "      <td>-2.084501</td>\n",
       "      <td>-2.702442</td>\n",
       "      <td>-0.353291</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0  0.875992  1.102273 -0.013837 -1.519427 -0.092494  0.415984 -0.996862   \n",
       "1  0.465992  1.087628 -0.047477 -0.930583  0.178984  0.173824 -0.604853   \n",
       "2 -1.402175  0.604964  0.123842  0.281721  0.848501 -0.186188 -0.291182   \n",
       "3  0.058829 -0.197667  0.249246  0.399070 -0.488826  0.771099 -0.244885   \n",
       "4 -1.612250 -0.675024  0.110623  2.114412  0.565669  1.132714  1.806258   \n",
       "\n",
       "         V7        V8        V9  ...       V20       V21       V22       V23  \\\n",
       "0  0.529260 -0.325542  0.140034  ... -0.389289  0.217877  0.717147 -0.171055   \n",
       "1  0.194623 -0.278678  0.453561  ... -0.174391 -0.431114 -1.088996  0.399380   \n",
       "2 -0.005096 -0.015599  0.080048  ... -0.225029  0.087910  0.287896 -0.105378   \n",
       "3  0.438324 -0.030730  1.209704  ... -0.125580  0.155438  0.946489 -0.330075   \n",
       "4  0.461553 -0.045086  1.535788  ... -0.682098 -0.277512  0.768150  0.247363   \n",
       "\n",
       "        V24       V25       V26       V27       V28    Amount  \n",
       "0 -0.622575  0.811479  0.520065 -0.244661 -0.298899 -0.341627  \n",
       "1 -1.044356 -0.370659  0.491879 -0.173318 -0.193811 -0.297983  \n",
       "2  0.094572  0.998518 -0.610760  0.087681  0.087681 -0.285216  \n",
       "3  1.257612 -0.470666  0.026986  0.297138  0.591042 -0.346292  \n",
       "4 -1.850173 -1.228224 -0.931617 -2.084501 -2.702442 -0.353291  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_new = pd.DataFrame(scaler.fit_transform(df))\n",
    "#scaling version 따로 만들었습니다\n",
    "df_x=df.drop(['Class'],axis=1)\n",
    "#class변수를 제외하고 scaling하였고 scaling 후에 다시 class 변수를 추가시켰습니다\n",
    "fitted = scaler.fit_transform(df_x)\n",
    "new_df = pd.DataFrame(fitted, columns=df_x.columns, index=list(df_x.index.values))\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Time</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",
       "      <td>0</td>\n",
       "      <td>0.875992</td>\n",
       "      <td>1.102273</td>\n",
       "      <td>-0.013837</td>\n",
       "      <td>-1.519427</td>\n",
       "      <td>-0.092494</td>\n",
       "      <td>0.415984</td>\n",
       "      <td>-0.996862</td>\n",
       "      <td>0.529260</td>\n",
       "      <td>-0.325542</td>\n",
       "      <td>0.140034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.217877</td>\n",
       "      <td>0.717147</td>\n",
       "      <td>-0.171055</td>\n",
       "      <td>-0.622575</td>\n",
       "      <td>0.811479</td>\n",
       "      <td>0.520065</td>\n",
       "      <td>-0.244661</td>\n",
       "      <td>-0.298899</td>\n",
       "      <td>-0.341627</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.465992</td>\n",
       "      <td>1.087628</td>\n",
       "      <td>-0.047477</td>\n",
       "      <td>-0.930583</td>\n",
       "      <td>0.178984</td>\n",
       "      <td>0.173824</td>\n",
       "      <td>-0.604853</td>\n",
       "      <td>0.194623</td>\n",
       "      <td>-0.278678</td>\n",
       "      <td>0.453561</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.431114</td>\n",
       "      <td>-1.088996</td>\n",
       "      <td>0.399380</td>\n",
       "      <td>-1.044356</td>\n",
       "      <td>-0.370659</td>\n",
       "      <td>0.491879</td>\n",
       "      <td>-0.173318</td>\n",
       "      <td>-0.193811</td>\n",
       "      <td>-0.297983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.402175</td>\n",
       "      <td>0.604964</td>\n",
       "      <td>0.123842</td>\n",
       "      <td>0.281721</td>\n",
       "      <td>0.848501</td>\n",
       "      <td>-0.186188</td>\n",
       "      <td>-0.291182</td>\n",
       "      <td>-0.005096</td>\n",
       "      <td>-0.015599</td>\n",
       "      <td>0.080048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087910</td>\n",
       "      <td>0.287896</td>\n",
       "      <td>-0.105378</td>\n",
       "      <td>0.094572</td>\n",
       "      <td>0.998518</td>\n",
       "      <td>-0.610760</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>-0.285216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058829</td>\n",
       "      <td>-0.197667</td>\n",
       "      <td>0.249246</td>\n",
       "      <td>0.399070</td>\n",
       "      <td>-0.488826</td>\n",
       "      <td>0.771099</td>\n",
       "      <td>-0.244885</td>\n",
       "      <td>0.438324</td>\n",
       "      <td>-0.030730</td>\n",
       "      <td>1.209704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155438</td>\n",
       "      <td>0.946489</td>\n",
       "      <td>-0.330075</td>\n",
       "      <td>1.257612</td>\n",
       "      <td>-0.470666</td>\n",
       "      <td>0.026986</td>\n",
       "      <td>0.297138</td>\n",
       "      <td>0.591042</td>\n",
       "      <td>-0.346292</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.612250</td>\n",
       "      <td>-0.675024</td>\n",
       "      <td>0.110623</td>\n",
       "      <td>2.114412</td>\n",
       "      <td>0.565669</td>\n",
       "      <td>1.132714</td>\n",
       "      <td>1.806258</td>\n",
       "      <td>0.461553</td>\n",
       "      <td>-0.045086</td>\n",
       "      <td>1.535788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.277512</td>\n",
       "      <td>0.768150</td>\n",
       "      <td>0.247363</td>\n",
       "      <td>-1.850173</td>\n",
       "      <td>-1.228224</td>\n",
       "      <td>-0.931617</td>\n",
       "      <td>-2.084501</td>\n",
       "      <td>-2.702442</td>\n",
       "      <td>-0.353291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28475</td>\n",
       "      <td>-1.269851</td>\n",
       "      <td>-0.303659</td>\n",
       "      <td>0.045113</td>\n",
       "      <td>1.652929</td>\n",
       "      <td>0.549944</td>\n",
       "      <td>-0.343748</td>\n",
       "      <td>1.176108</td>\n",
       "      <td>-0.300411</td>\n",
       "      <td>0.632508</td>\n",
       "      <td>0.600344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152658</td>\n",
       "      <td>0.798688</td>\n",
       "      <td>-0.012156</td>\n",
       "      <td>-0.489836</td>\n",
       "      <td>-0.263086</td>\n",
       "      <td>-0.676960</td>\n",
       "      <td>0.320643</td>\n",
       "      <td>0.103960</td>\n",
       "      <td>-0.148559</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28476</td>\n",
       "      <td>0.717842</td>\n",
       "      <td>1.005432</td>\n",
       "      <td>-0.160124</td>\n",
       "      <td>-1.274450</td>\n",
       "      <td>0.223870</td>\n",
       "      <td>0.311822</td>\n",
       "      <td>-0.612151</td>\n",
       "      <td>0.464676</td>\n",
       "      <td>-0.280385</td>\n",
       "      <td>0.046481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111694</td>\n",
       "      <td>0.395568</td>\n",
       "      <td>-0.135871</td>\n",
       "      <td>-0.513670</td>\n",
       "      <td>0.430712</td>\n",
       "      <td>1.253071</td>\n",
       "      <td>-0.281188</td>\n",
       "      <td>-0.248409</td>\n",
       "      <td>-0.038367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28477</td>\n",
       "      <td>-0.428102</td>\n",
       "      <td>-1.771869</td>\n",
       "      <td>-0.421366</td>\n",
       "      <td>0.944253</td>\n",
       "      <td>-1.515500</td>\n",
       "      <td>0.294278</td>\n",
       "      <td>-0.779094</td>\n",
       "      <td>-2.189361</td>\n",
       "      <td>-2.011887</td>\n",
       "      <td>1.684925</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.046568</td>\n",
       "      <td>0.404609</td>\n",
       "      <td>-4.579363</td>\n",
       "      <td>0.428147</td>\n",
       "      <td>-0.572160</td>\n",
       "      <td>-0.415799</td>\n",
       "      <td>0.480802</td>\n",
       "      <td>-0.906453</td>\n",
       "      <td>0.375804</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28478</td>\n",
       "      <td>0.906084</td>\n",
       "      <td>0.670550</td>\n",
       "      <td>-1.069014</td>\n",
       "      <td>-0.594471</td>\n",
       "      <td>0.304593</td>\n",
       "      <td>-0.827940</td>\n",
       "      <td>-0.147452</td>\n",
       "      <td>-0.270542</td>\n",
       "      <td>0.052176</td>\n",
       "      <td>0.971411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.552837</td>\n",
       "      <td>0.515797</td>\n",
       "      <td>-0.295245</td>\n",
       "      <td>-0.511557</td>\n",
       "      <td>-1.032926</td>\n",
       "      <td>1.283600</td>\n",
       "      <td>-0.299928</td>\n",
       "      <td>0.007600</td>\n",
       "      <td>1.216236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28479</td>\n",
       "      <td>-1.909931</td>\n",
       "      <td>0.720580</td>\n",
       "      <td>-0.220569</td>\n",
       "      <td>0.456264</td>\n",
       "      <td>-0.322366</td>\n",
       "      <td>-0.768278</td>\n",
       "      <td>-0.751067</td>\n",
       "      <td>-0.503880</td>\n",
       "      <td>-0.249034</td>\n",
       "      <td>0.433548</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.315702</td>\n",
       "      <td>-0.796420</td>\n",
       "      <td>0.213515</td>\n",
       "      <td>0.490653</td>\n",
       "      <td>0.403848</td>\n",
       "      <td>-1.070219</td>\n",
       "      <td>-0.041204</td>\n",
       "      <td>0.077816</td>\n",
       "      <td>-0.259344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28480 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0      0.875992  1.102273 -0.013837 -1.519427 -0.092494  0.415984 -0.996862   \n",
       "1      0.465992  1.087628 -0.047477 -0.930583  0.178984  0.173824 -0.604853   \n",
       "2     -1.402175  0.604964  0.123842  0.281721  0.848501 -0.186188 -0.291182   \n",
       "3      0.058829 -0.197667  0.249246  0.399070 -0.488826  0.771099 -0.244885   \n",
       "4     -1.612250 -0.675024  0.110623  2.114412  0.565669  1.132714  1.806258   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "28475 -1.269851 -0.303659  0.045113  1.652929  0.549944 -0.343748  1.176108   \n",
       "28476  0.717842  1.005432 -0.160124 -1.274450  0.223870  0.311822 -0.612151   \n",
       "28477 -0.428102 -1.771869 -0.421366  0.944253 -1.515500  0.294278 -0.779094   \n",
       "28478  0.906084  0.670550 -1.069014 -0.594471  0.304593 -0.827940 -0.147452   \n",
       "28479 -1.909931  0.720580 -0.220569  0.456264 -0.322366 -0.768278 -0.751067   \n",
       "\n",
       "             V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0      0.529260 -0.325542  0.140034  ...  0.217877  0.717147 -0.171055   \n",
       "1      0.194623 -0.278678  0.453561  ... -0.431114 -1.088996  0.399380   \n",
       "2     -0.005096 -0.015599  0.080048  ...  0.087910  0.287896 -0.105378   \n",
       "3      0.438324 -0.030730  1.209704  ...  0.155438  0.946489 -0.330075   \n",
       "4      0.461553 -0.045086  1.535788  ... -0.277512  0.768150  0.247363   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "28475 -0.300411  0.632508  0.600344  ...  0.152658  0.798688 -0.012156   \n",
       "28476  0.464676 -0.280385  0.046481  ...  0.111694  0.395568 -0.135871   \n",
       "28477 -2.189361 -2.011887  1.684925  ... -2.046568  0.404609 -4.579363   \n",
       "28478 -0.270542  0.052176  0.971411  ...  0.552837  0.515797 -0.295245   \n",
       "28479 -0.503880 -0.249034  0.433548  ... -0.315702 -0.796420  0.213515   \n",
       "\n",
       "            V24       V25       V26       V27       V28    Amount  Class  \n",
       "0     -0.622575  0.811479  0.520065 -0.244661 -0.298899 -0.341627      0  \n",
       "1     -1.044356 -0.370659  0.491879 -0.173318 -0.193811 -0.297983      0  \n",
       "2      0.094572  0.998518 -0.610760  0.087681  0.087681 -0.285216      0  \n",
       "3      1.257612 -0.470666  0.026986  0.297138  0.591042 -0.346292      0  \n",
       "4     -1.850173 -1.228224 -0.931617 -2.084501 -2.702442 -0.353291      0  \n",
       "...         ...       ...       ...       ...       ...       ...    ...  \n",
       "28475 -0.489836 -0.263086 -0.676960  0.320643  0.103960 -0.148559      0  \n",
       "28476 -0.513670  0.430712  1.253071 -0.281188 -0.248409 -0.038367      0  \n",
       "28477  0.428147 -0.572160 -0.415799  0.480802 -0.906453  0.375804      0  \n",
       "28478 -0.511557 -1.032926  1.283600 -0.299928  0.007600  1.216236      0  \n",
       "28479  0.490653  0.403848 -1.070219 -0.041204  0.077816 -0.259344      0  \n",
       "\n",
       "[28480 rows x 31 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Class']=df['Class']\n",
    "\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>136330</td>\n",
       "      <td>2.108286</td>\n",
       "      <td>-0.020359</td>\n",
       "      <td>-2.234273</td>\n",
       "      <td>-0.124080</td>\n",
       "      <td>0.559843</td>\n",
       "      <td>-1.315913</td>\n",
       "      <td>0.631887</td>\n",
       "      <td>-0.385490</td>\n",
       "      <td>0.150628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.162940</td>\n",
       "      <td>0.519705</td>\n",
       "      <td>-0.091751</td>\n",
       "      <td>-0.379542</td>\n",
       "      <td>0.418822</td>\n",
       "      <td>0.248646</td>\n",
       "      <td>-0.098857</td>\n",
       "      <td>-0.094773</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>116819</td>\n",
       "      <td>2.080143</td>\n",
       "      <td>-0.075408</td>\n",
       "      <td>-1.359381</td>\n",
       "      <td>0.261263</td>\n",
       "      <td>0.232201</td>\n",
       "      <td>-0.797886</td>\n",
       "      <td>0.233487</td>\n",
       "      <td>-0.330165</td>\n",
       "      <td>0.493154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.317765</td>\n",
       "      <td>-0.784150</td>\n",
       "      <td>0.233078</td>\n",
       "      <td>-0.636937</td>\n",
       "      <td>-0.191137</td>\n",
       "      <td>0.234986</td>\n",
       "      <td>-0.070648</td>\n",
       "      <td>-0.062794</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>27917</td>\n",
       "      <td>1.152650</td>\n",
       "      <td>0.204938</td>\n",
       "      <td>0.441832</td>\n",
       "      <td>1.211595</td>\n",
       "      <td>-0.254895</td>\n",
       "      <td>-0.383380</td>\n",
       "      <td>-0.004286</td>\n",
       "      <td>-0.019590</td>\n",
       "      <td>0.085094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066674</td>\n",
       "      <td>0.209828</td>\n",
       "      <td>-0.054351</td>\n",
       "      <td>0.058103</td>\n",
       "      <td>0.515330</td>\n",
       "      <td>-0.299402</td>\n",
       "      <td>0.032551</td>\n",
       "      <td>0.022865</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>97443</td>\n",
       "      <td>-0.389692</td>\n",
       "      <td>0.410148</td>\n",
       "      <td>0.616187</td>\n",
       "      <td>-0.686644</td>\n",
       "      <td>1.040312</td>\n",
       "      <td>-0.322199</td>\n",
       "      <td>0.523625</td>\n",
       "      <td>-0.037453</td>\n",
       "      <td>1.319237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116692</td>\n",
       "      <td>0.685267</td>\n",
       "      <td>-0.182303</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>-0.242738</td>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.115370</td>\n",
       "      <td>0.176041</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17920</td>\n",
       "      <td>-1.306986</td>\n",
       "      <td>0.183306</td>\n",
       "      <td>3.164803</td>\n",
       "      <td>0.810135</td>\n",
       "      <td>1.529576</td>\n",
       "      <td>2.388322</td>\n",
       "      <td>0.551279</td>\n",
       "      <td>-0.054401</td>\n",
       "      <td>1.675481</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.203993</td>\n",
       "      <td>0.556523</td>\n",
       "      <td>0.146513</td>\n",
       "      <td>-1.128693</td>\n",
       "      <td>-0.633623</td>\n",
       "      <td>-0.454903</td>\n",
       "      <td>-0.826329</td>\n",
       "      <td>-0.826186</td>\n",
       "      <td>4.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28475</td>\n",
       "      <td>34214</td>\n",
       "      <td>-0.593368</td>\n",
       "      <td>0.076106</td>\n",
       "      <td>2.479141</td>\n",
       "      <td>0.787814</td>\n",
       "      <td>-0.468074</td>\n",
       "      <td>1.555597</td>\n",
       "      <td>-0.355871</td>\n",
       "      <td>0.745527</td>\n",
       "      <td>0.653514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114633</td>\n",
       "      <td>0.578569</td>\n",
       "      <td>-0.001268</td>\n",
       "      <td>-0.298537</td>\n",
       "      <td>-0.135632</td>\n",
       "      <td>-0.331485</td>\n",
       "      <td>0.124664</td>\n",
       "      <td>0.027819</td>\n",
       "      <td>53.22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28476</td>\n",
       "      <td>128804</td>\n",
       "      <td>1.922195</td>\n",
       "      <td>-0.259743</td>\n",
       "      <td>-1.870291</td>\n",
       "      <td>0.324977</td>\n",
       "      <td>0.418911</td>\n",
       "      <td>-0.807530</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>-0.332180</td>\n",
       "      <td>0.048422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084291</td>\n",
       "      <td>0.287557</td>\n",
       "      <td>-0.071716</td>\n",
       "      <td>-0.313082</td>\n",
       "      <td>0.222354</td>\n",
       "      <td>0.603893</td>\n",
       "      <td>-0.113299</td>\n",
       "      <td>-0.079409</td>\n",
       "      <td>79.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28477</td>\n",
       "      <td>74271</td>\n",
       "      <td>-3.414694</td>\n",
       "      <td>-0.687238</td>\n",
       "      <td>1.426208</td>\n",
       "      <td>-2.143933</td>\n",
       "      <td>0.395175</td>\n",
       "      <td>-1.028140</td>\n",
       "      <td>-2.604747</td>\n",
       "      <td>-2.376290</td>\n",
       "      <td>1.838413</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.514325</td>\n",
       "      <td>0.294084</td>\n",
       "      <td>-2.602020</td>\n",
       "      <td>0.261669</td>\n",
       "      <td>-0.295107</td>\n",
       "      <td>-0.204915</td>\n",
       "      <td>0.187991</td>\n",
       "      <td>-0.279656</td>\n",
       "      <td>176.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28478</td>\n",
       "      <td>137762</td>\n",
       "      <td>1.278683</td>\n",
       "      <td>-1.747044</td>\n",
       "      <td>-0.859994</td>\n",
       "      <td>0.439557</td>\n",
       "      <td>-1.123184</td>\n",
       "      <td>-0.193445</td>\n",
       "      <td>-0.320310</td>\n",
       "      <td>0.060421</td>\n",
       "      <td>1.058903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.411044</td>\n",
       "      <td>0.374350</td>\n",
       "      <td>-0.162470</td>\n",
       "      <td>-0.311792</td>\n",
       "      <td>-0.532853</td>\n",
       "      <td>0.618689</td>\n",
       "      <td>-0.120710</td>\n",
       "      <td>-0.001504</td>\n",
       "      <td>375.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28479</td>\n",
       "      <td>3754</td>\n",
       "      <td>1.374820</td>\n",
       "      <td>-0.358655</td>\n",
       "      <td>0.701164</td>\n",
       "      <td>-0.450366</td>\n",
       "      <td>-1.042462</td>\n",
       "      <td>-0.991103</td>\n",
       "      <td>-0.598109</td>\n",
       "      <td>-0.295170</td>\n",
       "      <td>0.471290</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.232280</td>\n",
       "      <td>-0.572940</td>\n",
       "      <td>0.127239</td>\n",
       "      <td>0.299814</td>\n",
       "      <td>0.208493</td>\n",
       "      <td>-0.522076</td>\n",
       "      <td>-0.018410</td>\n",
       "      <td>0.019863</td>\n",
       "      <td>27.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28480 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0      136330  2.108286 -0.020359 -2.234273 -0.124080  0.559843 -1.315913   \n",
       "1      116819  2.080143 -0.075408 -1.359381  0.261263  0.232201 -0.797886   \n",
       "2       27917  1.152650  0.204938  0.441832  1.211595 -0.254895 -0.383380   \n",
       "3       97443 -0.389692  0.410148  0.616187 -0.686644  1.040312 -0.322199   \n",
       "4       17920 -1.306986  0.183306  3.164803  0.810135  1.529576  2.388322   \n",
       "...       ...       ...       ...       ...       ...       ...       ...   \n",
       "28475   34214 -0.593368  0.076106  2.479141  0.787814 -0.468074  1.555597   \n",
       "28476  128804  1.922195 -0.259743 -1.870291  0.324977  0.418911 -0.807530   \n",
       "28477   74271 -3.414694 -0.687238  1.426208 -2.143933  0.395175 -1.028140   \n",
       "28478  137762  1.278683 -1.747044 -0.859994  0.439557 -1.123184 -0.193445   \n",
       "28479    3754  1.374820 -0.358655  0.701164 -0.450366 -1.042462 -0.991103   \n",
       "\n",
       "             V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0      0.631887 -0.385490  0.150628  ...  0.162940  0.519705 -0.091751   \n",
       "1      0.233487 -0.330165  0.493154  ... -0.317765 -0.784150  0.233078   \n",
       "2     -0.004286 -0.019590  0.085094  ...  0.066674  0.209828 -0.054351   \n",
       "3      0.523625 -0.037453  1.319237  ...  0.116692  0.685267 -0.182303   \n",
       "4      0.551279 -0.054401  1.675481  ... -0.203993  0.556523  0.146513   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "28475 -0.355871  0.745527  0.653514  ...  0.114633  0.578569 -0.001268   \n",
       "28476  0.554997 -0.332180  0.048422  ...  0.084291  0.287557 -0.071716   \n",
       "28477 -2.604747 -2.376290  1.838413  ... -1.514325  0.294084 -2.602020   \n",
       "28478 -0.320310  0.060421  1.058903  ...  0.411044  0.374350 -0.162470   \n",
       "28479 -0.598109 -0.295170  0.471290  ... -0.232280 -0.572940  0.127239   \n",
       "\n",
       "            V24       V25       V26       V27       V28  Amount  Class  \n",
       "0     -0.379542  0.418822  0.248646 -0.098857 -0.094773    7.70      0  \n",
       "1     -0.636937 -0.191137  0.234986 -0.070648 -0.062794   17.99      0  \n",
       "2      0.058103  0.515330 -0.299402  0.032551  0.022865   21.00      0  \n",
       "3      0.767857 -0.242738  0.009678  0.115370  0.176041    6.60      0  \n",
       "4     -1.128693 -0.633623 -0.454903 -0.826329 -0.826186    4.95      0  \n",
       "...         ...       ...       ...       ...       ...     ...    ...  \n",
       "28475 -0.298537 -0.135632 -0.331485  0.124664  0.027819   53.22      0  \n",
       "28476 -0.313082  0.222354  0.603893 -0.113299 -0.079409   79.20      0  \n",
       "28477  0.261669 -0.295107 -0.204915  0.187991 -0.279656  176.85      0  \n",
       "28478 -0.311792 -0.532853  0.618689 -0.120710 -0.001504  375.00      0  \n",
       "28479  0.299814  0.208493 -0.522076 -0.018410  0.019863   27.10      0  \n",
       "\n",
       "[28480 rows x 31 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    28428\n",
       "1       52\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V21</th>\n",
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       "      <th>V23</th>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0.875992</td>\n",
       "      <td>1.102273</td>\n",
       "      <td>-0.013837</td>\n",
       "      <td>-1.519427</td>\n",
       "      <td>-0.092494</td>\n",
       "      <td>0.415984</td>\n",
       "      <td>-0.996862</td>\n",
       "      <td>0.529260</td>\n",
       "      <td>-0.325542</td>\n",
       "      <td>0.140034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.217877</td>\n",
       "      <td>0.717147</td>\n",
       "      <td>-0.171055</td>\n",
       "      <td>-0.622575</td>\n",
       "      <td>0.811479</td>\n",
       "      <td>0.520065</td>\n",
       "      <td>-0.244661</td>\n",
       "      <td>-0.298899</td>\n",
       "      <td>-0.341627</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.465992</td>\n",
       "      <td>1.087628</td>\n",
       "      <td>-0.047477</td>\n",
       "      <td>-0.930583</td>\n",
       "      <td>0.178984</td>\n",
       "      <td>0.173824</td>\n",
       "      <td>-0.604853</td>\n",
       "      <td>0.194623</td>\n",
       "      <td>-0.278678</td>\n",
       "      <td>0.453561</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.431114</td>\n",
       "      <td>-1.088996</td>\n",
       "      <td>0.399380</td>\n",
       "      <td>-1.044356</td>\n",
       "      <td>-0.370659</td>\n",
       "      <td>0.491879</td>\n",
       "      <td>-0.173318</td>\n",
       "      <td>-0.193811</td>\n",
       "      <td>-0.297983</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.402175</td>\n",
       "      <td>0.604964</td>\n",
       "      <td>0.123842</td>\n",
       "      <td>0.281721</td>\n",
       "      <td>0.848501</td>\n",
       "      <td>-0.186188</td>\n",
       "      <td>-0.291182</td>\n",
       "      <td>-0.005096</td>\n",
       "      <td>-0.015599</td>\n",
       "      <td>0.080048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087910</td>\n",
       "      <td>0.287896</td>\n",
       "      <td>-0.105378</td>\n",
       "      <td>0.094572</td>\n",
       "      <td>0.998518</td>\n",
       "      <td>-0.610760</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>-0.285216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.058829</td>\n",
       "      <td>-0.197667</td>\n",
       "      <td>0.249246</td>\n",
       "      <td>0.399070</td>\n",
       "      <td>-0.488826</td>\n",
       "      <td>0.771099</td>\n",
       "      <td>-0.244885</td>\n",
       "      <td>0.438324</td>\n",
       "      <td>-0.030730</td>\n",
       "      <td>1.209704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155438</td>\n",
       "      <td>0.946489</td>\n",
       "      <td>-0.330075</td>\n",
       "      <td>1.257612</td>\n",
       "      <td>-0.470666</td>\n",
       "      <td>0.026986</td>\n",
       "      <td>0.297138</td>\n",
       "      <td>0.591042</td>\n",
       "      <td>-0.346292</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.612250</td>\n",
       "      <td>-0.675024</td>\n",
       "      <td>0.110623</td>\n",
       "      <td>2.114412</td>\n",
       "      <td>0.565669</td>\n",
       "      <td>1.132714</td>\n",
       "      <td>1.806258</td>\n",
       "      <td>0.461553</td>\n",
       "      <td>-0.045086</td>\n",
       "      <td>1.535788</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.277512</td>\n",
       "      <td>0.768150</td>\n",
       "      <td>0.247363</td>\n",
       "      <td>-1.850173</td>\n",
       "      <td>-1.228224</td>\n",
       "      <td>-0.931617</td>\n",
       "      <td>-2.084501</td>\n",
       "      <td>-2.702442</td>\n",
       "      <td>-0.353291</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0  0.875992  1.102273 -0.013837 -1.519427 -0.092494  0.415984 -0.996862   \n",
       "1  0.465992  1.087628 -0.047477 -0.930583  0.178984  0.173824 -0.604853   \n",
       "2 -1.402175  0.604964  0.123842  0.281721  0.848501 -0.186188 -0.291182   \n",
       "3  0.058829 -0.197667  0.249246  0.399070 -0.488826  0.771099 -0.244885   \n",
       "4 -1.612250 -0.675024  0.110623  2.114412  0.565669  1.132714  1.806258   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0  0.529260 -0.325542  0.140034  ...  0.217877  0.717147 -0.171055 -0.622575   \n",
       "1  0.194623 -0.278678  0.453561  ... -0.431114 -1.088996  0.399380 -1.044356   \n",
       "2 -0.005096 -0.015599  0.080048  ...  0.087910  0.287896 -0.105378  0.094572   \n",
       "3  0.438324 -0.030730  1.209704  ...  0.155438  0.946489 -0.330075  1.257612   \n",
       "4  0.461553 -0.045086  1.535788  ... -0.277512  0.768150  0.247363 -1.850173   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.811479  0.520065 -0.244661 -0.298899 -0.341627      0  \n",
       "1 -0.370659  0.491879 -0.173318 -0.193811 -0.297983      0  \n",
       "2  0.998518 -0.610760  0.087681  0.087681 -0.285216      0  \n",
       "3 -0.470666  0.026986  0.297138  0.591042 -0.346292      0  \n",
       "4 -1.228224 -0.931617 -2.084501 -2.702442 -0.353291      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1번. Logistic Regression 진행하기\n",
    "(그냥 version과 scaling versino 둘 다 진행해보았습니다)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression #logisticregression은 sklearn 패키지를 사용하였습니다\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trainset과 testset을 분류하였고\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,:-1], df.iloc[:,-1],random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21360, 30), (7120, 30), (21360,), (7120,))"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train set, test set 구분 확인\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression() #로지스틱 회귀모델 사용\n",
    "classifier.fit(X_train,y_train) #trainset을 fit시키고"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#classfier을 통해 얻은 y 예측값을 구한다\n",
    "#logistic regression, classification이므로 0또는 1의 값으로 나온다\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99209106e-01, 7.90894246e-04],\n",
       "       [9.99738708e-01, 2.61292060e-04],\n",
       "       [9.99998580e-01, 1.42049928e-06],\n",
       "       ...,\n",
       "       [9.96341932e-01, 3.65806843e-03],\n",
       "       [9.99395800e-01, 6.04199595e-04],\n",
       "       [9.99578107e-01, 4.21893358e-04]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y 예측값이 1인 확률 역시 구해본다\n",
    "classifier.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998876404494382"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean accuracy 확인해보았습니다\n",
    "classifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "#똑같은 과정을 scaling한 데이터도 진행하였습니다\n",
    "#Scaled Version\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(new_df.iloc[:, :-1], new_df.iloc[:, -1], random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier2 = LogisticRegression()\n",
    "classifier2.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2 = classifier2.predict(X_test2) # class 예측\n",
    "y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998595505617978"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier2.score(X_test2, y_test2) # Scaling 후 성능 향상!\n",
    "#scaling 후 mean accuracy를 찍어보니 약간 향상되었음을 확인할 수 있었습니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2번. Confusion Matrix 찍어보기 → Mean Accuracy와 비교\n",
    "\n",
    "confusion matrix란 training을 통한 prediction 성능을 측정하기 위해\n",
    "예측 value와 실제 value를 비교하기 위한 표입니다\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f1-score은 recall과 precision을 이용한 조화평균을 이용한 score인데 이 두 값이 어느정도 균형을 이룰 때 높게 나온다\n",
    "\n",
    "recall은 input된 class에 대해 classifier가 어떤 class로 예측을 하는지에 대한 척도이고,\n",
    "precision은 예측한 값들 중에서 제대로 예측했는지에 대한 척도입니다\n",
    "\n",
    "잘 이해가 안되어서\n",
    "https://nittaku.tistory.com/295 이걸 참고하여 이해했습니다..!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7108,    3],\n",
       "       [   5,    4]], dtype=int64)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confusion matrix를 찍어보았습니다\n",
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998876404494382"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)\n",
    "#앞서 구한 mean accuracy와 똑같이 나옵니다\n",
    "#accuracy: 예측 결과가 실제와 얼마나 동일한지를 측정\n",
    "#(TP+TN)/(TP+FN+FP+TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred) #다른 지표들도 구해보았습니다\n",
    "#f1-score: precision과 recall을 결합한 지표(2*(precision*recall/(precision+recall)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7220112814262724"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred) #roc auc score도 이용해 성능을 평가했습니다(뒤에서 비교할 때 사용)\n",
    "#AUC : roc curve 아래의 면적으로, 클수록(1에 가까울수록) 좋음!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4444444444444444"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred)\n",
    "#recall: TP/(FN+TP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5714285714285714"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred)\n",
    "#precision: TP/(FP+TP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7111,    0],\n",
       "       [   1,    8]], dtype=int64)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling version도 똑같이 구하였습니다\n",
    "#confusion matrix를 찍어보고\n",
    "confusion_matrix(y_test2,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998595505617978"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test2,y_pred2)\n",
    "#이 역시 아까 구한 값과 똑같이 나옵니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9411764705882353"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling version도 다른 지표들도 구해 보았습니다\n",
    "f1_score(y_test2, y_pred2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test2,y_pred2) #이거도 뒤에서 비교할때 사용했습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test2,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test2,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3번. Logistic은 cut-off를 어떻게 설정하느냐에 따라 Classification의 성능이 달라짐. 최적의 Cut-off를 ROC 커브를 사용해 서 찾아보세요. 또한 새롭게 찾은 cut-off로 예측을 다시 진행하여 성능 평가를 진행하세요(mean accuracy 제외).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fpr = 1-specificity\n",
    "# tpr = sensitivity\n",
    "# thresholds = 해당 fpr, tpr을 리턴할 때의 thesholds(cut-off) 값!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "#최적의 cutoff값을 찾기 위해 결정함수가 아닌 앞서 구한 predict score의 값을 가져와\n",
    "#적절한 thresholds값을 찾았습니다\n",
    "#1로 예측할 확률을 predscore로 정의.\n",
    "pred_score=classifier.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.90894246e-04, 2.61292060e-04, 1.42049928e-06, ...,\n",
       "       3.65806843e-03, 6.04199595e-04, 4.21893358e-04])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr,tpr,thresholds = roc_curve(y_test,pred_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sensitivity')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAU4UlEQVR4nO3df7BndX3f8eeLn2kKq8alCbKLi3GJWakFe8OPmEYITgo0stPRKlhGsNQ1aRGFNDM0pOiQZMZo468Rha0QiAm/lDRsnXXQ+rsKGy7lR2QtdsvPBWZYIgEtUQTf/eN71nx7996939295/vl3s/zMXPnfs85n/P9vj97d/d1z+ec8zmpKiRJ7dpr0gVIkibLIJCkxhkEktQ4g0CSGmcQSFLj9pl0Abtq+fLltWrVqkmXIUmLym233fZ4VR0027ZFFwSrVq1ienp60mVI0qKS5IG5tjk0JEmNMwgkqXEGgSQ1ziCQpMYZBJLUuN6CIMkVSR5L8q05tifJR5NsSXJXklf3VYskaW59HhFcCZy0k+0nA6u7r3XAJ3qsRZI0h97uI6iqryVZtZMma4E/rcE82LckeWGSg6vq0b5qms/Vmx7kxjsentTHS9JOrXnJMt7z+lcu+PtO8hzBIcBDQ8tbu3U7SLIuyXSS6W3btvVW0I13PMzmR5/q7f0l6flokncWZ5Z1sz4lp6rWA+sBpqamen2SzpqDl3HdO47r8yMk6XllkkcEW4GVQ8srgEcmVIskNWuSQbABeGt39dCxwJOTPD8gSa3qbWgoyTXA8cDyJFuB9wD7AlTVpcBG4BRgC/A08La+apEkza3Pq4ZOn2d7Af++r8+XJI3GO4slqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklq3CSfWTwxV296kBvveHiH9ZsffYo1By+bQEWSNDlNHhHceMfDbH70qR3Wrzl4GWuPPGQCFUnS5DR5RACD//Sve8dxky5DkiauySMCSdLfMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1LhegyDJSUnuSbIlyQWzbD80yZeT3J7kriSn9FmPJGlHvQVBkr2BS4CTgTXA6UnWzGj2e8D1VXUUcBrw8b7qkSTNrs8jgqOBLVV1b1U9A1wLrJ3RpoDtjwR7AfBIj/VIkmbRZxAcAjw0tLy1WzfsvcAZSbYCG4F3zvZGSdYlmU4yvW3btj5qlaRm9RkEmWVdzVg+HbiyqlYApwCfSrJDTVW1vqqmqmrqoIMO6qFUSWpXn0GwFVg5tLyCHYd+zgauB6iqm4GfApb3WJMkaYY+g+BWYHWSw5Lsx+Bk8IYZbR4ETgRI8osMgqC3sZ+rNz3Imy+7edYH10tSq3oLgqp6FjgHuAn4NoOrg+5OcnGSU7tmvw28PcmdwDXAWVU1c/howdx4x8NsfvQp1hy8jLVHzjxdIUlt2qfPN6+qjQxOAg+vu2jo9WbgNX3WMNOag5dx3TuOG+dHStLzmncWS1LjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMaNFARJbkjyL5LsUnAkOSnJPUm2JLlgjjZvSrI5yd1Jrt6V95ck7blR/2P/BPAW4H8neV+SV8y3Q5K9gUuAk4E1wOlJ1sxosxr4j8BrquqVwLt3pXhJ0p4bKQiq6r9X1b8GXg3cD3whyTeTvC3JvnPsdjSwparurapngGuBtTPavB24pKqe6D7nsd3phCRp94081JPkxcBZwL8Fbgc+wiAYvjDHLocADw0tb+3WDTscODzJN5LckuSkOT57XZLpJNPbtm0btWRJ0gj2GaVRkr8AXgF8Cnh9VT3abbouyfRcu82yrmb5/NXA8cAK4OtJjqiqv/3/dqpaD6wHmJqamvkekqQ9MFIQAJ+sqo3DK5LsX1U/rKqpOfbZCqwcWl4BPDJLm1uq6kfAfUnuYRAMt45YlyRpD406NPQHs6y7eZ59bgVWJzksyX7AacCGGW3+EjgBIMlyBkNF945YkyRpAez0iCDJzzEY1/8HSY7i74d7lgE/vbN9q+rZJOcANwF7A1dU1d1JLgamq2pDt+3Xk2wGngN+p6r+Zo96JEnaJfMNDf1zBieIVwAfHFr/PeB353vzbjhp44x1Fw29LuD87kuSNAE7DYKqugq4KskbquqGMdUkSRqj+YaGzqiqPwNWJdnht/aq+uAsu0mSFpH5hob+Yff9gL4LkSRNxnxDQ5d1Lz9eVd7JJUlL0KiXj34zyeeTnJ3kRb1WJEkaq1HnGloN/B7wSuC2JJ9NckavlUmSxmLkuYaq6q+q6nwGk8l9F7iqt6okSWMz6vMIliU5M8nngG8CjzIIBEnSIjfqXEN3MpgO4uKqmm9qCUnSIjJqELysuwtYkrTEzHdD2Yer6t3AhiQ7BEFVndpbZZKksZjviOBT3ff/3HchkqTJmO+Gstu6l0dW1UeGtyV5F/DVvgqTJI3HqJePnjnLurMWsA5J0oTMd47gdOAtwGFJhh8qcyDgcwMkaQmY7xzB9nsGlgN/PLT+e8BdfRUlSRqf+c4RPAA8ABw3nnIkSeM239DQ/6iqX0nyPWD48tEweMDYsl6rkyT1br4jgl/pvh84nnIkSeM26lxDP59k/+718UnOTfLCfkuTJI3DqJeP3gA8l+TlwOXAYcDVvVUlSRqbUYPgx1X1LPAvgQ9X1XnAwf2VJUkal1GD4EfdPQVnAp/t1u3bT0mSpHEaNQjexuAS0j+sqvuSHAb8WX9lSZLGZdRHVW6uqnOr6ppu+b6qel+/pS2sqzc9yKb7vjvpMiTpeWek5xEkeQ3wXuCl3T7b7yN4WX+lLawb73gYgLVHHjLhSiTp+WXUB9NcDpwH3AY81185/TrmsJ/hLcccOukyJOl5ZdQgeLKqPtdrJZKkiRg1CL6c5APAXwA/3L6yqv5nL1VJksZm1CA4pvs+NbSugF9b2HIkSeM2UhBU1Ql9FyJJmoxR5xr62SSXJ/lct7wmydkj7HdSknuSbElywU7avTFJJZmaq40kqR+j3lB2JXAT8JJu+TvAu3e2Q5K9gUuAk4E1wOlJ1szS7kDgXGDTiLVIkhbQqEGwvKquB34M0M07NN9lpEcDW6rq3qp6BrgWWDtLu98H3g/8YMRaJEkLaNQg+L9JXkz3cJokxwJPzrPPIcBDQ8tbu3U/keQoYGVVfZadSLIuyXSS6W3bto1YsiRpFKNeNXQ+sAH4+STfAA4C3jjPPpll3U+ecpZkL+BDwFnzfXhVrQfWA0xNTdU8zSVJu2CnRwRJfinJz3X3C7wW+F0G9xF8nsFv+DuzFVg5tLwCeGRo+UDgCOArSe4HjgU2eMJYksZrvqGhy4Bnute/DFzI4ATwE3S/oe/ErcDqJIcl2Q84jcFRBQBV9WRVLa+qVVW1CrgFOLWqpne9G5Kk3TXf0NDeVbV9ys43A+ur6gbghiR37GzHqno2yTkMrjbaG7iiqu5OcjEwXVUbdra/JGk85g2CJPt0VwmdCKzbhX2pqo3AxhnrLpqj7fHzvZ8kaeHN95/5NcBXkzwO/B3wdYDu2cXzXTUkSVoEdhoEVfWHSb7I4PnEn6+q7Vfs7AW8s+/iJEn9G2V455ZZ1n2nn3IkSeM26g1lkqQlyiCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUuF6DIMlJSe5JsiXJBbNsPz/J5iR3Jflikpf2WY8kaUe9BUGSvYFLgJOBNcDpSdbMaHY7MFVVrwI+A7y/r3okSbPr84jgaGBLVd1bVc8A1wJrhxtU1Zer6ulu8RZgRY/1SJJm0WcQHAI8NLS8tVs3l7OBz822Icm6JNNJprdt27aAJUqS+gyCzLKuZm2YnAFMAR+YbXtVra+qqaqaOuiggxawREnSPj2+91Zg5dDyCuCRmY2SvA64EHhtVf2wx3okSbPo84jgVmB1ksOS7AecBmwYbpDkKOAy4NSqeqzHWiRJc+gtCKrqWeAc4Cbg28D1VXV3kouTnNo1+wBwAPDpJHck2TDH20mSetLn0BBVtRHYOGPdRUOvX9fn50uS5uedxZLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNa7XIEhyUpJ7kmxJcsEs2/dPcl23fVOSVX3WI0naUW9BkGRv4BLgZGANcHqSNTOanQ08UVUvBz4E/FFf9UiSZtfnEcHRwJaqureqngGuBdbOaLMWuKp7/RngxCTpsSZJ0gz79PjehwAPDS1vBY6Zq01VPZvkSeDFwOPDjZKsA9YBHHroobtVzJqXLNut/SRpqeszCGb7zb52ow1VtR5YDzA1NbXD9lG85/Wv3J3dJGnJ63NoaCuwcmh5BfDIXG2S7AO8APhujzVJkmboMwhuBVYnOSzJfsBpwIYZbTYAZ3av3wh8qap26zd+SdLu6W1oqBvzPwe4CdgbuKKq7k5yMTBdVRuAy4FPJdnC4EjgtL7qkSTNrs9zBFTVRmDjjHUXDb3+AfCv+qxBkrRz3lksSY0zCCSpcQaBJDXOIJCkxmWxXa2ZZBvwwG7uvpwZdy03wD63wT63YU/6/NKqOmi2DYsuCPZEkumqmpp0HeNkn9tgn9vQV58dGpKkxhkEktS41oJg/aQLmAD73Ab73IZe+tzUOQJJ0o5aOyKQJM1gEEhS45ZkECQ5Kck9SbYkuWCW7fsnua7bvinJqvFXubBG6PP5STYnuSvJF5O8dBJ1LqT5+jzU7o1JKsmiv9RwlD4neVP3s747ydXjrnGhjfB3+9AkX05ye/f3+5RJ1LlQklyR5LEk35pje5J8tPvzuCvJq/f4Q6tqSX0xmPL6/wAvA/YD7gTWzGjz74BLu9enAddNuu4x9PkE4Ke717/VQp+7dgcCXwNuAaYmXfcYfs6rgduBF3XL/2jSdY+hz+uB3+perwHun3Tde9jnXwVeDXxrju2nAJ9j8ITHY4FNe/qZS/GI4GhgS1XdW1XPANcCa2e0WQtc1b3+DHBiktkem7lYzNvnqvpyVT3dLd7C4Ilxi9koP2eA3wfeD/xgnMX1ZJQ+vx24pKqeAKiqx8Zc40Ibpc8FbH8o+QvY8UmIi0pVfY2dP6lxLfCnNXAL8MIkB+/JZy7FIDgEeGhoeWu3btY2VfUs8CTw4rFU149R+jzsbAa/USxm8/Y5yVHAyqr67DgL69EoP+fDgcOTfCPJLUlOGlt1/Rilz+8FzkiylcHzT945ntImZlf/vc+r1wfTTMhsv9nPvEZ2lDaLycj9SXIGMAW8tteK+rfTPifZC/gQcNa4ChqDUX7O+zAYHjqewVHf15McUVV/23NtfRmlz6cDV1bVHyc5jsFTD4+oqh/3X95ELPj/X0vxiGArsHJoeQU7Hir+pE2SfRgcTu7sUOz5bpQ+k+R1wIXAqVX1wzHV1pf5+nwgcATwlST3MxhL3bDITxiP+nf7xqr6UVXdB9zDIBgWq1H6fDZwPUBV3Qz8FIPJ2Zaqkf6974qlGAS3AquTHJZkPwYngzfMaLMBOLN7/UbgS9WdhVmk5u1zN0xyGYMQWOzjxjBPn6vqyapaXlWrqmoVg/Mip1bV9GTKXRCj/N3+SwYXBpBkOYOhonvHWuXCGqXPDwInAiT5RQZBsG2sVY7XBuCt3dVDxwJPVtWje/KGS25oqKqeTXIOcBODKw6uqKq7k1wMTFfVBuByBoePWxgcCZw2uYr33Ih9/gBwAPDp7rz4g1V16sSK3kMj9nlJGbHPNwG/nmQz8BzwO1X1N5Ores+M2OffBv5LkvMYDJGctZh/sUtyDYOhveXdeY/3APsCVNWlDM6DnAJsAZ4G3rbHn7mI/7wkSQtgKQ4NSZJ2gUEgSY0zCCSpcQaBJDXOIJCkxhkEWpLmm8Gxa3NhN0PnXUnuSHLMAtewMckLu9fnJvl2kj9PcurOZkvt2n+z+74qyVsWsi5pJi8f1ZKU5FeB7zOYnOuIWbYfB3wQOL6qftjdfLVfVfUyYVmS/wWc3N3tuyv7HQ/8h6r6jT7qksAjAi1RI8zgeDDw+PapNqrq8e0hkOT+JH+U5K+6r5d36w9KckOSW7uv13TrD0jyJ0n+uju6eMPQ+yxPcimDaZQ3JDkvyVlJPta1+dkk/zXJnd3XL3frv9/V+T7gn3VHLOcl+XqSI7d3optc7lUL+EenBhkEatXngZVJvpPk40lmTsL3VFUdDXwM+HC37iPAh6rql4A3AJ/s1v8nBrf5/+OqehXwpeE3qqrfZDAXzAlV9aEZn/NR4KtV9U8YzEF/94ztFwBfr6oju30/STeRXpLDgf2r6q7d6L/0EwaBmlRV3wf+KbCOwbw01yU5a6jJNUPfj+tevw74WJI7GMz3sizJgd36S4be+4ldKOXXgE90+z1XVU/O0/7TwG8k2Rf4N8CVu/BZ0qyW3FxD0mySrAT+W7d4aVVdWlXPAV9hMEPpXzOYiPDKrs3wybPtr/cCjquqv5vx3mFM05hX1dNJvsDg4SRvYjCluLRHPCJQE6rqoW545ciqujTJLyQZnp75SOCBoeU3D32/uXv9eeCc7Q2Gxupnrn/RLpT2RQaPDiXJ3kmWzdj+PQZTag/7JIMhpVurajFPn67nCYNAS1I3g+PNwC8k2Zrk7BlNDgCuyuAh73cxeNbte4e2759kE/Au4Lxu3bnAVHdCeDPwm936PwBelORbSe6kmwZ6RO8CTuiOSG4DXjlj+13As92J5PMAquo24CngT3bhc6Q5efmoNEMGD7KZqqrHJ13LbJK8hMGQ1iuW8FO4NEYeEUiLSJK3ApuACw0BLRSPCCSpcR4RSFLjDAJJapxBIEmNMwgkqXEGgSQ17v8Blp/V6yxJ69MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#roc curve그려보았습니다\n",
    "x = fpr\n",
    "y = tpr\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('1-Specificity')\n",
    "plt.ylabel('Sensitivity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.0, 1.99999996277483],\n",
       " [0.0, 0.1111111111111111, 0.9999999627748299],\n",
       " [0.0, 0.2222222222222222, 0.9999743030401465],\n",
       " [0.0001406271972999578, 0.2222222222222222, 0.9987101056722099],\n",
       " [0.0001406271972999578, 0.3333333333333333, 0.961486138324919],\n",
       " [0.00042188159189987344, 0.3333333333333333, 0.6377590391882765],\n",
       " [0.00042188159189987344, 0.4444444444444444, 0.5480648497676882],\n",
       " [0.0007031359864997891, 0.4444444444444444, 0.3677194548486106],\n",
       " [0.0007031359864997891, 0.5555555555555556, 0.36116172003101726],\n",
       " [0.001546899170299536, 0.5555555555555556, 0.1364743131323381],\n",
       " [0.001546899170299536, 0.6666666666666666, 0.13398855807607823],\n",
       " [0.002953171143299114, 0.6666666666666666, 0.08674769295102296],\n",
       " [0.002953171143299114, 0.7777777777777778, 0.07938603463799468],\n",
       " [0.008859513429897343, 0.7777777777777778, 0.03257216838073841],\n",
       " [0.008859513429897343, 0.8888888888888888, 0.03222970134151204],\n",
       " [0.02770355786809169, 0.8888888888888888, 0.013500255362707096],\n",
       " [0.02770355786809169, 1.0, 0.013377036978403109],\n",
       " [0.35480241878779356, 1.0, 0.000596636187807372],\n",
       " [0.3550836731823935, 1.0, 0.0005963209532274108],\n",
       " [1.0, 1.0, 5.447798052241285e-12]]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = [[fpr_i, tpr_i, thres_i] for fpr_i, tpr_i, thres_i in zip(fpr, tpr, thresholds)]\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9722964421319084, 0.013377036978403109]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fpr과 tpr값 차이가 가장 큰 값을 찾아 cut off 값을 찾아서 성능을 평가해보았습니다\n",
    "res = [[abs(fpr_i- tpr_i), thres_i] for fpr_i, tpr_i, thres_i in zip(fpr, tpr, thresholds)]\n",
    "max(res)\n",
    "#threshold(cut off): 0.013377036978403109이 나왔습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6914,  197],\n",
       "       [   1,    8]], dtype=int64)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#구한 cutoff값을 이용하여 그 값보다 큰 경우 true, 작은 경우 false로 구한 뒤 confusion matrix를 구했습니다\n",
    "confusion_matrix(y_test,pred_score >0.013377036978403109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9305926655103987"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#roc curve의 roccurve의 auc score값을 통해 성능을 확인하였습니다\n",
    "roc_auc_score(y_test,pred_score>0.013377036978403109)\n",
    "\n",
    "# 2번에서 로지스틱회귀를 진행하였을 때 0.7220112814262724였는데\n",
    "# cutoff를 설정하고 나서 roc_auc_score이 증가하였음을 확인할 수 있습니다\n",
    "#성능 좋아짐!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07476635514018691"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,pred_score>0.013377036978403109)\n",
    "#f1score은 감소하였습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#동일한 방식을 앞서 scaling한 version 똑같이 진행하였습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_score2=classifier2.predict_proba(X_test2)[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00053345, 0.00137104, 0.00023725, ..., 0.00033921, 0.00428698,\n",
       "       0.00207661])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_score2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr2,tpr2,thresholds2 = roc_curve(y_test2,pred_score2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sensitivity')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scaling version roc curve 그리기\n",
    "x = fpr2\n",
    "y = tpr2\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('1-Specificity')\n",
    "plt.ylabel('Sensitivity')\n",
    "\n",
    "#scaling version이라 그런지 roc curve자체가 굉장히 좋게 나오더라고요\n",
    "#그래도 cutoff값을 찾아보았습니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.0, 1.9999995235185173],\n",
       " [0.0, 0.1111111111111111, 0.9999995235185172],\n",
       " [0.0, 0.8888888888888888, 0.7347856987302641],\n",
       " [0.00042188159189987344, 0.8888888888888888, 0.18573960979032114],\n",
       " [0.00042188159189987344, 1.0, 0.14897425361235758],\n",
       " [0.4636478694979609, 1.0, 0.0005036185022503212],\n",
       " [0.4639291238925608, 1.0, 0.000503557405250285],\n",
       " [1.0, 1.0, 6.890831924312993e-08]]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2 = [[fpr_i, tpr_i, thres_i] for fpr_i, tpr_i, thres_i in zip(fpr2, tpr2, thresholds2)]\n",
    "res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9995781184081002, 0.14897425361235758]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2 = [[abs(fpr_i-tpr_i), thres_i] for fpr_i, tpr_i, thres_i in zip(fpr2, tpr2, thresholds2)]\n",
    "max(res2)\n",
    "\n",
    "#threshold = 0.14897425361235758 (cut off)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7108,    3],\n",
       "       [   1,    8]], dtype=int64)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test2,pred_score2 > 0.14897425361235758)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9442335036484945"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test2,pred_score2 > 0.14897425361235758)\n",
    "\n",
    "#cutoff값을 정하기 전도 너무 완벽한 roc curve를 보였듯이\n",
    "#오히려 cutoff값을 정해서 roc_auc_score값을 정하니까 2번에서 구했던 그 전(0.9444444444444444)보다\n",
    "#아주 약간 감소하였습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7999999999999999"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test2,pred_score2 >0.14897425361235758 )\n",
    "#f1_score값도 감소하였습니다"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}