baseball_gboost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17706fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 한글 폰트를 사용하기 위한 코드\n",
    "import platform\n",
    "if platform.system() == 'Windows':\n",
    "    plt.rc('font', family='Malgun Gothic')\n",
    "elif platform.system() == 'Linux':\n",
    "    plt.rc('font', family='NanumGothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1ea8f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "년도         0\n",
      "날짜         0\n",
      "승패         0\n",
      "상대         0\n",
      "AVG_K      0\n",
      "OPS_K      0\n",
      "LI_K       0\n",
      "WPA_K      0\n",
      "HR/AB_K    0\n",
      "SO/AB_K    0\n",
      "ERA_K      0\n",
      "WHIP_K     0\n",
      "LI_K.1     0\n",
      "WPA_K.1    0\n",
      "SO/IP_K    0\n",
      "AVG_R      0\n",
      "OPS_R      0\n",
      "LI_R       0\n",
      "WPA_R      0\n",
      "HR/AB_R    0\n",
      "SO/AB_R    0\n",
      "ERA_R      0\n",
      "WHIP_R     0\n",
      "LI_R.1     0\n",
      "WPA_R.1    0\n",
      "SO/IP_R    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>년도</th>\n",
       "      <th>날짜</th>\n",
       "      <th>승패</th>\n",
       "      <th>상대</th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
       "      <th>LI_K</th>\n",
       "      <th>WPA_K</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>...</th>\n",
       "      <th>OPS_R</th>\n",
       "      <th>LI_R</th>\n",
       "      <th>WPA_R</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>WHIP_R</th>\n",
       "      <th>LI_R.1</th>\n",
       "      <th>WPA_R.1</th>\n",
       "      <th>SO/IP_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 24일</td>\n",
       "      <td>0</td>\n",
       "      <td>kt</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.243</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 25일</td>\n",
       "      <td>1</td>\n",
       "      <td>kt</td>\n",
       "      <td>0.410</td>\n",
       "      <td>1.224</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.242</td>\n",
       "      <td>15.75</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 27일</td>\n",
       "      <td>1</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.389</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>19.13</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.282</td>\n",
       "      <td>1.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 28일</td>\n",
       "      <td>0</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 29일</td>\n",
       "      <td>1</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.394</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.353</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.321</td>\n",
       "      <td>7.88</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 24일</td>\n",
       "      <td>0</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.411</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.361</td>\n",
       "      <td>1.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 27일</td>\n",
       "      <td>1</td>\n",
       "      <td>롯데</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.452</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-0.441</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.161</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 28일</td>\n",
       "      <td>1</td>\n",
       "      <td>롯데</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754</td>\n",
       "      <td>1.38</td>\n",
       "      <td>-0.278</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.171</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.222</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 29일</td>\n",
       "      <td>0</td>\n",
       "      <td>두산</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.81</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.098</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 30일</td>\n",
       "      <td>0</td>\n",
       "      <td>키움</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.143</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       년도       날짜  승패  상대  AVG_K  OPS_K  LI_K  WPA_K  HR/AB_K  SO/AB_K  ...  \\\n",
       "0    2018   3월 24일   0  kt  0.333  0.779  1.75 -0.225    0.000    0.222  ...   \n",
       "1    2018   3월 25일   1  kt  0.410  1.224  0.24  0.395    0.103    0.205  ...   \n",
       "2    2018   3월 27일   1  삼성  0.389  1.494  0.30  0.282    0.167    0.278  ...   \n",
       "3    2018   3월 28일   0  삼성  0.156  0.479  0.89 -0.386    0.000    0.156  ...   \n",
       "4    2018   3월 29일   1  삼성  0.394  1.080  0.61  0.147    0.061    0.212  ...   \n",
       "..    ...      ...  ..  ..    ...    ...   ...    ...      ...      ...  ...   \n",
       "549  2021  10월 24일   0  NC  0.194  0.411  1.28 -0.361    0.000    0.333  ...   \n",
       "550  2021  10월 27일   1  롯데  0.286  0.718  1.26  0.059    0.000    0.200  ...   \n",
       "551  2021  10월 28일   1  롯데  0.188  0.559  0.96  0.222    0.031    0.219  ...   \n",
       "552  2021  10월 29일   0  두산  0.286  0.833  0.81 -0.040    0.029    0.314  ...   \n",
       "553  2021  10월 30일   0  키움  0.161  0.419  0.58 -0.235    0.032    0.226  ...   \n",
       "\n",
       "     OPS_R  LI_R  WPA_R  HR/AB_R  SO/AB_R  ERA_R  WHIP_R  LI_R.1  WPA_R.1  \\\n",
       "0    0.883  1.01  0.275    0.081    0.243   4.00    1.56     1.7    0.225   \n",
       "1    0.545  0.23 -0.105    0.030    0.242  15.75    2.38     0.2   -0.395   \n",
       "2    0.454  0.32 -0.218    0.000    0.156  19.13    2.88     0.3   -0.282   \n",
       "3    0.826  0.60  0.114    0.026    0.179   0.00    0.78     0.9    0.386   \n",
       "4    0.372  0.84 -0.353    0.000    0.321   7.88    2.13     0.6   -0.147   \n",
       "..     ...   ...    ...      ...      ...    ...     ...     ...      ...   \n",
       "549  0.698  1.20  0.139    0.032    0.323   1.00    0.89     1.3    0.361   \n",
       "550  0.452  1.20 -0.441    0.032    0.161   3.00    1.56     1.3   -0.059   \n",
       "551  0.754  1.38 -0.278    0.057    0.171   5.00    1.11     1.0   -0.222   \n",
       "552  0.892  0.42  0.460    0.049    0.098   5.00    1.89     0.8    0.040   \n",
       "553  0.886  0.51  0.265    0.029    0.143   1.00    0.56     0.6    0.235   \n",
       "\n",
       "     SO/IP_R  \n",
       "0      0.889  \n",
       "1      1.000  \n",
       "2      1.250  \n",
       "3      0.556  \n",
       "4      0.875  \n",
       "..       ...  \n",
       "549    1.333  \n",
       "550    0.778  \n",
       "551    0.778  \n",
       "552    1.222  \n",
       "553    0.778  \n",
       "\n",
       "[554 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data 가져오기\n",
    "df = pd.read_csv('./t_data.csv')\n",
    "# 결측값 확인\n",
    "print(df.isnull().sum())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bcde294a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>승패</th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>ERA_K</th>\n",
       "      <th>WHIP_K</th>\n",
       "      <th>SO/IP_K</th>\n",
       "      <th>AVG_R</th>\n",
       "      <th>OPS_R</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>WHIP_R</th>\n",
       "      <th>SO/IP_R</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.243</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.410</td>\n",
       "      <td>1.224</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.242</td>\n",
       "      <td>15.75</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.389</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>19.13</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.394</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.321</td>\n",
       "      <td>7.88</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.161</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.219</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.171</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.314</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.098</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.226</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.143</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     승패  AVG_K  OPS_K  HR/AB_K  SO/AB_K  ERA_K  WHIP_K  SO/IP_K  AVG_R  OPS_R  \\\n",
       "0     0  0.333  0.779    0.000    0.222   5.00    1.33    1.000  0.297  0.883   \n",
       "1     1  0.410  1.224    0.103    0.205   1.00    0.78    0.889  0.212  0.545   \n",
       "2     1  0.389  1.494    0.167    0.278   0.00    0.89    0.556  0.188  0.454   \n",
       "3     0  0.156  0.479    0.000    0.156   6.00    1.78    0.778  0.359  0.826   \n",
       "4     1  0.394  1.080    0.061    0.212   0.00    1.00    1.000  0.107  0.372   \n",
       "..   ..    ...    ...      ...      ...    ...     ...      ...    ...    ...   \n",
       "549   0  0.194  0.411    0.000    0.333   3.38    1.38    1.250  0.258  0.698   \n",
       "550   1  0.286  0.718    0.000    0.200   2.00    0.56    0.556  0.161  0.452   \n",
       "551   1  0.188  0.559    0.031    0.219   3.00    1.33    0.667  0.257  0.754   \n",
       "552   0  0.286  0.833    0.029    0.314   8.00    2.11    0.444  0.317  0.892   \n",
       "553   0  0.161  0.419    0.032    0.226   6.00    2.00    0.556  0.343  0.886   \n",
       "\n",
       "     HR/AB_R  SO/AB_R  ERA_R  WHIP_R  SO/IP_R  \n",
       "0      0.081    0.243   4.00    1.56    0.889  \n",
       "1      0.030    0.242  15.75    2.38    1.000  \n",
       "2      0.000    0.156  19.13    2.88    1.250  \n",
       "3      0.026    0.179   0.00    0.78    0.556  \n",
       "4      0.000    0.321   7.88    2.13    0.875  \n",
       "..       ...      ...    ...     ...      ...  \n",
       "549    0.032    0.323   1.00    0.89    1.333  \n",
       "550    0.032    0.161   3.00    1.56    0.778  \n",
       "551    0.057    0.171   5.00    1.11    0.778  \n",
       "552    0.049    0.098   5.00    1.89    1.222  \n",
       "553    0.029    0.143   1.00    0.56    0.778  \n",
       "\n",
       "[554 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필요없는 특징 제거 (WPA, LI도 다 제외했음)\n",
    "df_1 = df.drop(['년도', '날짜', '상대', 'WPA_K', 'WPA_K.1', 'LI_K', 'LI_K.1', 'LI_R', 'LI_R.1', 'WPA_R', 'WPA_R.1'], axis=1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6191e63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "plt.title(\"승패\")\n",
    "plt.xlabel('승패')\n",
    "plt.ylabel('수')\n",
    "plt.hist(df_1['승패'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5de5d714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>승패</td>        <th>  R-squared (uncentered):</th>      <td>   0.793</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.788</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   147.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 08 Dec 2022</td> <th>  Prob (F-statistic):</th>          <td>2.90e-174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:28:29</td>     <th>  Log-Likelihood:    </th>          <td> -143.41</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   554</td>      <th>  AIC:               </th>          <td>   314.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   540</td>      <th>  BIC:               </th>          <td>   375.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AVG_K</th>   <td>   -0.7454</td> <td>    0.620</td> <td>   -1.203</td> <td> 0.229</td> <td>   -1.962</td> <td>    0.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OPS_K</th>   <td>    0.7599</td> <td>    0.323</td> <td>    2.350</td> <td> 0.019</td> <td>    0.125</td> <td>    1.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_K</th> <td>   -1.8006</td> <td>    1.029</td> <td>   -1.750</td> <td> 0.081</td> <td>   -3.822</td> <td>    0.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_K</th> <td>    0.1847</td> <td>    1.045</td> <td>    0.177</td> <td> 0.860</td> <td>   -1.867</td> <td>    2.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_K</th>   <td>   -0.0543</td> <td>    0.007</td> <td>   -7.697</td> <td> 0.000</td> <td>   -0.068</td> <td>   -0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WHIP_K</th>  <td>   -0.0089</td> <td>    0.060</td> <td>   -0.149</td> <td> 0.882</td> <td>   -0.126</td> <td>    0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/IP_K</th> <td>   -0.7308</td> <td>    0.220</td> <td>   -3.315</td> <td> 0.001</td> <td>   -1.164</td> <td>   -0.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AVG_R</th>   <td>    0.7335</td> <td>    0.469</td> <td>    1.564</td> <td> 0.118</td> <td>   -0.188</td> <td>    1.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OPS_R</th>   <td>   -0.1748</td> <td>    0.199</td> <td>   -0.877</td> <td> 0.381</td> <td>   -0.566</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_R</th> <td>   -0.3014</td> <td>    0.465</td> <td>   -0.649</td> <td> 0.517</td> <td>   -1.214</td> <td>    0.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_R</th> <td>    3.4517</td> <td>    0.834</td> <td>    4.137</td> <td> 0.000</td> <td>    1.813</td> <td>    5.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_R</th>   <td>    0.0457</td> <td>    0.008</td> <td>    5.944</td> <td> 0.000</td> <td>    0.031</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WHIP_R</th>  <td>    0.0313</td> <td>    0.073</td> <td>    0.429</td> <td> 0.668</td> <td>   -0.112</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/IP_R</th> <td>   -0.0435</td> <td>    0.276</td> <td>   -0.158</td> <td> 0.875</td> <td>   -0.586</td> <td>    0.499</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>23.236</td> <th>  Durbin-Watson:     </th> <td>   2.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  10.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.035</td> <th>  Prob(JB):          </th> <td> 0.00557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.333</td> <th>  Cond. No.          </th> <td>    780.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                     승패   R-squared (uncentered):                   0.793\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.788\n",
       "Method:                 Least Squares   F-statistic:                              147.8\n",
       "Date:                Thu, 08 Dec 2022   Prob (F-statistic):                   2.90e-174\n",
       "Time:                        16:28:29   Log-Likelihood:                         -143.41\n",
       "No. Observations:                 554   AIC:                                      314.8\n",
       "Df Residuals:                     540   BIC:                                      375.3\n",
       "Df Model:                          14                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "AVG_K         -0.7454      0.620     -1.203      0.229      -1.962       0.472\n",
       "OPS_K          0.7599      0.323      2.350      0.019       0.125       1.395\n",
       "HR/AB_K       -1.8006      1.029     -1.750      0.081      -3.822       0.221\n",
       "SO/AB_K        0.1847      1.045      0.177      0.860      -1.867       2.237\n",
       "ERA_K         -0.0543      0.007     -7.697      0.000      -0.068      -0.040\n",
       "WHIP_K        -0.0089      0.060     -0.149      0.882      -0.126       0.108\n",
       "SO/IP_K       -0.7308      0.220     -3.315      0.001      -1.164      -0.298\n",
       "AVG_R          0.7335      0.469      1.564      0.118      -0.188       1.655\n",
       "OPS_R         -0.1748      0.199     -0.877      0.381      -0.566       0.217\n",
       "HR/AB_R       -0.3014      0.465     -0.649      0.517      -1.214       0.611\n",
       "SO/AB_R        3.4517      0.834      4.137      0.000       1.813       5.091\n",
       "ERA_R          0.0457      0.008      5.944      0.000       0.031       0.061\n",
       "WHIP_R         0.0313      0.073      0.429      0.668      -0.112       0.175\n",
       "SO/IP_R       -0.0435      0.276     -0.158      0.875      -0.586       0.499\n",
       "==============================================================================\n",
       "Omnibus:                       23.236   Durbin-Watson:                   2.022\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               10.379\n",
       "Skew:                           0.035   Prob(JB):                      0.00557\n",
       "Kurtosis:                       2.333   Cond. No.                         780.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r- square\n",
    "df_test = df_1.drop(['승패'], axis=1)\n",
    "lm = sm.OLS(df_1['승패'], df_test)\n",
    "results = lm.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df9ebe8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data, label 나누기\n",
    "df_train = df_1.drop(['승패'], axis = 1) \n",
    "df_label = df_1.loc[:, ['승패']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d08a9283",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.333</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.33</td>\n",
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       "      <td>4.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.889</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.410</td>\n",
       "      <td>1.224</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.78</td>\n",
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       "      <td>0.030</td>\n",
       "      <td>0.242</td>\n",
       "      <td>15.75</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.389</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>19.13</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1.250</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.156</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.556</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.394</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.321</td>\n",
       "      <td>7.88</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.875</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>549</th>\n",
       "      <td>0.194</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.333</td>\n",
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       "      <th>550</th>\n",
       "      <td>0.286</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.161</td>\n",
       "      <td>3.00</td>\n",
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       "      <td>0.188</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.219</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.171</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.778</td>\n",
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       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.286</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.314</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.098</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.222</td>\n",
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       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.161</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.226</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.143</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AVG_K  OPS_K  HR/AB_K  SO/AB_K  ERA_K  WHIP_K  SO/IP_K  AVG_R  OPS_R  \\\n",
       "0    0.333  0.779    0.000    0.222   5.00    1.33    1.000  0.297  0.883   \n",
       "1    0.410  1.224    0.103    0.205   1.00    0.78    0.889  0.212  0.545   \n",
       "2    0.389  1.494    0.167    0.278   0.00    0.89    0.556  0.188  0.454   \n",
       "3    0.156  0.479    0.000    0.156   6.00    1.78    0.778  0.359  0.826   \n",
       "4    0.394  1.080    0.061    0.212   0.00    1.00    1.000  0.107  0.372   \n",
       "..     ...    ...      ...      ...    ...     ...      ...    ...    ...   \n",
       "549  0.194  0.411    0.000    0.333   3.38    1.38    1.250  0.258  0.698   \n",
       "550  0.286  0.718    0.000    0.200   2.00    0.56    0.556  0.161  0.452   \n",
       "551  0.188  0.559    0.031    0.219   3.00    1.33    0.667  0.257  0.754   \n",
       "552  0.286  0.833    0.029    0.314   8.00    2.11    0.444  0.317  0.892   \n",
       "553  0.161  0.419    0.032    0.226   6.00    2.00    0.556  0.343  0.886   \n",
       "\n",
       "     HR/AB_R  SO/AB_R  ERA_R  WHIP_R  SO/IP_R  \n",
       "0      0.081    0.243   4.00    1.56    0.889  \n",
       "1      0.030    0.242  15.75    2.38    1.000  \n",
       "2      0.000    0.156  19.13    2.88    1.250  \n",
       "3      0.026    0.179   0.00    0.78    0.556  \n",
       "4      0.000    0.321   7.88    2.13    0.875  \n",
       "..       ...      ...    ...     ...      ...  \n",
       "549    0.032    0.323   1.00    0.89    1.333  \n",
       "550    0.032    0.161   3.00    1.56    0.778  \n",
       "551    0.057    0.171   5.00    1.11    0.778  \n",
       "552    0.049    0.098   5.00    1.89    1.222  \n",
       "553    0.029    0.143   1.00    0.56    0.778  \n",
       "\n",
       "[554 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 잘 나눠졌는지 확인\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea48702f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     승패\n",
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       "..   ..\n",
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       "553   0\n",
       "\n",
       "[554 rows x 1 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2b1e8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> numpy\n",
    "df_res = df_train.values\n",
    "df_label = df_label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d944fec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(554, 14)\n",
      "(554, 1)\n"
     ]
    }
   ],
   "source": [
    "print(df_res.shape)\n",
    "print(df_label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a46beaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train, test data 분리 (8 : 2)\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_res, df_label, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50ac0c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gboost 활용\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84dcd673",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Miniconda\\envs\\chonnam\\lib\\site-packages\\sklearn\\ensemble\\_gb.py:494: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f0be490",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_pred = gb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e5d739a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.85295622, -0.21518236,  0.82052608,  0.97201525, -0.06451832,\n",
       "        0.70283887,  0.70978601,  0.97395246,  0.97375836, -0.02343164,\n",
       "       -0.05937302, -0.00685929,  0.96643259,  0.04932059, -0.02607335,\n",
       "        0.06100585,  0.99733017,  1.01028387,  0.00838905,  0.00461485,\n",
       "       -0.02570214, -0.0188271 ,  0.02055421,  0.07774247,  1.01174354,\n",
       "       -0.05226949,  0.48991017,  0.86521756,  0.56319479,  0.05597584,\n",
       "        0.20526312, -0.02304744,  0.67891058,  0.77451131, -0.01407315,\n",
       "       -0.03823391, -0.01682222,  0.7917659 , -0.05584421,  0.68830635,\n",
       "       -0.00842937,  0.06506227, -0.02680521, -0.04946611, -0.05843594,\n",
       "        0.21656307,  0.59273559,  0.11599303, -0.02220294, -0.03044168,\n",
       "        0.14204294,  0.05669222,  1.01173512,  1.02249876,  0.93456167,\n",
       "        0.71024502,  0.98247237,  1.00893652,  0.40621179,  1.06310963,\n",
       "       -0.06034211,  0.97440132,  0.95852004,  0.00794449, -0.04204693,\n",
       "        1.03176165, -0.00645221,  0.00280178,  0.88422125,  0.00376279,\n",
       "       -0.01496896, -0.06609152,  0.00682296, -0.09626436,  1.01449072,\n",
       "        0.01488536,  0.22355348,  0.86353753,  0.96300827, -0.01479564,\n",
       "        0.9928827 ,  0.01484578, -0.00869641,  1.02558132,  0.46243473,\n",
       "        0.06394881,  1.03029619,  1.02926606,  0.51452954,  0.31568513,\n",
       "        1.11492775,  0.96430107,  0.03251053,  0.84215548, -0.04865709,\n",
       "        0.97003629,  0.46132629,  0.34451785,  0.09513537,  1.01218168,\n",
       "        0.68153856,  0.95636954,  0.69717663, -0.01927123,  0.9896359 ,\n",
       "        0.98097127, -0.01042739,  0.80057162,  0.94590347, -0.02728103,\n",
       "        0.05702972])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7030edfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(mean_squared_error(gb_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c82b8fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20451617085149468"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05daeeca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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