fine_dust.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mglearn\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../final.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 분석 및 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 230 entries, 0 to 229\n",
      "Data columns (total 15 columns):\n",
      " #   Column                                          Non-Null Count  Dtype  \n",
      "---  ------                                          --------------  -----  \n",
      " 0   fine_dust                                       230 non-null    int64  \n",
      " 1   O3                                              230 non-null    int64  \n",
      " 2   region                                          230 non-null    object \n",
      " 3   heavy_metal                                     230 non-null    float64\n",
      " 4   traffic                                         230 non-null    float64\n",
      " 5   num_of_factories                                230 non-null    int64  \n",
      " 6   num_of_power_plants                             230 non-null    int64  \n",
      " 7   num_of_people                                   230 non-null    int64  \n",
      " 8   household_waste_discharge_per_person (kg/days)  230 non-null    float64\n",
      " 9   wind_speed                                      230 non-null    float64\n",
      " 10  wind_direction                                  230 non-null    float64\n",
      " 11  rainfall(mm)                                    230 non-null    float64\n",
      " 12  C                                               230 non-null    float64\n",
      " 13  avg_O3                                          230 non-null    float64\n",
      " 14  avg_fine_dust                                   230 non-null    float64\n",
      "dtypes: float64(9), int64(5), object(1)\n",
      "memory usage: 27.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "미세먼지 나쁜 지역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data[data.iloc[:,0] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fine_dust</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도강릉시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도고성군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도동해시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도삼척시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도속초시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0</td>\n",
       "      <td>충청남도계룡시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도괴산군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도단양군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도영동군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     fine_dust      region\n",
       "0            0      강원도강릉시\n",
       "1            0      강원도고성군\n",
       "2            0      강원도동해시\n",
       "3            0      강원도삼척시\n",
       "4            0      강원도속초시\n",
       "..         ...         ...\n",
       "201          0  제주특별자치도제주시\n",
       "202          0     충청남도계룡시\n",
       "218          0     충청북도괴산군\n",
       "219          0     충청북도단양군\n",
       "221          0     충청북도영동군\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[:,0:3:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "미세먼지 좋은 지역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = data[data.iloc[:,0] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fine_dust</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도철원군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도평창군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도홍천군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>경기도고양시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>경기도과천시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1</td>\n",
       "      <td>충청북도증평군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1</td>\n",
       "      <td>충청북도진천군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1</td>\n",
       "      <td>충청북도청원군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1</td>\n",
       "      <td>충청북도청주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1</td>\n",
       "      <td>충청북도충주시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     fine_dust   region\n",
       "11           1   강원도철원군\n",
       "14           1   강원도평창군\n",
       "15           1   강원도홍천군\n",
       "19           1   경기도고양시\n",
       "20           1   경기도과천시\n",
       "..         ...      ...\n",
       "225          1  충청북도증평군\n",
       "226          1  충청북도진천군\n",
       "227          1  충청북도청원군\n",
       "228          1  충청북도청주시\n",
       "229          1  충청북도충주시\n",
       "\n",
       "[116 rows x 2 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.iloc[:,0:3:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 X,y로 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 2:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train,test, validation 분리, linear regression 제외하고 모두 이 train test val을 쓸 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,stratify = y, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, stratify = y_train, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(129, 11) (43, 11) (58, 11) (129,) (43,) (58,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_val.shape ,X_test.shape, y_train.shape, y_val.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train, test, val에서 region colunm 따로 빼서 list저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_train_region = X_train.pop(\"region\")\n",
    "x_test_region = X_test.pop(\"region\")\n",
    "x_val_region = X_val.pop(\"region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_region = list(x_train_region)\n",
    "x_test_region = list(x_test_region)\n",
    "x_val_region = list(x_val_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최적의 하이퍼파라미터 max_depth, min_samples_leaf 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "training_accuracy = []\n",
    "val_accuracy = []\n",
    "\n",
    "msl_settings = [3, 5, 7,8, 9, 10, 20]\n",
    "md_settings = [1,2, 5, 7, 13, 20, 30]\n",
    "for msl in msl_settings:\n",
    "    for md in md_settings:\n",
    "        clf = DecisionTreeClassifier(max_depth =md, min_samples_leaf = msl, random_state =0)\n",
    "        clf.fit(X_train, y_train)\n",
    "        y_train_hat = clf.predict(X_train)\n",
    "        y_val_hat = clf.predict(X_val)\n",
    "        \n",
    "        training_accuracy.append(accuracy_score(y_train, y_train_hat))\n",
    "        val_accuracy.append(accuracy_score(y_val, y_val_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>training accuracy</th>\n",
       "      <th>validation accuracy</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
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       "      <td>0.844961</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.937984</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.937984</td>\n",
       "      <td>0.837209</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0.937984</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.813953</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.813953</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_samples_leaf  max_depth  training accuracy  validation accuracy\n",
       "0                  3          1           0.751938             0.720930\n",
       "1                  3          2           0.844961             0.790698\n",
       "2                  3          5           0.914729             0.813953\n",
       "3                  3          7           0.937984             0.837209\n",
       "4                  3         13           0.937984             0.837209\n",
       "5                  3         20           0.937984             0.837209\n",
       "6                  3         30           0.937984             0.837209\n",
       "7                  5          1           0.751938             0.720930\n",
       "8                  5          2           0.844961             0.790698\n",
       "9                  5          5           0.906977             0.813953\n",
       "10                 5          7           0.906977             0.813953\n",
       "11                 5         13           0.906977             0.813953\n",
       "12                 5         20           0.906977             0.813953\n",
       "13                 5         30           0.906977             0.813953\n",
       "14                 7          1           0.751938             0.720930\n",
       "15                 7          2           0.844961             0.790698\n",
       "16                 7          5           0.891473             0.744186\n",
       "17                 7          7           0.891473             0.744186\n",
       "18                 7         13           0.891473             0.744186\n",
       "19                 7         20           0.891473             0.744186\n",
       "20                 7         30           0.891473             0.744186\n",
       "21                 8          1           0.751938             0.720930\n",
       "22                 8          2           0.844961             0.790698\n",
       "23                 8          5           0.868217             0.790698\n",
       "24                 8          7           0.868217             0.790698\n",
       "25                 8         13           0.868217             0.790698\n",
       "26                 8         20           0.868217             0.790698\n",
       "27                 8         30           0.868217             0.790698\n",
       "28                 9          1           0.751938             0.720930\n",
       "29                 9          2           0.844961             0.790698\n",
       "30                 9          5           0.868217             0.790698\n",
       "31                 9          7           0.868217             0.790698\n",
       "32                 9         13           0.868217             0.790698\n",
       "33                 9         20           0.868217             0.790698\n",
       "34                 9         30           0.868217             0.790698\n",
       "35                10          1           0.751938             0.720930\n",
       "36                10          2           0.844961             0.790698\n",
       "37                10          5           0.860465             0.790698\n",
       "38                10          7           0.860465             0.790698\n",
       "39                10         13           0.860465             0.790698\n",
       "40                10         20           0.860465             0.790698\n",
       "41                10         30           0.860465             0.790698\n",
       "42                20          1           0.751938             0.720930\n",
       "43                20          2           0.844961             0.790698\n",
       "44                20          5           0.844961             0.790698\n",
       "45                20          7           0.844961             0.790698\n",
       "46                20         13           0.844961             0.790698\n",
       "47                20         20           0.844961             0.790698\n",
       "48                20         30           0.844961             0.790698"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"min_samples_leaf\":sorted(msl_settings*7), \"max_depth\":md_settings*7, \"training accuracy\": training_accuracy, \"validation accuracy\": val_accuracy})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최종 모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=7, min_samples_leaf=10, random_state=0)"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth =7, min_samples_leaf = 10, random_state =0)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_hat = clf.predict(X_train)\n",
    "y_val_hat = clf.predict(X_val)\n",
    "y_test_hat = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_dt = []\n",
    "for i in y_test_hat:\n",
    "    predict_dt.append(i)\n",
    "actual_value_dt = []\n",
    "for i in y_test:\n",
    "    actual_value_dt.append(i)\n",
    "\n",
    "meta_data = list(zip(x_test_region,predict_dt, actual_value_dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "DT = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>fine_dust_predict</th>\n",
       "      <th>fine_dust_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전라북도정읍시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>부산광역시연제구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광주광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경상남도밀양시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>강원도홍천군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>울산광역시울주군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>전라북도임실군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>경기도과천시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경상북도상주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>부산광역시해운대구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청북도청주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>전라남도순천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>경상북도구미시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경상남도고성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>경상남도하동군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>전라남도진도군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>충청남도서산시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>전라북도고창군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>부산광역시강서구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>충청북도영동군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>전라남도해남군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>대전광역시유성구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>강원도횡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>경상북도청송군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>인천광역시남동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>전라남도고흥군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도김천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>경기도수원시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>강원도철원군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>경기도용인시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>전라남도여수시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>대전광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도김제시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>부산광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>충청남도태안군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>서울특별시성동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경기도평택시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>경상남도창녕군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  fine_dust_predict  fine_dust_value\n",
       "0      전라북도정읍시                  1                0\n",
       "1     부산광역시연제구                  0                0\n",
       "2     광주광역시남 구                  0                0\n",
       "3      경상남도밀양시                  0                0\n",
       "4       경기도광주시                  1                1\n",
       "5       강원도홍천군                  0                1\n",
       "6     울산광역시울주군                  0                0\n",
       "7      전라북도임실군                  0                1\n",
       "8       경기도과천시                  0                1\n",
       "9      경상북도상주시                  1                1\n",
       "10      강원도고성군                  0                0\n",
       "11      경기도부천시                  1                1\n",
       "12  제주특별자치도제주시                  0                0\n",
       "13   부산광역시해운대구                  0                0\n",
       "14     충청북도청주시                  1                1\n",
       "15    인천광역시동 구                  1                1\n",
       "16    대구광역시남 구                  0                1\n",
       "17     경상북도고령군                  0                0\n",
       "18    인천광역시옹진군                  1                1\n",
       "19     전라남도순천시                  0                0\n",
       "20     전라북도순창군                  0                1\n",
       "21     경상북도구미시                  1                1\n",
       "22     충청남도예산군                  1                1\n",
       "23     경상남도고성군                  0                0\n",
       "24     경상남도하동군                  0                0\n",
       "25     전라남도진도군                  0                0\n",
       "26     충청남도서산시                  1                1\n",
       "27     전라북도고창군                  0                0\n",
       "28    부산광역시강서구                  0                1\n",
       "29    서울특별시관악구                  1                0\n",
       "30     충청북도영동군                  1                0\n",
       "31     경상남도김해시                  0                0\n",
       "32     전라남도해남군                  0                0\n",
       "33    대전광역시유성구                  1                0\n",
       "34   서울특별시영등포구                  1                1\n",
       "35      강원도횡성군                  0                0\n",
       "36     경상북도청송군                  0                0\n",
       "37    인천광역시남동구                  1                1\n",
       "38     전라북도장수군                  0                0\n",
       "39     전라남도고흥군                  0                0\n",
       "40     경상북도김천시                  1                1\n",
       "41     경상북도칠곡군                  1                1\n",
       "42      강원도동해시                  0                0\n",
       "43     전라남도곡성군                  0                0\n",
       "44    대구광역시서 구                  0                1\n",
       "45    대전광역시중 구                  1                1\n",
       "46     충청북도충주시                  1                1\n",
       "47      경기도수원시                  1                1\n",
       "48      강원도철원군                  0                1\n",
       "49      경기도용인시                  1                1\n",
       "50     전라남도여수시                  0                0\n",
       "51    대전광역시동 구                  1                0\n",
       "52     전라북도김제시                  1                1\n",
       "53    부산광역시남 구                  0                0\n",
       "54     충청남도태안군                  1                1\n",
       "55    서울특별시성동구                  1                1\n",
       "56      경기도평택시                  1                1\n",
       "57     경상남도창녕군                  0                0"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.86\n",
      "Accuracy on test set: 0.78\n",
      "[[24  5]\n",
      " [ 8 21]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(\"Accuracy on training set: {:.2f}\".format(accuracy_score(y_train, y_train_hat)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(accuracy_score(y_test, y_test_hat)))\n",
    "print(confusion_matrix(y_test, y_test_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시각화1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5416666666666666, 0.9, 'X[7] <= -16.994\\ngini = 0.5\\nsamples = 129\\nvalue = [64, 65]'),\n",
       " Text(0.3333333333333333, 0.7, 'X[4] <= 82190.0\\ngini = 0.421\\nsamples = 83\\nvalue = [25, 58]'),\n",
       " Text(0.16666666666666666, 0.5, 'X[6] <= 3.72\\ngini = 0.393\\nsamples = 26\\nvalue = [19, 7]'),\n",
       " Text(0.08333333333333333, 0.3, 'gini = 0.48\\nsamples = 10\\nvalue = [4, 6]'),\n",
       " Text(0.25, 0.3, 'gini = 0.117\\nsamples = 16\\nvalue = [15, 1]'),\n",
       " Text(0.5, 0.5, 'X[0] <= 0.539\\ngini = 0.188\\nsamples = 57\\nvalue = [6, 51]'),\n",
       " Text(0.4166666666666667, 0.3, 'gini = 0.42\\nsamples = 10\\nvalue = [3, 7]'),\n",
       " Text(0.5833333333333334, 0.3, 'X[8] <= 55.425\\ngini = 0.12\\nsamples = 47\\nvalue = [3, 44]'),\n",
       " Text(0.5, 0.1, 'gini = 0.0\\nsamples = 28\\nvalue = [0, 28]'),\n",
       " Text(0.6666666666666666, 0.1, 'gini = 0.266\\nsamples = 19\\nvalue = [3, 16]'),\n",
       " Text(0.75, 0.7, 'X[8] <= 49.15\\ngini = 0.258\\nsamples = 46\\nvalue = [39, 7]'),\n",
       " Text(0.6666666666666666, 0.5, 'gini = 0.432\\nsamples = 19\\nvalue = [13, 6]'),\n",
       " Text(0.8333333333333334, 0.5, 'X[2] <= 468.5\\ngini = 0.071\\nsamples = 27\\nvalue = [26, 1]'),\n",
       " Text(0.75, 0.3, 'gini = 0.0\\nsamples = 17\\nvalue = [17, 0]'),\n",
       " Text(0.9166666666666666, 0.3, 'gini = 0.18\\nsamples = 10\\nvalue = [9, 1]')]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "tree.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시각화2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X.drop(['region'], axis=1, inplace=True)\n",
    "from sklearn.tree import export_graphviz\n",
    "export_graphviz(clf, out_file=\"tree.dot\", class_names=[\"Bad\", \"Good\"],\n",
    "                feature_names=X.columns, impurity=False, filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n -->\r\n<!-- Title: Tree Pages: 1 -->\r\n<svg width=\"824pt\" height=\"477pt\"\r\n viewBox=\"0.00 0.00 824.00 477.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 473)\">\r\n<title>Tree</title>\r\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-473 820,-473 820,4 -4,4\"/>\r\n<!-- 0 -->\r\n<g id=\"node1\" class=\"node\"><title>0</title>\r\n<polygon fill=\"#fcfdff\" stroke=\"black\" points=\"506,-469 325,-469 325,-401 506,-401 506,-469\"/>\r\n<text text-anchor=\"middle\" x=\"415.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">wind_direction &lt;= &#45;16.994</text>\r\n<text text-anchor=\"middle\" x=\"415.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 129</text>\r\n<text text-anchor=\"middle\" x=\"415.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [64, 65]</text>\r\n<text text-anchor=\"middle\" x=\"415.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 1 -->\r\n<g id=\"node2\" class=\"node\"><title>1</title>\r\n<polygon fill=\"#8ec7f0\" stroke=\"black\" points=\"417.5,-365 223.5,-365 223.5,-297 417.5,-297 417.5,-365\"/>\r\n<text text-anchor=\"middle\" x=\"320.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">num_of_people &lt;= 82190.0</text>\r\n<text text-anchor=\"middle\" x=\"320.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 83</text>\r\n<text text-anchor=\"middle\" x=\"320.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [25, 58]</text>\r\n<text text-anchor=\"middle\" x=\"320.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 0&#45;&gt;1 -->\r\n<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M384.656,-400.884C376.277,-391.887 367.106,-382.041 358.387,-372.678\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"360.891,-370.232 351.514,-365.299 355.768,-375.003 360.891,-370.232\"/>\r\n<text text-anchor=\"middle\" x=\"350.626\" y=\"-386.584\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\r\n</g>\r\n<!-- 10 -->\r\n<g id=\"node11\" class=\"node\"><title>10</title>\r\n<polygon fill=\"#ea985d\" stroke=\"black\" points=\"587,-365 436,-365 436,-297 587,-297 587,-365\"/>\r\n<text text-anchor=\"middle\" x=\"511.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">rainfall(mm) &lt;= 49.15</text>\r\n<text text-anchor=\"middle\" x=\"511.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\r\n<text text-anchor=\"middle\" x=\"511.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [39, 7]</text>\r\n<text text-anchor=\"middle\" x=\"511.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 0&#45;&gt;10 -->\r\n<g id=\"edge10\" class=\"edge\"><title>0&#45;&gt;10</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M446.668,-400.884C455.136,-391.887 464.403,-382.041 473.215,-372.678\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"475.854,-374.98 480.159,-365.299 470.757,-370.183 475.854,-374.98\"/>\r\n<text text-anchor=\"middle\" x=\"480.917\" y=\"-386.588\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n</g>\r\n<!-- 2 -->\r\n<g id=\"node3\" class=\"node\"><title>2</title>\r\n<polygon fill=\"#efaf82\" stroke=\"black\" points=\"234.5,-261 88.5,-261 88.5,-193 234.5,-193 234.5,-261\"/>\r\n<text text-anchor=\"middle\" x=\"161.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">wind_speed &lt;= 3.72</text>\r\n<text text-anchor=\"middle\" x=\"161.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 26</text>\r\n<text text-anchor=\"middle\" x=\"161.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [19, 7]</text>\r\n<text text-anchor=\"middle\" x=\"161.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 1&#45;&gt;2 -->\r\n<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M268.877,-296.884C253.805,-287.214 237.202,-276.563 221.651,-266.587\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"223.442,-263.578 213.136,-261.125 219.663,-269.47 223.442,-263.578\"/>\r\n</g>\r\n<!-- 5 -->\r\n<g id=\"node6\" class=\"node\"><title>5</title>\r\n<polygon fill=\"#50a9e8\" stroke=\"black\" points=\"410,-261 253,-261 253,-193 410,-193 410,-261\"/>\r\n<text text-anchor=\"middle\" x=\"331.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">heavy_metal &lt;= 0.539</text>\r\n<text text-anchor=\"middle\" x=\"331.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 57</text>\r\n<text text-anchor=\"middle\" x=\"331.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 51]</text>\r\n<text text-anchor=\"middle\" x=\"331.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 1&#45;&gt;5 -->\r\n<g id=\"edge5\" class=\"edge\"><title>1&#45;&gt;5</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M324.071,-296.884C324.946,-288.778 325.894,-279.982 326.812,-271.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"330.316,-271.617 327.909,-261.299 323.357,-270.866 330.316,-271.617\"/>\r\n</g>\r\n<!-- 3 -->\r\n<g id=\"node4\" class=\"node\"><title>3</title>\r\n<polygon fill=\"#bddef6\" stroke=\"black\" points=\"103,-149.5 0,-149.5 0,-96.5 103,-96.5 103,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 6]</text>\r\n<text text-anchor=\"middle\" x=\"51.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 2&#45;&gt;3 -->\r\n<g id=\"edge3\" class=\"edge\"><title>2&#45;&gt;3</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M125.786,-192.884C113.214,-181.226 99.1026,-168.141 86.583,-156.532\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"88.734,-153.753 79.0215,-149.52 83.9744,-158.886 88.734,-153.753\"/>\r\n</g>\r\n<!-- 4 -->\r\n<g id=\"node5\" class=\"node\"><title>4</title>\r\n<polygon fill=\"#e78946\" stroke=\"black\" points=\"226,-149.5 121,-149.5 121,-96.5 226,-96.5 226,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"173.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\r\n<text text-anchor=\"middle\" x=\"173.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [15, 1]</text>\r\n<text text-anchor=\"middle\" x=\"173.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 2&#45;&gt;4 -->\r\n<g id=\"edge4\" class=\"edge\"><title>2&#45;&gt;4</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M165.396,-192.884C166.651,-182.216 168.047,-170.352 169.321,-159.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"172.805,-159.86 170.498,-149.52 165.853,-159.042 172.805,-159.86\"/>\r\n</g>\r\n<!-- 6 -->\r\n<g id=\"node7\" class=\"node\"><title>6</title>\r\n<polygon fill=\"#8ec7f0\" stroke=\"black\" points=\"371,-149.5 268,-149.5 268,-96.5 371,-96.5 371,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"319.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n<text text-anchor=\"middle\" x=\"319.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 7]</text>\r\n<text text-anchor=\"middle\" x=\"319.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 5&#45;&gt;6 -->\r\n<g id=\"edge6\" class=\"edge\"><title>5&#45;&gt;6</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M327.604,-192.884C326.349,-182.216 324.953,-170.352 323.679,-159.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"327.147,-159.042 322.502,-149.52 320.195,-159.86 327.147,-159.042\"/>\r\n</g>\r\n<!-- 7 -->\r\n<g id=\"node8\" class=\"node\"><title>7</title>\r\n<polygon fill=\"#46a4e7\" stroke=\"black\" points=\"548,-157 389,-157 389,-89 548,-89 548,-157\"/>\r\n<text text-anchor=\"middle\" x=\"468.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">rainfall(mm) &lt;= 55.425</text>\r\n<text text-anchor=\"middle\" x=\"468.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\r\n<text text-anchor=\"middle\" x=\"468.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 44]</text>\r\n<text text-anchor=\"middle\" x=\"468.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 5&#45;&gt;7 -->\r\n<g id=\"edge7\" class=\"edge\"><title>5&#45;&gt;7</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M375.98,-192.884C388.662,-183.442 402.601,-173.064 415.726,-163.292\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"417.844,-166.079 423.774,-157.299 413.663,-160.464 417.844,-166.079\"/>\r\n</g>\r\n<!-- 8 -->\r\n<g id=\"node9\" class=\"node\"><title>8</title>\r\n<polygon fill=\"#399de5\" stroke=\"black\" points=\"459,-53 354,-53 354,-0 459,-0 459,-53\"/>\r\n<text text-anchor=\"middle\" x=\"406.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 28</text>\r\n<text text-anchor=\"middle\" x=\"406.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 28]</text>\r\n<text text-anchor=\"middle\" x=\"406.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 7&#45;&gt;8 -->\r\n<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M446.815,-88.9485C441.054,-80.1664 434.819,-70.6629 429.014,-61.815\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"431.797,-59.6779 423.385,-53.2367 425.945,-63.5178 431.797,-59.6779\"/>\r\n</g>\r\n<!-- 9 -->\r\n<g id=\"node10\" class=\"node\"><title>9</title>\r\n<polygon fill=\"#5eafea\" stroke=\"black\" points=\"582,-53 477,-53 477,-0 582,-0 582,-53\"/>\r\n<text text-anchor=\"middle\" x=\"529.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 19</text>\r\n<text text-anchor=\"middle\" x=\"529.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 16]</text>\r\n<text text-anchor=\"middle\" x=\"529.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 7&#45;&gt;9 -->\r\n<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M489.835,-88.9485C495.504,-80.1664 501.638,-70.6629 507.35,-61.815\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"510.404,-63.5365 512.887,-53.2367 504.523,-59.7402 510.404,-63.5365\"/>\r\n</g>\r\n<!-- 11 -->\r\n<g id=\"node12\" class=\"node\"><title>11</title>\r\n<polygon fill=\"#f1bb94\" stroke=\"black\" points=\"554,-253.5 449,-253.5 449,-200.5 554,-200.5 554,-253.5\"/>\r\n<text text-anchor=\"middle\" x=\"501.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 19</text>\r\n<text text-anchor=\"middle\" x=\"501.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 6]</text>\r\n<text text-anchor=\"middle\" x=\"501.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 10&#45;&gt;11 -->\r\n<g id=\"edge11\" class=\"edge\"><title>10&#45;&gt;11</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M508.253,-296.884C507.207,-286.216 506.044,-274.352 504.982,-263.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"508.461,-263.131 504.002,-253.52 501.494,-263.814 508.461,-263.131\"/>\r\n</g>\r\n<!-- 12 -->\r\n<g id=\"node13\" class=\"node\"><title>12</title>\r\n<polygon fill=\"#e68641\" stroke=\"black\" points=\"760.5,-261 572.5,-261 572.5,-193 760.5,-193 760.5,-261\"/>\r\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">num_of_factories &lt;= 468.5</text>\r\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 27</text>\r\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [26, 1]</text>\r\n<text text-anchor=\"middle\" x=\"666.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 10&#45;&gt;12 -->\r\n<g id=\"edge12\" class=\"edge\"><title>10&#45;&gt;12</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M561.824,-296.884C576.381,-287.304 592.403,-276.761 607.441,-266.864\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"609.734,-269.546 616.163,-261.125 605.886,-263.698 609.734,-269.546\"/>\r\n</g>\r\n<!-- 13 -->\r\n<g id=\"node14\" class=\"node\"><title>13</title>\r\n<polygon fill=\"#e58139\" stroke=\"black\" points=\"695,-149.5 590,-149.5 590,-96.5 695,-96.5 695,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"642.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17</text>\r\n<text text-anchor=\"middle\" x=\"642.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [17, 0]</text>\r\n<text text-anchor=\"middle\" x=\"642.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 12&#45;&gt;13 -->\r\n<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M658.708,-192.884C656.198,-182.216 653.406,-170.352 650.857,-159.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"654.202,-158.452 648.505,-149.52 647.388,-160.056 654.202,-158.452\"/>\r\n</g>\r\n<!-- 14 -->\r\n<g id=\"node15\" class=\"node\"><title>14</title>\r\n<polygon fill=\"#e88f4f\" stroke=\"black\" points=\"816,-149.5 713,-149.5 713,-96.5 816,-96.5 816,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"764.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n<text text-anchor=\"middle\" x=\"764.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 1]</text>\r\n<text text-anchor=\"middle\" x=\"764.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 12&#45;&gt;14 -->\r\n<g id=\"edge14\" class=\"edge\"><title>12&#45;&gt;14</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M698.318,-192.884C709.413,-181.336 721.853,-168.387 732.928,-156.86\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"735.576,-159.156 739.981,-149.52 730.529,-154.306 735.576,-159.156\"/>\r\n</g>\r\n</g>\r\n</svg>\r\n",
      "text/plain": [
       "<graphviz.files.Source at 0x248167ca248>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import graphviz\n",
    "with open(\"tree.dot\") as f:\n",
    "    dot_graph = f.read()\n",
    "display(graphviz.Source(dot_graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importance 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.024 0.    0.003 0.    0.362 0.    0.092 0.459 0.06  0.   ]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importances:\")\n",
    "print(clf.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['heavy_metal', 'traffic', 'num_of_factories', 'num_of_power_plants',\n",
       "       'num_of_people', 'household_waste_discharge_per_person (kg/days)',\n",
       "       'wind_speed', 'wind_direction', 'rainfall(mm)', 'C'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importances_cancer(model):\n",
    "    n_features = X.shape[1]\n",
    "    plt.barh(np.arange(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), X.columns)\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.ylim(-1, n_features)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plot_feature_importances_cancer(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM-StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_val_scaled = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature magnitude')"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X_train_scaled, manage_ticks=False)\n",
    "plt.yscale(\"symlog\")\n",
    "plt.xlabel(\"Feature index\")\n",
    "plt.ylabel(\"Feature magnitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최적의 하이퍼파라미터 C, gamma 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "training_accuracy = []\n",
    "val_accuracy = []\n",
    "\n",
    "C_settings = [0.01, 1, 10, 100, 1000]\n",
    "gamma_settings = [0.01, 0.1, 1, 10, 100]\n",
    "for C in C_settings:\n",
    "    for gamma in gamma_settings:\n",
    "        svc = SVC(C=C, kernel = 'rbf', gamma = gamma, random_state = 0)\n",
    "        svc.fit(X_train_scaled, y_train)\n",
    "        \n",
    "        y_train_hat = svc.predict(X_train_scaled)\n",
    "        y_val_hat = svc.predict(X_val_scaled)\n",
    "        \n",
    "        training_accuracy.append(accuracy_score(y_train, y_train_hat))\n",
    "        val_accuracy.append(accuracy_score(y_val, y_val_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>gamma</th>\n",
       "      <th>training accuracy</th>\n",
       "      <th>validation accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.775194</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.968992</td>\n",
       "      <td>0.674419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.744186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C   gamma  training accuracy  validation accuracy\n",
       "0      0.01    0.01           0.503876             0.511628\n",
       "1      0.01    0.10           0.503876             0.511628\n",
       "2      0.01    1.00           0.503876             0.511628\n",
       "3      0.01   10.00           0.503876             0.511628\n",
       "4      0.01  100.00           0.503876             0.511628\n",
       "5      1.00    0.01           0.775194             0.837209\n",
       "6      1.00    0.10           0.860465             0.813953\n",
       "7      1.00    1.00           0.968992             0.674419\n",
       "8      1.00   10.00           1.000000             0.581395\n",
       "9      1.00  100.00           1.000000             0.511628\n",
       "10    10.00    0.01           0.829457             0.813953\n",
       "11    10.00    0.10           0.930233             0.813953\n",
       "12    10.00    1.00           1.000000             0.767442\n",
       "13    10.00   10.00           1.000000             0.604651\n",
       "14    10.00  100.00           1.000000             0.511628\n",
       "15   100.00    0.01           0.875969             0.837209\n",
       "16   100.00    0.10           0.976744             0.744186\n",
       "17   100.00    1.00           1.000000             0.744186\n",
       "18   100.00   10.00           1.000000             0.604651\n",
       "19   100.00  100.00           1.000000             0.511628\n",
       "20  1000.00    0.01           0.930233             0.767442\n",
       "21  1000.00    0.10           1.000000             0.813953\n",
       "22  1000.00    1.00           1.000000             0.744186\n",
       "23  1000.00   10.00           1.000000             0.604651\n",
       "24  1000.00  100.00           1.000000             0.511628"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"C\":sorted(C_settings*5), \"gamma\":gamma_settings*5, \"training accuracy\": training_accuracy, \"validation accuracy\": val_accuracy})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최종 모델링 후 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.8294573643410853\n",
      "Accuracy on testing set:  0.7413793103448276\n",
      "[[20  9]\n",
      " [ 6 23]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "svc = SVC(C=10, kernel = 'rbf', gamma = 0.01)\n",
    "svc.fit(X_train_scaled, y_train)\n",
    "        \n",
    "y_train_hat = svc.predict(X_train_scaled)\n",
    "y_test_hat = svc.predict(X_test_scaled)\n",
    "\n",
    "print(\"Accuracy on training set: \", accuracy_score(y_train, y_train_hat))\n",
    "print(\"Accuracy on testing set: \", accuracy_score(y_test, y_test_hat))\n",
    "print(confusion_matrix(y_test, y_test_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지역별 예측값 실제값 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>fine_dust_predict</th>\n",
       "      <th>fine_dust_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전라북도정읍시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>부산광역시연제구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광주광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경상남도밀양시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>강원도홍천군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>울산광역시울주군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>전라북도임실군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>경기도과천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경상북도상주시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>부산광역시해운대구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청북도청주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>전라남도순천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>경상북도구미시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경상남도고성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>경상남도하동군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>전라남도진도군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>충청남도서산시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>전라북도고창군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>부산광역시강서구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>충청북도영동군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>전라남도해남군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>대전광역시유성구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>강원도횡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>경상북도청송군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>인천광역시남동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>전라남도고흥군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도김천시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시서 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>경기도수원시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>강원도철원군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>경기도용인시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>전라남도여수시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>대전광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도김제시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>부산광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>충청남도태안군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>서울특별시성동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경기도평택시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>경상남도창녕군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  fine_dust_predict  fine_dust_value\n",
       "0      전라북도정읍시                  0                0\n",
       "1     부산광역시연제구                  0                0\n",
       "2     광주광역시남 구                  0                0\n",
       "3      경상남도밀양시                  0                0\n",
       "4       경기도광주시                  1                1\n",
       "5       강원도홍천군                  1                1\n",
       "6     울산광역시울주군                  1                0\n",
       "7      전라북도임실군                  0                1\n",
       "8       경기도과천시                  1                1\n",
       "9      경상북도상주시                  0                1\n",
       "10      강원도고성군                  1                0\n",
       "11      경기도부천시                  1                1\n",
       "12  제주특별자치도제주시                  0                0\n",
       "13   부산광역시해운대구                  0                0\n",
       "14     충청북도청주시                  1                1\n",
       "15    인천광역시동 구                  1                1\n",
       "16    대구광역시남 구                  0                1\n",
       "17     경상북도고령군                  0                0\n",
       "18    인천광역시옹진군                  1                1\n",
       "19     전라남도순천시                  0                0\n",
       "20     전라북도순창군                  0                1\n",
       "21     경상북도구미시                  1                1\n",
       "22     충청남도예산군                  1                1\n",
       "23     경상남도고성군                  1                0\n",
       "24     경상남도하동군                  1                0\n",
       "25     전라남도진도군                  0                0\n",
       "26     충청남도서산시                  1                1\n",
       "27     전라북도고창군                  0                0\n",
       "28    부산광역시강서구                  1                1\n",
       "29    서울특별시관악구                  1                0\n",
       "30     충청북도영동군                  1                0\n",
       "31     경상남도김해시                  1                0\n",
       "32     전라남도해남군                  0                0\n",
       "33    대전광역시유성구                  1                0\n",
       "34   서울특별시영등포구                  1                1\n",
       "35      강원도횡성군                  0                0\n",
       "36     경상북도청송군                  0                0\n",
       "37    인천광역시남동구                  1                1\n",
       "38     전라북도장수군                  0                0\n",
       "39     전라남도고흥군                  0                0\n",
       "40     경상북도김천시                  0                1\n",
       "41     경상북도칠곡군                  0                1\n",
       "42      강원도동해시                  0                0\n",
       "43     전라남도곡성군                  0                0\n",
       "44    대구광역시서 구                  1                1\n",
       "45    대전광역시중 구                  1                1\n",
       "46     충청북도충주시                  1                1\n",
       "47      경기도수원시                  1                1\n",
       "48      강원도철원군                  1                1\n",
       "49      경기도용인시                  1                1\n",
       "50     전라남도여수시                  0                0\n",
       "51    대전광역시동 구                  1                0\n",
       "52     전라북도김제시                  1                1\n",
       "53    부산광역시남 구                  0                0\n",
       "54     충청남도태안군                  1                1\n",
       "55    서울특별시성동구                  1                1\n",
       "56      경기도평택시                  1                1\n",
       "57     경상남도창녕군                  0                0"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_svms = []\n",
    "for i in y_test_hat:\n",
    "    predict_svms.append(i)\n",
    "actual_value_svms = []\n",
    "for i in y_test:\n",
    "    actual_value_svms.append(i)\n",
    "\n",
    "meta_data = list(zip(x_test_region,predict_svms, actual_value_svms))\n",
    "\n",
    "SVMS = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])\n",
    "SVMS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM-MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature magnitude')"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_val_scaled = scaler.transform(X_val)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "plt.boxplot(X_train_scaled, manage_ticks=False)\n",
    "plt.yscale(\"symlog\")\n",
    "plt.xlabel(\"Feature index\")\n",
    "plt.ylabel(\"Feature magnitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최적의 하이퍼파라미터 C, gamma 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_accuracy = []\n",
    "val_accuracy = []\n",
    "\n",
    "C_settings = [0.01, 0.1, 1, 10, 100]\n",
    "gamma_settings = [0.01, 0.1, 1, 10, 100]\n",
    "for C in C_settings:\n",
    "    for gamma in gamma_settings:\n",
    "        svc = SVC(C=C, kernel = 'rbf', gamma = gamma, random_state=0)\n",
    "        svc.fit(X_train_scaled, y_train)\n",
    "        \n",
    "        y_train_hat = svc.predict(X_train_scaled)\n",
    "        y_val_hat = svc.predict(X_val_scaled)\n",
    "        \n",
    "        training_accuracy.append(accuracy_score(y_train, y_train_hat))\n",
    "        val_accuracy.append(accuracy_score(y_val, y_val_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>gamma</th>\n",
       "      <th>training accuracy</th>\n",
       "      <th>validation accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.10</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.503876</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.651163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.713178</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.868217</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.968992</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.674419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>100.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>100.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.674419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C   gamma  training accuracy  validation accuracy\n",
       "0     0.01    0.01           0.503876             0.511628\n",
       "1     0.01    0.10           0.503876             0.511628\n",
       "2     0.01    1.00           0.503876             0.511628\n",
       "3     0.01   10.00           0.503876             0.511628\n",
       "4     0.01  100.00           0.503876             0.511628\n",
       "5     0.10    0.01           0.503876             0.511628\n",
       "6     0.10    0.10           0.503876             0.511628\n",
       "7     0.10    1.00           0.767442             0.790698\n",
       "8     0.10   10.00           0.837209             0.767442\n",
       "9     0.10  100.00           0.503876             0.511628\n",
       "10    1.00    0.01           0.503876             0.511628\n",
       "11    1.00    0.10           0.720930             0.790698\n",
       "12    1.00    1.00           0.829457             0.790698\n",
       "13    1.00   10.00           0.891473             0.813953\n",
       "14    1.00  100.00           0.976744             0.651163\n",
       "15   10.00    0.01           0.713178             0.790698\n",
       "16   10.00    0.10           0.798450             0.813953\n",
       "17   10.00    1.00           0.868217             0.790698\n",
       "18   10.00   10.00           0.968992             0.767442\n",
       "19   10.00  100.00           1.000000             0.674419\n",
       "20  100.00    0.01           0.782946             0.813953\n",
       "21  100.00    0.10           0.798450             0.813953\n",
       "22  100.00    1.00           0.930233             0.813953\n",
       "23  100.00   10.00           1.000000             0.813953\n",
       "24  100.00  100.00           1.000000             0.674419"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"C\":sorted(C_settings*5), \"gamma\":gamma_settings*5, \"training accuracy\": training_accuracy, \"validation accuracy\": val_accuracy})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최종 모델링 후 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.7984496124031008\n",
      "Accuracy on testing set:  0.7241379310344828\n",
      "[[22  7]\n",
      " [ 9 20]]\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(C=10, kernel = 'rbf', gamma = 0.1)\n",
    "svc.fit(X_train_scaled, y_train)\n",
    "        \n",
    "y_train_hat = svc.predict(X_train_scaled)\n",
    "y_test_hat = svc.predict(X_test_scaled)\n",
    "\n",
    "print(\"Accuracy on training set: \", accuracy_score(y_train, y_train_hat))\n",
    "print(\"Accuracy on testing set: \", accuracy_score(y_test, y_test_hat))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_test_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지역별 예측값 실제값 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>fine_dust_predict</th>\n",
       "      <th>fine_dust_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전라북도정읍시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>부산광역시연제구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광주광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경상남도밀양시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>강원도홍천군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>울산광역시울주군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>전라북도임실군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>경기도과천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경상북도상주시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>부산광역시해운대구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청북도청주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>전라남도순천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>경상북도구미시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경상남도고성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>경상남도하동군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>전라남도진도군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>충청남도서산시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>전라북도고창군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>부산광역시강서구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>충청북도영동군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>전라남도해남군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>대전광역시유성구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>강원도횡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>경상북도청송군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>인천광역시남동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>전라남도고흥군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도김천시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>경기도수원시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>강원도철원군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>경기도용인시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>전라남도여수시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>대전광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도김제시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>부산광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>충청남도태안군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>서울특별시성동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경기도평택시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>경상남도창녕군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  fine_dust_predict  fine_dust_value\n",
       "0      전라북도정읍시                  0                0\n",
       "1     부산광역시연제구                  0                0\n",
       "2     광주광역시남 구                  0                0\n",
       "3      경상남도밀양시                  0                0\n",
       "4       경기도광주시                  1                1\n",
       "5       강원도홍천군                  1                1\n",
       "6     울산광역시울주군                  1                0\n",
       "7      전라북도임실군                  0                1\n",
       "8       경기도과천시                  1                1\n",
       "9      경상북도상주시                  0                1\n",
       "10      강원도고성군                  1                0\n",
       "11      경기도부천시                  1                1\n",
       "12  제주특별자치도제주시                  0                0\n",
       "13   부산광역시해운대구                  0                0\n",
       "14     충청북도청주시                  1                1\n",
       "15    인천광역시동 구                  1                1\n",
       "16    대구광역시남 구                  0                1\n",
       "17     경상북도고령군                  0                0\n",
       "18    인천광역시옹진군                  1                1\n",
       "19     전라남도순천시                  0                0\n",
       "20     전라북도순창군                  0                1\n",
       "21     경상북도구미시                  0                1\n",
       "22     충청남도예산군                  1                1\n",
       "23     경상남도고성군                  0                0\n",
       "24     경상남도하동군                  0                0\n",
       "25     전라남도진도군                  0                0\n",
       "26     충청남도서산시                  1                1\n",
       "27     전라북도고창군                  0                0\n",
       "28    부산광역시강서구                  1                1\n",
       "29    서울특별시관악구                  1                0\n",
       "30     충청북도영동군                  1                0\n",
       "31     경상남도김해시                  1                0\n",
       "32     전라남도해남군                  0                0\n",
       "33    대전광역시유성구                  1                0\n",
       "34   서울특별시영등포구                  1                1\n",
       "35      강원도횡성군                  0                0\n",
       "36     경상북도청송군                  0                0\n",
       "37    인천광역시남동구                  1                1\n",
       "38     전라북도장수군                  0                0\n",
       "39     전라남도고흥군                  0                0\n",
       "40     경상북도김천시                  0                1\n",
       "41     경상북도칠곡군                  0                1\n",
       "42      강원도동해시                  0                0\n",
       "43     전라남도곡성군                  0                0\n",
       "44    대구광역시서 구                  0                1\n",
       "45    대전광역시중 구                  1                1\n",
       "46     충청북도충주시                  1                1\n",
       "47      경기도수원시                  1                1\n",
       "48      강원도철원군                  1                1\n",
       "49      경기도용인시                  1                1\n",
       "50     전라남도여수시                  0                0\n",
       "51    대전광역시동 구                  1                0\n",
       "52     전라북도김제시                  0                1\n",
       "53    부산광역시남 구                  0                0\n",
       "54     충청남도태안군                  1                1\n",
       "55    서울특별시성동구                  1                1\n",
       "56      경기도평택시                  1                1\n",
       "57     경상남도창녕군                  0                0"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_svmm = []\n",
    "for i in y_test_hat:\n",
    "    predict_svmm.append(i)\n",
    "actual_value_svmm = []\n",
    "for i in y_test:\n",
    "    actual_value_svmm.append(i)\n",
    "\n",
    "meta_data = list(zip(x_test_region,predict_svmm, actual_value_svmm))\n",
    "\n",
    "SVMM = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])\n",
    "SVMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_val_scaled = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최적의 파라미터 C 서치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "training_accuracy = []\n",
    "val_accuracy = []\n",
    "\n",
    "C_settings = [0.01, 0.1, 1, 10, 100, 1000, 10000]\n",
    "for C in C_settings:\n",
    "    LR = LogisticRegression(C=C, max_iter = 1000, multi_class='ovr')\n",
    "    LR.fit(X_train, y_train)\n",
    "    \n",
    "    y_train_hat = LR.predict(X_train)\n",
    "    training_accuracy.append(accuracy_score(y_train, y_train_hat))\n",
    "    \n",
    "    y_val_hat = LR.predict(X_val)\n",
    "    val_accuracy.append(accuracy_score(y_val, y_val_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>training accuracy</th>\n",
       "      <th>val accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.751938</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000.00</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>0.883721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C  training accuracy  val accuracy\n",
       "0      0.01           0.751938      0.837209\n",
       "1      0.10           0.767442      0.813953\n",
       "2      1.00           0.751938      0.837209\n",
       "3     10.00           0.751938      0.837209\n",
       "4    100.00           0.767442      0.813953\n",
       "5   1000.00           0.767442      0.813953\n",
       "6  10000.00           0.782946      0.883721"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"C\":C_settings, \"training accuracy\": training_accuracy, \"val accuracy\": val_accuracy})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, max_iter=1000)"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LogisticRegression(C=0.01, max_iter = 1000, multi_class='auto')\n",
    "LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 0 0\n",
      " 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "y_test_predict = LR.predict(X_test)\n",
    "print(y_test_predict[0:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.6666666666666666\n",
      "Accuracy on testing set:  0.7413793103448276\n",
      "[[22  7]\n",
      " [ 9 20]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\imgproc\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "y_train_predict = LR.predict(X_train_scaled)\n",
    "y_test_predict = LR.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: \", accuracy_score(y_train, y_train_predict))\n",
    "print(\"Accuracy on testing set: \", accuracy_score(y_test, y_test_predict))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_test_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>fine_dust_predict</th>\n",
       "      <th>fine_dust_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전라북도정읍시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>부산광역시연제구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>광주광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경상남도밀양시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>강원도홍천군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>울산광역시울주군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>전라북도임실군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>경기도과천시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경상북도상주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>부산광역시해운대구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청북도청주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>전라남도순천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>경상북도구미시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경상남도고성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>경상남도하동군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>전라남도진도군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>충청남도서산시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>전라북도고창군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>부산광역시강서구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>충청북도영동군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>전라남도해남군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>대전광역시유성구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>강원도횡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>경상북도청송군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>인천광역시남동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>전라남도고흥군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도김천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>경기도수원시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>강원도철원군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>경기도용인시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>전라남도여수시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>대전광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도김제시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>부산광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>충청남도태안군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>서울특별시성동구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경기도평택시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>경상남도창녕군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        region  fine_dust_predict  fine_dust_value\n",
       "0      전라북도정읍시                  1                0\n",
       "1     부산광역시연제구                  0                0\n",
       "2     광주광역시남 구                  0                0\n",
       "3      경상남도밀양시                  0                0\n",
       "4       경기도광주시                  1                1\n",
       "5       강원도홍천군                  0                1\n",
       "6     울산광역시울주군                  1                0\n",
       "7      전라북도임실군                  1                1\n",
       "8       경기도과천시                  0                1\n",
       "9      경상북도상주시                  1                1\n",
       "10      강원도고성군                  0                0\n",
       "11      경기도부천시                  1                1\n",
       "12  제주특별자치도제주시                  0                0\n",
       "13   부산광역시해운대구                  1                0\n",
       "14     충청북도청주시                  1                1\n",
       "15    인천광역시동 구                  1                1\n",
       "16    대구광역시남 구                  0                1\n",
       "17     경상북도고령군                  1                0\n",
       "18    인천광역시옹진군                  1                1\n",
       "19     전라남도순천시                  0                0\n",
       "20     전라북도순창군                  1                1\n",
       "21     경상북도구미시                  1                1\n",
       "22     충청남도예산군                  1                1\n",
       "23     경상남도고성군                  0                0\n",
       "24     경상남도하동군                  0                0\n",
       "25     전라남도진도군                  0                0\n",
       "26     충청남도서산시                  1                1\n",
       "27     전라북도고창군                  1                0\n",
       "28    부산광역시강서구                  1                1\n",
       "29    서울특별시관악구                  1                0\n",
       "30     충청북도영동군                  0                0\n",
       "31     경상남도김해시                  1                0\n",
       "32     전라남도해남군                  0                0\n",
       "33    대전광역시유성구                  1                0\n",
       "34   서울특별시영등포구                  1                1\n",
       "35      강원도횡성군                  0                0\n",
       "36     경상북도청송군                  0                0\n",
       "37    인천광역시남동구                  1                1\n",
       "38     전라북도장수군                  1                0\n",
       "39     전라남도고흥군                  0                0\n",
       "40     경상북도김천시                  1                1\n",
       "41     경상북도칠곡군                  1                1\n",
       "42      강원도동해시                  0                0\n",
       "43     전라남도곡성군                  0                0\n",
       "44    대구광역시서 구                  0                1\n",
       "45    대전광역시중 구                  1                1\n",
       "46     충청북도충주시                  1                1\n",
       "47      경기도수원시                  1                1\n",
       "48      강원도철원군                  0                1\n",
       "49      경기도용인시                  1                1\n",
       "50     전라남도여수시                  0                0\n",
       "51    대전광역시동 구                  1                0\n",
       "52     전라북도김제시                  1                1\n",
       "53    부산광역시남 구                  0                0\n",
       "54     충청남도태안군                  1                1\n",
       "55    서울특별시성동구                  1                1\n",
       "56      경기도평택시                  1                1\n",
       "57     경상남도창녕군                  0                0"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_LR = []\n",
    "for i in y_test_predict:\n",
    "    predict_LR.append(i)\n",
    "actual_value_svmm = []\n",
    "for i in y_test:\n",
    "    actual_value_svmm.append(i)\n",
    "\n",
    "meta_data = list(zip(x_test_region,predict_LR, actual_value_svmm))\n",
    "\n",
    "SVMM = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])\n",
    "SVMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "회귀계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀계수: [[-0.    -0.014  0.001  0.     0.    -0.    -0.002 -0.019 -0.016 -0.005]]  상수항: [-0.]\n"
     ]
    }
   ],
   "source": [
    "print(\"회귀계수:\", LR.coef_,\" 상수항:\", LR.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 2:13]\n",
    "y = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_region = X_train.pop(\"region\")\n",
    "x_test_region = X_test.pop(\"region\")\n",
    "x_train_region = list(x_train_region)\n",
    "x_test_region = list(x_test_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀계수: [ 2.199 -0.072  0.001  0.457  0.     0.144 -1.238 -0.041 -0.055 -0.709]  상수항: 59.38395133823418\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)\n",
    "y_predict = lr_model.predict(X_test) #numpy array\n",
    "\n",
    "print(\"회귀계수:\", lr_model.coef_,\" 상수항:\", lr_model.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "결정계수: 0.4745643155100302\n"
     ]
    }
   ],
   "source": [
    "R2 = lr_model.score(X_train, y_train)\n",
    "print(\"결정계수:\", R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>fine_dust_predict</th>\n",
       "      <th>fine_dust_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>충청남도홍성군</td>\n",
       "      <td>46.218385</td>\n",
       "      <td>46.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>경상남도합천군</td>\n",
       "      <td>39.118621</td>\n",
       "      <td>30.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강원도인제군</td>\n",
       "      <td>44.895638</td>\n",
       "      <td>34.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>전라북도완주군</td>\n",
       "      <td>41.593626</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>강원도홍천군</td>\n",
       "      <td>44.599627</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>전라북도진안군</td>\n",
       "      <td>41.399238</td>\n",
       "      <td>37.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>경기도김포시</td>\n",
       "      <td>54.721885</td>\n",
       "      <td>54.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>전라북도임실군</td>\n",
       "      <td>42.949791</td>\n",
       "      <td>46.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천광역시강화군</td>\n",
       "      <td>43.270353</td>\n",
       "      <td>41.405270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>서울특별시강북구</td>\n",
       "      <td>44.542651</td>\n",
       "      <td>51.608070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>충청북도괴산군</td>\n",
       "      <td>43.195092</td>\n",
       "      <td>41.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기도고양시</td>\n",
       "      <td>53.842370</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>대구광역시달서구</td>\n",
       "      <td>45.955954</td>\n",
       "      <td>43.002590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>전라남도영광군</td>\n",
       "      <td>35.240637</td>\n",
       "      <td>44.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청북도음성군</td>\n",
       "      <td>46.344400</td>\n",
       "      <td>47.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>서울특별시서대문구</td>\n",
       "      <td>44.861406</td>\n",
       "      <td>40.610980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>서울특별시종로구</td>\n",
       "      <td>44.297744</td>\n",
       "      <td>46.834830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경기도수원시</td>\n",
       "      <td>49.768431</td>\n",
       "      <td>50.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>충청북도증평군</td>\n",
       "      <td>44.328623</td>\n",
       "      <td>49.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>전라남도해남군</td>\n",
       "      <td>35.945159</td>\n",
       "      <td>38.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>부산광역시강서구</td>\n",
       "      <td>43.882075</td>\n",
       "      <td>43.243490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>48.057786</td>\n",
       "      <td>43.532750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>충청남도금산군</td>\n",
       "      <td>47.359310</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>전라남도장성군</td>\n",
       "      <td>35.483589</td>\n",
       "      <td>41.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>서울특별시강동구</td>\n",
       "      <td>45.643615</td>\n",
       "      <td>51.869780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>경기도가평군</td>\n",
       "      <td>45.872543</td>\n",
       "      <td>42.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>서울특별시중 구</td>\n",
       "      <td>44.745115</td>\n",
       "      <td>50.795260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>대구광역시수성구</td>\n",
       "      <td>43.110463</td>\n",
       "      <td>41.611060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>대전광역시동 구</td>\n",
       "      <td>44.339670</td>\n",
       "      <td>43.052680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>경상북도울진군</td>\n",
       "      <td>40.855081</td>\n",
       "      <td>34.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>전라북도전주시</td>\n",
       "      <td>46.287391</td>\n",
       "      <td>48.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>경상남도창녕군</td>\n",
       "      <td>37.757036</td>\n",
       "      <td>35.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>부산광역시서 구</td>\n",
       "      <td>40.532363</td>\n",
       "      <td>41.445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>경기도평택시</td>\n",
       "      <td>49.748102</td>\n",
       "      <td>53.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>강원도화천군</td>\n",
       "      <td>44.094040</td>\n",
       "      <td>37.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>서울특별시강서구</td>\n",
       "      <td>46.714518</td>\n",
       "      <td>53.202350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>인천광역시부평구</td>\n",
       "      <td>48.010833</td>\n",
       "      <td>48.738260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>부산광역시수영구</td>\n",
       "      <td>40.265370</td>\n",
       "      <td>41.276770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>경상북도예천군</td>\n",
       "      <td>41.126471</td>\n",
       "      <td>39.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>서울특별시용산구</td>\n",
       "      <td>44.642978</td>\n",
       "      <td>49.838330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>광주광역시북 구</td>\n",
       "      <td>42.801231</td>\n",
       "      <td>42.894970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>전라남도구례군</td>\n",
       "      <td>35.847071</td>\n",
       "      <td>38.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>45.806258</td>\n",
       "      <td>46.688430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경상북도문경시</td>\n",
       "      <td>41.418001</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>부산광역시동래구</td>\n",
       "      <td>41.391100</td>\n",
       "      <td>40.995830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>부산광역시동 구</td>\n",
       "      <td>39.449017</td>\n",
       "      <td>39.031000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region  fine_dust_predict  fine_dust_value\n",
       "0     충청남도홍성군          46.218385        46.166667\n",
       "1     경상남도합천군          39.118621        30.333333\n",
       "2      강원도인제군          44.895638        34.833333\n",
       "3     전라북도완주군          41.593626        45.000000\n",
       "4      강원도홍천군          44.599627        47.000000\n",
       "5     전라북도진안군          41.399238        37.666667\n",
       "6      경기도김포시          54.721885        54.666667\n",
       "7     전라북도임실군          42.949791        46.333333\n",
       "8    인천광역시강화군          43.270353        41.405270\n",
       "9    서울특별시강북구          44.542651        51.608070\n",
       "10    충청북도괴산군          43.195092        41.666667\n",
       "11     경기도고양시          53.842370        48.500000\n",
       "12   대구광역시달서구          45.955954        43.002590\n",
       "13    전라남도영광군          35.240637        44.500000\n",
       "14    충청북도음성군          46.344400        47.333333\n",
       "15  서울특별시서대문구          44.861406        40.610980\n",
       "16   서울특별시종로구          44.297744        46.834830\n",
       "17     경기도수원시          49.768431        50.833333\n",
       "18    충청북도증평군          44.328623        49.666667\n",
       "19    전라남도해남군          35.945159        38.333333\n",
       "20   부산광역시강서구          43.882075        43.243490\n",
       "21   인천광역시옹진군          48.057786        43.532750\n",
       "22    충청남도금산군          47.359310        46.000000\n",
       "23    전라남도장성군          35.483589        41.666667\n",
       "24   서울특별시강동구          45.643615        51.869780\n",
       "25     경기도가평군          45.872543        42.833333\n",
       "26   서울특별시중 구          44.745115        50.795260\n",
       "27   대구광역시수성구          43.110463        41.611060\n",
       "28   대전광역시동 구          44.339670        43.052680\n",
       "29    경상북도울진군          40.855081        34.166667\n",
       "30    전라북도전주시          46.287391        48.333333\n",
       "31    경상남도창녕군          37.757036        35.333333\n",
       "32   부산광역시서 구          40.532363        41.445000\n",
       "33     경기도평택시          49.748102        53.833333\n",
       "34     강원도화천군          44.094040        37.400000\n",
       "35   서울특별시강서구          46.714518        53.202350\n",
       "36   인천광역시부평구          48.010833        48.738260\n",
       "37   부산광역시수영구          40.265370        41.276770\n",
       "38    경상북도예천군          41.126471        39.166667\n",
       "39   서울특별시용산구          44.642978        49.838330\n",
       "40   광주광역시북 구          42.801231        42.894970\n",
       "41    전라남도구례군          35.847071        38.333333\n",
       "42  서울특별시영등포구          45.806258        46.688430\n",
       "43    경상북도문경시          41.418001        40.000000\n",
       "44   부산광역시동래구          41.391100        40.995830\n",
       "45   부산광역시동 구          39.449017        39.031000"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_LR = []\n",
    "for i in y_predict:\n",
    "    predict_LR.append(i)\n",
    "actual_value_svmm = []\n",
    "for i in y_test:\n",
    "    actual_value_svmm.append(i)\n",
    "\n",
    "meta_data = list(zip(x_test_region,predict_LR, actual_value_svmm))\n",
    "\n",
    "SVMM = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])\n",
    "SVMM"
   ]
  }
 ],
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