O3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 135,
   "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": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../final.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 분석 및 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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": [
    "O3 나쁜 지역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data[data.iloc[:,1] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O3</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>5</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도양구군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도영월군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도증평군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도진천군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도청원군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도청주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0</td>\n",
       "      <td>충청북도충주시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O3   region\n",
       "2     0   강원도동해시\n",
       "3     0   강원도삼척시\n",
       "5     0   강원도양구군\n",
       "7     0   강원도영월군\n",
       "8     0   강원도원주시\n",
       "..   ..      ...\n",
       "225   0  충청북도증평군\n",
       "226   0  충청북도진천군\n",
       "227   0  충청북도청원군\n",
       "228   0  충청북도청주시\n",
       "229   0  충청북도충주시\n",
       "\n",
       "[141 rows x 2 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O3 좋은 지역"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a = data[data.iloc[:,1] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O3</th>\n",
       "      <th>region</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>강원도강릉시</td>\n",
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       "      <td>강원도고성군</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도속초시</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도양양군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>강원도인제군</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1</td>\n",
       "      <td>전라북도진안군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1</td>\n",
       "      <td>제주특별자치도서귀포시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>1</td>\n",
       "      <td>제주특별자치도제주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1</td>\n",
       "      <td>충청남도서산시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1</td>\n",
       "      <td>충청남도청양군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O3       region\n",
       "0     1       강원도강릉시\n",
       "1     1       강원도고성군\n",
       "4     1       강원도속초시\n",
       "6     1       강원도양양군\n",
       "9     1       강원도인제군\n",
       "..   ..          ...\n",
       "199   1      전라북도진안군\n",
       "200   1  제주특별자치도서귀포시\n",
       "201   1   제주특별자치도제주시\n",
       "209   1      충청남도서산시\n",
       "215   1      충청남도청양군\n",
       "\n",
       "[89 rows x 2 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "미세먼지 데이터 지우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['fine_dust', 'avg_fine_dust'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>O3</th>\n",
       "      <th>region</th>\n",
       "      <th>heavy_metal</th>\n",
       "      <th>traffic</th>\n",
       "      <th>num_of_factories</th>\n",
       "      <th>num_of_power_plants</th>\n",
       "      <th>num_of_people</th>\n",
       "      <th>household_waste_discharge_per_person (kg/days)</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rainfall(mm)</th>\n",
       "      <th>C</th>\n",
       "      <th>avg_O3</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>37.4</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>213321</td>\n",
       "      <td>1.3</td>\n",
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       "      <td>64.02192</td>\n",
       "      <td>42.53333</td>\n",
       "      <td>14.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>38.4</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>26757</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.810</td>\n",
       "      <td>-27.45230</td>\n",
       "      <td>37.68333</td>\n",
       "      <td>13.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>36.0</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>90593</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.295</td>\n",
       "      <td>64.02192</td>\n",
       "      <td>32.91667</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>0.4728</td>\n",
       "      <td>35.9</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>65243</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.295</td>\n",
       "      <td>64.02192</td>\n",
       "      <td>32.91667</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>34.8</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>82665</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.295</td>\n",
       "      <td>-27.45230</td>\n",
       "      <td>37.68333</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   O3  region  heavy_metal  traffic  num_of_factories  num_of_power_plants  \\\n",
       "0   1  강원도강릉시       0.9711     37.4               450                    2   \n",
       "1   1  강원도고성군       0.9711     38.4                81                    0   \n",
       "2   0  강원도동해시       0.9711     36.0               257                    4   \n",
       "3   0  강원도삼척시       0.4728     35.9               107                    2   \n",
       "4   1  강원도속초시       0.9711     34.8               141                    0   \n",
       "\n",
       "   num_of_people  household_waste_discharge_per_person (kg/days)  wind_speed  \\\n",
       "0         213321                                             1.3       6.295   \n",
       "1          26757                                             2.4       3.810   \n",
       "2          90593                                             1.6       6.295   \n",
       "3          65243                                             2.9       6.295   \n",
       "4          82665                                             1.4       6.295   \n",
       "\n",
       "   wind_direction  rainfall(mm)     C  avg_O3  \n",
       "0        64.02192      42.53333  14.6   0.035  \n",
       "1       -27.45230      37.68333  13.2   0.035  \n",
       "2        64.02192      32.91667  13.5   0.027  \n",
       "3        64.02192      32.91667  13.5   0.027  \n",
       "4       -27.45230      37.68333  13.2   0.034  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 230 entries, 0 to 229\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                          Non-Null Count  Dtype  \n",
      "---  ------                                          --------------  -----  \n",
      " 0   O3                                              230 non-null    int64  \n",
      " 1   region                                          230 non-null    object \n",
      " 2   heavy_metal                                     230 non-null    float64\n",
      " 3   traffic                                         230 non-null    float64\n",
      " 4   num_of_factories                                230 non-null    int64  \n",
      " 5   num_of_power_plants                             230 non-null    int64  \n",
      " 6   num_of_people                                   230 non-null    int64  \n",
      " 7   household_waste_discharge_per_person (kg/days)  230 non-null    float64\n",
      " 8   wind_speed                                      230 non-null    float64\n",
      " 9   wind_direction                                  230 non-null    float64\n",
      " 10  rainfall(mm)                                    230 non-null    float64\n",
      " 11  C                                               230 non-null    float64\n",
      " 12  avg_O3                                          230 non-null    float64\n",
      "dtypes: float64(8), int64(4), object(1)\n",
      "memory usage: 23.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O3</th>\n",
       "      <th>region</th>\n",
       "      <th>heavy_metal</th>\n",
       "      <th>traffic</th>\n",
       "      <th>num_of_factories</th>\n",
       "      <th>num_of_power_plants</th>\n",
       "      <th>num_of_people</th>\n",
       "      <th>household_waste_discharge_per_person (kg/days)</th>\n",
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       "      <th>rainfall(mm)</th>\n",
       "      <th>C</th>\n",
       "      <th>avg_O3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>37.4</td>\n",
       "      <td>450</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>6.295</td>\n",
       "      <td>64.02192</td>\n",
       "      <td>42.53333</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도고성군</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>38.4</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>26757</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.810</td>\n",
       "      <td>-27.45230</td>\n",
       "      <td>37.68333</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도동해시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>36.0</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>90593</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.295</td>\n",
       "      <td>64.02192</td>\n",
       "      <td>32.91667</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>0.4728</td>\n",
       "      <td>35.9</td>\n",
       "      <td>107</td>\n",
       "      <td>2</td>\n",
       "      <td>65243</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.295</td>\n",
       "      <td>64.02192</td>\n",
       "      <td>32.91667</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>34.8</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>82665</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.295</td>\n",
       "      <td>-27.45230</td>\n",
       "      <td>37.68333</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   O3  region  heavy_metal  traffic  num_of_factories  num_of_power_plants  \\\n",
       "0   1  강원도강릉시       0.9711     37.4               450                    2   \n",
       "1   1  강원도고성군       0.9711     38.4                81                    0   \n",
       "2   0  강원도동해시       0.9711     36.0               257                    4   \n",
       "3   0  강원도삼척시       0.4728     35.9               107                    2   \n",
       "4   1  강원도속초시       0.9711     34.8               141                    0   \n",
       "\n",
       "   num_of_people  household_waste_discharge_per_person (kg/days)  wind_speed  \\\n",
       "0         213321                                             1.3       6.295   \n",
       "1          26757                                             2.4       3.810   \n",
       "2          90593                                             1.6       6.295   \n",
       "3          65243                                             2.9       6.295   \n",
       "4          82665                                             1.4       6.295   \n",
       "\n",
       "   wind_direction  rainfall(mm)     C  avg_O3  \n",
       "0        64.02192      42.53333  14.6   0.035  \n",
       "1       -27.45230      37.68333  13.2   0.035  \n",
       "2        64.02192      32.91667  13.5   0.027  \n",
       "3        64.02192      32.91667  13.5   0.027  \n",
       "4       -27.45230      37.68333  13.2   0.034  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 230 entries, 0 to 229\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                          Non-Null Count  Dtype  \n",
      "---  ------                                          --------------  -----  \n",
      " 0   O3                                              230 non-null    int64  \n",
      " 1   region                                          230 non-null    object \n",
      " 2   heavy_metal                                     230 non-null    float64\n",
      " 3   traffic                                         230 non-null    float64\n",
      " 4   num_of_factories                                230 non-null    int64  \n",
      " 5   num_of_power_plants                             230 non-null    int64  \n",
      " 6   num_of_people                                   230 non-null    int64  \n",
      " 7   household_waste_discharge_per_person (kg/days)  230 non-null    float64\n",
      " 8   wind_speed                                      230 non-null    float64\n",
      " 9   wind_direction                                  230 non-null    float64\n",
      " 10  rainfall(mm)                                    230 non-null    float64\n",
      " 11  C                                               230 non-null    float64\n",
      " 12  avg_O3                                          230 non-null    float64\n",
      "dtypes: float64(8), int64(4), object(1)\n",
      "memory usage: 23.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 X,y로 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 1:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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": 151,
   "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": 152,
   "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": 153,
   "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": 154,
   "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": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>min_samples_leaf</th>\n",
       "      <th>max_depth</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.844961</td>\n",
       "      <td>0.860465</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.837209</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.837209</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>0.875969</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.852713</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.798450</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_samples_leaf  max_depth  training accuracy  validation accuracy\n",
       "0                  3          1           0.798450             0.767442\n",
       "1                  3          2           0.844961             0.860465\n",
       "2                  3          5           0.930233             0.837209\n",
       "3                  3          7           0.930233             0.837209\n",
       "4                  3         13           0.930233             0.837209\n",
       "5                  3         20           0.930233             0.837209\n",
       "6                  3         30           0.930233             0.837209\n",
       "7                  5          1           0.798450             0.767442\n",
       "8                  5          2           0.837209             0.860465\n",
       "9                  5          5           0.875969             0.860465\n",
       "10                 5          7           0.875969             0.860465\n",
       "11                 5         13           0.875969             0.860465\n",
       "12                 5         20           0.875969             0.860465\n",
       "13                 5         30           0.875969             0.860465\n",
       "14                 7          1           0.798450             0.767442\n",
       "15                 7          2           0.829457             0.860465\n",
       "16                 7          5           0.860465             0.837209\n",
       "17                 7          7           0.860465             0.837209\n",
       "18                 7         13           0.860465             0.837209\n",
       "19                 7         20           0.860465             0.837209\n",
       "20                 7         30           0.860465             0.837209\n",
       "21                 8          1           0.798450             0.767442\n",
       "22                 8          2           0.821705             0.860465\n",
       "23                 8          5           0.852713             0.837209\n",
       "24                 8          7           0.852713             0.837209\n",
       "25                 8         13           0.852713             0.837209\n",
       "26                 8         20           0.852713             0.837209\n",
       "27                 8         30           0.852713             0.837209\n",
       "28                 9          1           0.798450             0.767442\n",
       "29                 9          2           0.821705             0.837209\n",
       "30                 9          5           0.852713             0.813953\n",
       "31                 9          7           0.852713             0.813953\n",
       "32                 9         13           0.852713             0.813953\n",
       "33                 9         20           0.852713             0.813953\n",
       "34                 9         30           0.852713             0.813953\n",
       "35                10          1           0.798450             0.767442\n",
       "36                10          2           0.821705             0.837209\n",
       "37                10          5           0.852713             0.813953\n",
       "38                10          7           0.852713             0.813953\n",
       "39                10         13           0.852713             0.813953\n",
       "40                10         20           0.852713             0.813953\n",
       "41                10         30           0.852713             0.813953\n",
       "42                20          1           0.798450             0.767442\n",
       "43                20          2           0.821705             0.837209\n",
       "44                20          5           0.821705             0.837209\n",
       "45                20          7           0.821705             0.837209\n",
       "46                20         13           0.821705             0.837209\n",
       "47                20         20           0.821705             0.837209\n",
       "48                20         30           0.821705             0.837209"
      ]
     },
     "execution_count": 155,
     "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": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=7, min_samples_leaf=5, random_state=0)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth =7, min_samples_leaf = 5, random_state =0)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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": 158,
   "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": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "DT = pd.DataFrame(data = meta_data,columns=['region','fine_dust_predict', 'fine_dust_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>경기도의왕시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경상북도성주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>충청북도보은군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>충청북도제천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>전라남도나주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도인제군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강원도양양군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경상북도봉화군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경상북도울릉군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청남도아산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경기도안산시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>경상남도양산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>대전광역시대덕구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울특별시성북구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경기도성남시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>부산광역시수영구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>충청남도부여군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>경기도시흥시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>충청남도공주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>서울특별시강서구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>부산광역시영도구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>인천광역시연수구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>전라남도담양군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>경상북도영양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>경기도연천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>서울특별시은평구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>서울특별시강남구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>광주광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>서울특별시송파구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시수성구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>경기도포천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>충청북도옥천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>광주광역시북 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도무주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>경상남도남해군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경상남도사천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>강원도양구군</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      경기도의왕시                  0                0\n",
       "1      강원도강릉시                  0                1\n",
       "2     경상북도성주군                  1                1\n",
       "3   서울특별시영등포구                  0                0\n",
       "4     충청북도보은군                  0                0\n",
       "5     충청북도제천시                  0                0\n",
       "6     전라남도나주시                  1                1\n",
       "7      경기도부천시                  0                0\n",
       "8     충청남도예산군                  0                0\n",
       "9      경기도광주시                  0                0\n",
       "10     강원도인제군                  0                1\n",
       "11     강원도양양군                  0                1\n",
       "12    경상북도봉화군                  1                1\n",
       "13    경상북도울릉군                  1                1\n",
       "14    충청남도아산시                  0                0\n",
       "15   인천광역시서 구                  0                0\n",
       "16   대구광역시중 구                  0                0\n",
       "17     경기도안산시                  0                1\n",
       "18   대구광역시남 구                  0                0\n",
       "19    경상남도양산시                  0                0\n",
       "20   대전광역시대덕구                  0                0\n",
       "21   서울특별시관악구                  0                0\n",
       "22   서울특별시성북구                  0                0\n",
       "23     경기도성남시                  0                0\n",
       "24   부산광역시수영구                  1                1\n",
       "25    충청남도부여군                  0                0\n",
       "26     경기도시흥시                  0                0\n",
       "27    충청남도공주시                  0                0\n",
       "28   서울특별시강서구                  0                0\n",
       "29   부산광역시영도구                  0                1\n",
       "30   인천광역시연수구                  0                0\n",
       "31    전라남도담양군                  1                0\n",
       "32     강원도삼척시                  1                0\n",
       "33    경상북도영양군                  1                1\n",
       "34   대전광역시중 구                  0                0\n",
       "35     경기도연천군                  0                0\n",
       "36   서울특별시은평구                  0                0\n",
       "37    충청북도충주시                  0                0\n",
       "38   서울특별시강남구                  0                0\n",
       "39   광주광역시동 구                  1                1\n",
       "40    경상북도칠곡군                  1                0\n",
       "41    경상남도김해시                  1                1\n",
       "42   서울특별시송파구                  0                0\n",
       "43    경상북도고령군                  1                1\n",
       "44   대구광역시수성구                  0                0\n",
       "45    전라남도곡성군                  1                0\n",
       "46   인천광역시옹진군                  0                1\n",
       "47    전라북도장수군                  1                1\n",
       "48     경기도포천시                  0                0\n",
       "49    충청북도옥천군                  0                0\n",
       "50   광주광역시북 구                  0                1\n",
       "51    전라북도순창군                  1                1\n",
       "52    전라북도무주군                  1                1\n",
       "53    경상남도남해군                  1                1\n",
       "54     강원도속초시                  0                1\n",
       "55   인천광역시동 구                  0                0\n",
       "56    경상남도사천시                  1                1\n",
       "57     강원도양구군                  0                0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.88\n",
      "Accuracy on test set: 0.79\n",
      "[[32  4]\n",
      " [ 8 14]]\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": 162,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.36764705882352944, 0.9166666666666666, 'X[0] <= 0.48\\ngini = 0.475\\nsamples = 129\\nvalue = [79, 50]'),\n",
       " Text(0.17647058823529413, 0.75, 'X[4] <= 191475.5\\ngini = 0.337\\nsamples = 42\\nvalue = [9, 33]'),\n",
       " Text(0.11764705882352941, 0.5833333333333334, 'X[9] <= 12.6\\ngini = 0.239\\nsamples = 36\\nvalue = [5, 31]'),\n",
       " Text(0.058823529411764705, 0.4166666666666667, 'gini = 0.444\\nsamples = 9\\nvalue = [3, 6]'),\n",
       " Text(0.17647058823529413, 0.4166666666666667, 'X[9] <= 13.95\\ngini = 0.137\\nsamples = 27\\nvalue = [2, 25]'),\n",
       " Text(0.11764705882352941, 0.25, 'gini = 0.0\\nsamples = 16\\nvalue = [0, 16]'),\n",
       " Text(0.23529411764705882, 0.25, 'X[8] <= 46.458\\ngini = 0.298\\nsamples = 11\\nvalue = [2, 9]'),\n",
       " Text(0.17647058823529413, 0.08333333333333333, 'gini = 0.48\\nsamples = 5\\nvalue = [2, 3]'),\n",
       " Text(0.29411764705882354, 0.08333333333333333, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.23529411764705882, 0.5833333333333334, 'gini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.5588235294117647, 0.75, 'X[9] <= 14.75\\ngini = 0.314\\nsamples = 87\\nvalue = [70, 17]'),\n",
       " Text(0.35294117647058826, 0.5833333333333334, 'X[4] <= 45585.5\\ngini = 0.117\\nsamples = 64\\nvalue = [60, 4]'),\n",
       " Text(0.29411764705882354, 0.4166666666666667, 'gini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
       " Text(0.4117647058823529, 0.4166666666666667, 'X[7] <= -80.373\\ngini = 0.035\\nsamples = 56\\nvalue = [55, 1]'),\n",
       " Text(0.35294117647058826, 0.25, 'gini = 0.278\\nsamples = 6\\nvalue = [5, 1]'),\n",
       " Text(0.47058823529411764, 0.25, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0]'),\n",
       " Text(0.7647058823529411, 0.5833333333333334, 'X[0] <= 0.713\\ngini = 0.491\\nsamples = 23\\nvalue = [10, 13]'),\n",
       " Text(0.6470588235294118, 0.4166666666666667, 'X[1] <= 38.1\\ngini = 0.298\\nsamples = 11\\nvalue = [2, 9]'),\n",
       " Text(0.5882352941176471, 0.25, 'gini = 0.32\\nsamples = 5\\nvalue = [1, 4]'),\n",
       " Text(0.7058823529411765, 0.25, 'gini = 0.278\\nsamples = 6\\nvalue = [1, 5]'),\n",
       " Text(0.8823529411764706, 0.4166666666666667, 'X[1] <= 36.5\\ngini = 0.444\\nsamples = 12\\nvalue = [8, 4]'),\n",
       " Text(0.8235294117647058, 0.25, 'gini = 0.48\\nsamples = 5\\nvalue = [2, 3]'),\n",
       " Text(0.9411764705882353, 0.25, 'gini = 0.245\\nsamples = 7\\nvalue = [6, 1]')]"
      ]
     },
     "execution_count": 162,
     "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": 163,
   "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": 164,
   "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=\"1007pt\" height=\"581pt\"\r\n viewBox=\"0.00 0.00 1007.00 581.00\" 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(4 577)\">\r\n<title>Tree</title>\r\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-577 1003,-577 1003,4 -4,4\"/>\r\n<!-- 0 -->\r\n<g id=\"node1\" class=\"node\"><title>0</title>\r\n<polygon fill=\"#f5d1b6\" stroke=\"black\" points=\"464,-573 315,-573 315,-505 464,-505 464,-573\"/>\r\n<text text-anchor=\"middle\" x=\"389.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">heavy_metal &lt;= 0.48</text>\r\n<text text-anchor=\"middle\" x=\"389.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 129</text>\r\n<text text-anchor=\"middle\" x=\"389.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [79, 50]</text>\r\n<text text-anchor=\"middle\" x=\"389.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 1 -->\r\n<g id=\"node2\" class=\"node\"><title>1</title>\r\n<polygon fill=\"#6fb8ec\" stroke=\"black\" points=\"403,-469 200,-469 200,-401 403,-401 403,-469\"/>\r\n<text text-anchor=\"middle\" x=\"301.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">num_of_people &lt;= 191475.5</text>\r\n<text text-anchor=\"middle\" x=\"301.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 42</text>\r\n<text text-anchor=\"middle\" x=\"301.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 33]</text>\r\n<text text-anchor=\"middle\" x=\"301.5\" y=\"-408.8\" 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=\"M360.929,-504.884C353.244,-495.976 344.84,-486.235 336.835,-476.957\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"339.411,-474.585 330.229,-469.299 334.111,-479.157 339.411,-474.585\"/>\r\n<text text-anchor=\"middle\" x=\"328.392\" y=\"-490.532\" 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=\"#eba069\" stroke=\"black\" points=\"534,-469 421,-469 421,-401 534,-401 534,-469\"/>\r\n<text text-anchor=\"middle\" x=\"477.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">C &lt;= 14.75</text>\r\n<text text-anchor=\"middle\" x=\"477.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 87</text>\r\n<text text-anchor=\"middle\" x=\"477.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [70, 17]</text>\r\n<text text-anchor=\"middle\" x=\"477.5\" y=\"-408.8\" 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=\"M418.071,-504.884C425.756,-495.976 434.16,-486.235 442.165,-476.957\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"444.889,-479.157 448.771,-469.299 439.589,-474.585 444.889,-479.157\"/>\r\n<text text-anchor=\"middle\" x=\"450.608\" y=\"-490.532\" 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=\"#59ade9\" stroke=\"black\" points=\"238,-365 133,-365 133,-297 238,-297 238,-365\"/>\r\n<text text-anchor=\"middle\" x=\"185.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">C &lt;= 12.6</text>\r\n<text text-anchor=\"middle\" x=\"185.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 36</text>\r\n<text text-anchor=\"middle\" x=\"185.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 31]</text>\r\n<text text-anchor=\"middle\" x=\"185.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</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=\"M263.838,-400.884C253.404,-391.709 241.965,-381.65 231.13,-372.123\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"233.191,-369.274 223.37,-365.299 228.568,-374.531 233.191,-369.274\"/>\r\n</g>\r\n<!-- 9 -->\r\n<g id=\"node10\" class=\"node\"><title>9</title>\r\n<polygon fill=\"#f2c09c\" stroke=\"black\" points=\"353,-357.5 256,-357.5 256,-304.5 353,-304.5 353,-357.5\"/>\r\n<text text-anchor=\"middle\" x=\"304.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n<text text-anchor=\"middle\" x=\"304.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 2]</text>\r\n<text text-anchor=\"middle\" x=\"304.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 1&#45;&gt;9 -->\r\n<g id=\"edge9\" class=\"edge\"><title>1&#45;&gt;9</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M302.474,-400.884C302.788,-390.216 303.137,-378.352 303.455,-367.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"306.954,-367.619 303.749,-357.52 299.957,-367.413 306.954,-367.619\"/>\r\n</g>\r\n<!-- 3 -->\r\n<g id=\"node4\" class=\"node\"><title>3</title>\r\n<polygon fill=\"#9ccef2\" stroke=\"black\" points=\"125,-253.5 24,-253.5 24,-200.5 125,-200.5 125,-253.5\"/>\r\n<text text-anchor=\"middle\" x=\"74.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\r\n<text text-anchor=\"middle\" x=\"74.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 6]</text>\r\n<text text-anchor=\"middle\" x=\"74.5\" y=\"-208.3\" 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=\"M149.461,-296.884C136.775,-285.226 122.535,-272.141 109.902,-260.532\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"112.003,-257.709 102.272,-253.52 107.267,-262.863 112.003,-257.709\"/>\r\n</g>\r\n<!-- 4 -->\r\n<g id=\"node5\" class=\"node\"><title>4</title>\r\n<polygon fill=\"#49a5e7\" stroke=\"black\" points=\"248,-261 143,-261 143,-193 248,-193 248,-261\"/>\r\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">C &lt;= 13.95</text>\r\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 27</text>\r\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 25]</text>\r\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</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=\"M188.747,-296.884C189.541,-288.778 190.404,-279.982 191.238,-271.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"194.743,-271.593 192.235,-261.299 187.776,-270.91 194.743,-271.593\"/>\r\n</g>\r\n<!-- 5 -->\r\n<g id=\"node6\" class=\"node\"><title>5</title>\r\n<polygon fill=\"#399de5\" stroke=\"black\" points=\"105,-149.5 0,-149.5 0,-96.5 105,-96.5 105,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"52.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\r\n<text text-anchor=\"middle\" x=\"52.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 16]</text>\r\n<text text-anchor=\"middle\" x=\"52.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 4&#45;&gt;5 -->\r\n<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M149.072,-192.884C132.266,-180.896 113.343,-167.399 96.7311,-155.549\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"98.4516,-152.478 88.2779,-149.52 94.3867,-158.176 98.4516,-152.478\"/>\r\n</g>\r\n<!-- 6 -->\r\n<g id=\"node7\" class=\"node\"><title>6</title>\r\n<polygon fill=\"#65b3eb\" stroke=\"black\" points=\"282,-157 123,-157 123,-89 282,-89 282,-157\"/>\r\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">rainfall(mm) &lt;= 46.458</text>\r\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\r\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 9]</text>\r\n<text text-anchor=\"middle\" x=\"202.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 4&#45;&gt;6 -->\r\n<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M197.773,-192.884C198.329,-184.778 198.933,-175.982 199.517,-167.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"203.022,-167.516 200.215,-157.299 196.038,-167.036 203.022,-167.516\"/>\r\n</g>\r\n<!-- 7 -->\r\n<g id=\"node8\" class=\"node\"><title>7</title>\r\n<polygon fill=\"#bddef6\" stroke=\"black\" points=\"193,-53 92,-53 92,-0 193,-0 193,-53\"/>\r\n<text text-anchor=\"middle\" x=\"142.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n<text text-anchor=\"middle\" x=\"142.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 3]</text>\r\n<text text-anchor=\"middle\" x=\"142.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 6&#45;&gt;7 -->\r\n<g id=\"edge7\" class=\"edge\"><title>6&#45;&gt;7</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M181.515,-88.9485C175.939,-80.1664 169.905,-70.6629 164.287,-61.815\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"167.156,-59.8028 158.841,-53.2367 161.246,-63.5549 167.156,-59.8028\"/>\r\n</g>\r\n<!-- 8 -->\r\n<g id=\"node9\" class=\"node\"><title>8</title>\r\n<polygon fill=\"#399de5\" stroke=\"black\" points=\"312,-53 211,-53 211,-0 312,-0 312,-53\"/>\r\n<text text-anchor=\"middle\" x=\"261.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n<text text-anchor=\"middle\" x=\"261.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6]</text>\r\n<text text-anchor=\"middle\" x=\"261.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 6&#45;&gt;8 -->\r\n<g id=\"edge8\" class=\"edge\"><title>6&#45;&gt;8</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M223.135,-88.9485C228.618,-80.1664 234.552,-70.6629 240.076,-61.815\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"243.104,-63.5728 245.432,-53.2367 237.167,-59.8656 243.104,-63.5728\"/>\r\n</g>\r\n<!-- 11 -->\r\n<g id=\"node12\" class=\"node\"><title>11</title>\r\n<polygon fill=\"#e78946\" stroke=\"black\" points=\"571.5,-365 377.5,-365 377.5,-297 571.5,-297 571.5,-365\"/>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">num_of_people &lt;= 45585.5</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 64</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [60, 4]</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-304.8\" 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=\"M476.526,-400.884C476.288,-392.778 476.029,-383.982 475.779,-375.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"479.272,-375.192 475.479,-365.299 472.275,-375.398 479.272,-375.192\"/>\r\n</g>\r\n<!-- 16 -->\r\n<g id=\"node17\" class=\"node\"><title>16</title>\r\n<polygon fill=\"#d1e8f9\" stroke=\"black\" points=\"768,-365 611,-365 611,-297 768,-297 768,-365\"/>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">heavy_metal &lt;= 0.713</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 23</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [10, 13]</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 10&#45;&gt;16 -->\r\n<g id=\"edge16\" class=\"edge\"><title>10&#45;&gt;16</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M534.034,-406.8C557.74,-395.394 585.7,-381.941 611.326,-369.612\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"613.156,-372.616 620.65,-365.126 610.121,-366.308 613.156,-372.616\"/>\r\n</g>\r\n<!-- 12 -->\r\n<g id=\"node13\" class=\"node\"><title>12</title>\r\n<polygon fill=\"#f5cdb0\" stroke=\"black\" points=\"366,-253.5 269,-253.5 269,-200.5 366,-200.5 366,-253.5\"/>\r\n<text text-anchor=\"middle\" x=\"317.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\r\n<text text-anchor=\"middle\" x=\"317.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 3]</text>\r\n<text text-anchor=\"middle\" x=\"317.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 11&#45;&gt;12 -->\r\n<g id=\"edge12\" class=\"edge\"><title>11&#45;&gt;12</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M423.527,-296.884C404.821,-284.731 383.728,-271.027 365.31,-259.061\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"367.073,-256.033 356.781,-253.52 363.26,-261.903 367.073,-256.033\"/>\r\n</g>\r\n<!-- 13 -->\r\n<g id=\"node14\" class=\"node\"><title>13</title>\r\n<polygon fill=\"#e5833d\" stroke=\"black\" points=\"565,-261 384,-261 384,-193 565,-193 565,-261\"/>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">wind_direction &lt;= &#45;80.373</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 56</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [55, 1]</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 11&#45;&gt;13 -->\r\n<g id=\"edge13\" class=\"edge\"><title>11&#45;&gt;13</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M474.5,-296.884C474.5,-288.778 474.5,-279.982 474.5,-271.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"478,-271.299 474.5,-261.299 471,-271.299 478,-271.299\"/>\r\n</g>\r\n<!-- 14 -->\r\n<g id=\"node15\" class=\"node\"><title>14</title>\r\n<polygon fill=\"#ea9a61\" stroke=\"black\" points=\"404,-149.5 307,-149.5 307,-96.5 404,-96.5 404,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 1]</text>\r\n<text text-anchor=\"middle\" x=\"355.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 13&#45;&gt;14 -->\r\n<g id=\"edge14\" class=\"edge\"><title>13&#45;&gt;14</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M435.864,-192.884C422.135,-181.116 406.709,-167.894 393.071,-156.203\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"395.144,-153.37 385.273,-149.52 390.588,-158.685 395.144,-153.37\"/>\r\n</g>\r\n<!-- 15 -->\r\n<g id=\"node16\" class=\"node\"><title>15</title>\r\n<polygon fill=\"#e58139\" stroke=\"black\" points=\"527,-149.5 422,-149.5 422,-96.5 527,-96.5 527,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 0]</text>\r\n<text text-anchor=\"middle\" x=\"474.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 13&#45;&gt;15 -->\r\n<g id=\"edge15\" class=\"edge\"><title>13&#45;&gt;15</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M474.5,-192.884C474.5,-182.326 474.5,-170.597 474.5,-159.854\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"478,-159.52 474.5,-149.52 471,-159.52 478,-159.52\"/>\r\n</g>\r\n<!-- 17 -->\r\n<g id=\"node18\" class=\"node\"><title>17</title>\r\n<polygon fill=\"#65b3eb\" stroke=\"black\" points=\"741.5,-261 637.5,-261 637.5,-193 741.5,-193 741.5,-261\"/>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">traffic &lt;= 38.1</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 9]</text>\r\n<text text-anchor=\"middle\" x=\"689.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 16&#45;&gt;17 -->\r\n<g id=\"edge17\" class=\"edge\"><title>16&#45;&gt;17</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M689.5,-296.884C689.5,-288.778 689.5,-279.982 689.5,-271.472\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"693,-271.299 689.5,-261.299 686,-271.299 693,-271.299\"/>\r\n</g>\r\n<!-- 20 -->\r\n<g id=\"node21\" class=\"node\"><title>20</title>\r\n<polygon fill=\"#f2c09c\" stroke=\"black\" points=\"885.5,-261 781.5,-261 781.5,-193 885.5,-193 885.5,-261\"/>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">traffic &lt;= 36.5</text>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8, 4]</text>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 16&#45;&gt;20 -->\r\n<g id=\"edge20\" class=\"edge\"><title>16&#45;&gt;20</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M736.253,-296.884C749.582,-287.442 764.234,-277.064 778.029,-267.292\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"780.352,-269.936 786.489,-261.299 776.306,-264.223 780.352,-269.936\"/>\r\n</g>\r\n<!-- 18 -->\r\n<g id=\"node19\" class=\"node\"><title>18</title>\r\n<polygon fill=\"#6ab6ec\" stroke=\"black\" points=\"646,-149.5 545,-149.5 545,-96.5 646,-96.5 646,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"595.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n<text text-anchor=\"middle\" x=\"595.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 4]</text>\r\n<text text-anchor=\"middle\" x=\"595.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 17&#45;&gt;18 -->\r\n<g id=\"edge18\" class=\"edge\"><title>17&#45;&gt;18</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M658.981,-192.884C648.44,-181.446 636.633,-168.634 626.087,-157.19\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"628.369,-154.502 619.018,-149.52 623.221,-159.245 628.369,-154.502\"/>\r\n</g>\r\n<!-- 19 -->\r\n<g id=\"node20\" class=\"node\"><title>19</title>\r\n<polygon fill=\"#61b1ea\" stroke=\"black\" points=\"765,-149.5 664,-149.5 664,-96.5 765,-96.5 765,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"714.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n<text text-anchor=\"middle\" x=\"714.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 5]</text>\r\n<text text-anchor=\"middle\" x=\"714.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 17&#45;&gt;19 -->\r\n<g id=\"edge19\" class=\"edge\"><title>17&#45;&gt;19</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M697.617,-192.884C700.231,-182.216 703.139,-170.352 705.794,-159.519\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"709.264,-160.066 708.245,-149.52 702.465,-158.399 709.264,-160.066\"/>\r\n</g>\r\n<!-- 21 -->\r\n<g id=\"node22\" class=\"node\"><title>21</title>\r\n<polygon fill=\"#bddef6\" stroke=\"black\" points=\"884,-149.5 783,-149.5 783,-96.5 884,-96.5 884,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 3]</text>\r\n<text text-anchor=\"middle\" x=\"833.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Good</text>\r\n</g>\r\n<!-- 20&#45;&gt;21 -->\r\n<g id=\"edge21\" class=\"edge\"><title>20&#45;&gt;21</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M833.5,-192.884C833.5,-182.326 833.5,-170.597 833.5,-159.854\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"837,-159.52 833.5,-149.52 830,-159.52 837,-159.52\"/>\r\n</g>\r\n<!-- 22 -->\r\n<g id=\"node23\" class=\"node\"><title>22</title>\r\n<polygon fill=\"#e9965a\" stroke=\"black\" points=\"999,-149.5 902,-149.5 902,-96.5 999,-96.5 999,-149.5\"/>\r\n<text text-anchor=\"middle\" x=\"950.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\r\n<text text-anchor=\"middle\" x=\"950.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 1]</text>\r\n<text text-anchor=\"middle\" x=\"950.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Bad</text>\r\n</g>\r\n<!-- 20&#45;&gt;22 -->\r\n<g id=\"edge22\" class=\"edge\"><title>20&#45;&gt;22</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M871.487,-192.884C884.985,-181.116 900.151,-167.894 913.561,-156.203\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"915.989,-158.73 921.227,-149.52 911.389,-153.453 915.989,-158.73\"/>\r\n</g>\r\n</g>\r\n</svg>\r\n",
      "text/plain": [
       "<graphviz.files.Source at 0x1eb822ec8c8>"
      ]
     },
     "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": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.57  0.031 0.    0.    0.118 0.    0.    0.008 0.022 0.251]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importances:\")\n",
    "print(clf.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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": 168,
   "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": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature magnitude')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAwcOcN1117Fp06bQoYlIAqJcp5F191xsAeSn8L4LmEt+mZJr3H2zmf0x8GXyU263uHvH8X72dK/TmO486LjncYfaBKpUNkEKrZLn6av+dNd/QtdpmNl57r4N2GdmHz72eXe/tRjBufu4lxG7+x3AHcWoQ4pneBOktWvXjpRpEySJQ11dHc8888ykr5lsnO/UU0/l6aePnZhZPvWXqskGwt8JbAM+NM5zDhQlaUh5Gd4ECWD16tV0dXXR1tbG6tWrA0cmleaZZ56Zdku/nOsvVZH2CC9X6p6KRzabZdOmTTz//PNkMhlWrVpFLhdbD2bJquTuiVKoP/TfX+j3h/z8ybqnooxpZICPAA2Mapm4+xeKGGMslDQkTqF//pVef+i/v9DvD/n501p7ivxsqWfJz5x6vpiBiYhIeYmSNOrd/fypXyYiIpUuyvpN283sjbFHImUj9CZMixYtGnM9jJaLEUlOlKSxHLjPzB4ys1+Y2U4z+0XcgUlpGt6EKZfLMTg4SC6Xo729PbHEsWjRIvbs2cOyZcvYv38/y5YtY8+ePUocIgmJMhB+5njl7v54LBEVkQbCi6+5uZlcLkdLS8tIWW9vL9lsNpFNmMyMZcuWcc8994yUnXvuuWzfvj3IhY6VPBAduv7Qf3+h3x/y86c7e2q8tTCec/ejxQguTkoaxVddXc3g4CA1NTUjZUePHqW2tpahofi3ODEz9u/fz4IFC0bKDhw4wOmnn66kUWH1h/77C/3+kJ8/WdKI0j11P3AQeBj4VeH+Y2Z2v5m9pXhhSjlobGykr2/sxo19fX00NjYmFsPFF1886WMRiU+U2VN3k99i9U4AM3sf+es2vk5+v+63xReelJLhK1zPO++8CZ+P+8x34cKFbN++nXPPPZdbbrmFiy++mO3bt7Nw4cKp3yxSRkp1GZMoLY23DycMAHe/C3iHu/8/QAsOpcjw8uvd3d00NTUB0NTURHd3d2JLsz/xxBMjieP0008fSRhPPPFE7HWnjVZ5Dmt4GZMTvU2VcE5UlDGNu4Dvk99yFeDjwHvJ7+t9r7u/OZbIikBjGvEKXX9oob9/qf//0vvL9/3THdO4hPwmSLcVbosKZdXAx04oIhERKUtR9gj/HZCd4OlHihuOSOko1T5lkZCi7BE+D7gKaAJqh8vdffzRUJEKoaWxRV4uSvfUN4FfAouBa4HHyG/HKiIx0kC0lKIoU25PK2y/eoW7/xD4oZkpaYjETC0dKUVRksbwld8HzOwDwH5ApzAiIikUJWl80cxeCVwJ5ICTgb+ONSoBNBArIqUnyuyp7xXuPgu0TPZaKS51T4hIqYkye2ox+Sm3DYzd7vWC+MISEZFSFKV76jZgM/Bd4MVYoxERkZIWJWkMuvs/xR6JiIiUvChJ43ozuwa4C3h+uNDd748tKhERKUlRksYbgT8FzuOl7ikvPE6UmZ0PXE9+3auvufuXko5BRNLBrzkZPv/K6b2/AkVJGh8FXuPuR+IOZjJmVg18lfwKu3uBe83sdnd/MGRcIlKZ7NrfT3+V2c8XL55SESVp9AOnAE/GG8qU3go84u6/BjCzbwEXArElDZ1piIiMFSVpnAL8srB0yOgxjaSn3J4B7Bn1eC/j7BpoZpcDlwMsWrRoWhXqTENEZKwoSeOa2KMoIne/AbgB8pswBQ5nWkK3dHRFuogcK8oV4T9MIpAI9gGjN4KuL5RVrNAtHV2RLhJO6JPGiURpaZSKe4GzCleo7wM+QX4HQRGRihP6pHEiZZM03P0FM1sD3El+yu0Wd38gcFgiIqkSKWmY2SuARe7+UMzxTMrd7wDuCBmDiEiaRVmw8EPAPwIzgcVm9ibgC1qwUGI3jf7clz7j2el/hoiMiNLS+Dz5ayR+AODuPyuMK4jEK+UH/FIdCJV0i7Rzn7s/e8xMmLKeylpOpjMD6dRTTy1iJJK0Uh0ITYqSZmmKkjQeMLNLgGozOwv4NLA93rAEmPKAYWbTOqiIlLK0J81SFSVpZIF28leDd5OfvfTFOIMSKQU60xV5uUmTRmGRwP/j7i3kE4ekSNoPmjrTldBKsXt60qTh7kNm9qKZvdLd0z0qmUI6aKZb2k8aQivV7uko3VOHgJ1mdjcwMFzo7p+OLSoRCU4nDTKeKEnj1sJNUqgUm8ciEk6UBQu/kUQgUnpKtXksIuFEuSJ8N+Ncl+Hur4klIhEZoZaelJoo3VNLR92vJb/9a1084UgpG+8AdmyZWh7Fo5aelKIo3VNPHVP0ZTO7D/jbeEKSUqUDlCRNLa3SE6V76s2jHlaRb3mUzZLqIlKe1NIqTVEO/p2j7r8A7AY+Fk84IiJSyqIkjVZ3//XoAq1yKyKSTlURXnNLxDIRkYpiZid8q9QxlQlbGmb2BqAJeKWZfXjUUyeTn0UlIhUuzQPRGlMZ32TdU68HPgicAnxoVPlzwKoYYyopaf6jkXTTQVPGM2HScPfvAN8xs3e4+48TjKlk6I9GdNIgMlaUgfCfmtmnyHdVjXRLufvK2KISKQE6aRB5uSgD4f8TmA/8F+CHQD35LioREUmZKEnjP7n754CBwuKFHwDeFm9YIiJSiqJ0Tx0t/PsfZtYM/AZ4VXwhTczMZgP/DBwBfuDu3wwRh4hIWkVpadxgZqcCnwNuBx4E/qEYlZvZFjN70sz6jyk/38weMrNHzGzdqKc+DNzi7quAC4oRg4iIRDdl0nD3r7n7M+7+Q3d/jbu/yt27ilT/jcD5owsK+5J/FXg/sARYYWZLCk/XA3sK94eKFIOIiEQ0ZdIws1eb2WYz+7+Fx0vMrLUYlbv7j4Cnjyl+K/CIu//a3Y8A3wIuLDy3l3ziiBR7pTn2itOJykTioP9/AtEOvDcCdwKnFx4/DPxVTPEAnMFLrQnIJ4ozCvdvBT5iZv8CfHe8N5vZ5Wa2w8x2HDx4MMYwk+fuU94kPmk/aOr/n0C0gfC57v6/zeyzAO7+gplF6hoys38jP133WO2FiwePi7sPAJ+c4jU3ADcALF26VP+LpWh0UBSJljQGzOw0Clu+mtnbgWejfLi7v+cEYtoHLBz1uL5QJiIigUVJGmvJz5p6rZndA8wDLo4xpnuBswrLr+8DPgFcEmN9IiIS0WSr3C5y9yfc/X4zeyf5BQwNeMjdj070vuNhZj3Au4C5ZrYXuMbdN5vZGvLjKNXAFnd/oBj1iYjI9EzW0rgNGN7q9SZ3/0ixK3f3FROU3wHcUez6RERkeiabPTV6Kshr4g5ERERK32RJwye4LyIiKTVZ99Qfmdnvybc4XlG4T+Gxu/vJsUcnIiIlZbJNmKqTDEREREpf6pbiEBGRExflOg0RkeDGW6bl2LJKvmq/VL6/koaIlIVKTghRlMr3V/eUiIhEpqQhIiKRKWmIiEhkShoiIhKZkoaIiESmpCEiIpEpaYiISGRKGiIiZeTss88esy/92WefnWj9ShoiZWL0gWL4SuDxyqRynX322ezcuZMLLriAgwcPcsEFF7Bz585EE4eShkiZcPcpb1LZdu7cyTnnnMOjjz7Kq1/9ah599FHOOeccdu7cmVgMShoiImXkqaeeIpfLMTg4SC6X46mnnkq0fiUNEZEyUl9fT0tLCzU1NbS0tFBfX59o/VqwUEQkglJZZXb79u3MmTOHQ4cOcdJJJ3Ho0KHY6xxNLQ0RkQhKYUypvr6e6urqkURx6NAhqqurE21tKGmIlJlsNkttbS1mRm1tLdlsNnRIkqA5c+bQ0NBAVVUVDQ0NzJkzJ9H6lTREykg2m6Wrq4v169czMDDA+vXr6erqUuJIiX379lFTUwO81BVWU1PDvn37EotBSUOkjGzatIkNGzawdu1aZs2axdq1a9mwYQObNm0KHZokYObMmaxbt47du3fz4osvsnv3btatW8fMmTMTi8EqeW730qVLfceOHbF9vplpbrwkyswYGBhg1qxZI2WHDx9m9uzZ+r+YAlVVVZx55pls2bKF5cuX09fXx8qVK3n88cd58cUXi1aPmd3n7kvHjaFotYhI7DKZDF1dXWPKurq6yGQygSKSJC1ZsoRLL710ZFwrm81y6aWXsmTJksRiUNIQKSOrVq2ira2NjRs3cvjwYTZu3EhbWxurVq0KHZokoL29ne7u7jEX93V3d9Pe3p5YDLpOQ6SM5HI5AK6++mquvPJKMpkMq1evHimXyrZixQogPyFi165dNDY20tHRMVKeBI1pTIPGNESkEk02pqGWxnEolStCRURCUdI4DkoIIpJ2GggXEZHIlDRERCQyJQ0REYlMSUNERCJT0hARkciUNEREykhPTw/Nzc1UV1fT3NxMT09PovVryq2ISJno6emhvb2dzZs3jyxY2NraCpDYVeG6IlxEpEw0NzeTy+VoaWkZKevt7SWbzdLf31+0eia7IlxJQ0SkTFRXVzM4ODiyERPA0aNHqa2tZWhoqGj1aGl0EZEK0NjYSF9f35iyvr4+GhsbE4tBSUNEpEy0t7fT2tpKb28vR48epbe3l9bWVi2NLiIiL6el0WOmMQ0RkeOnMQ0RESkKJQ0RkTKii/tERCQSXdwXM41piEgl0cV9MVPSEJFKoov7REQkMl3cJyIikZXCxX1KGmUom81SW1uLmVFbW0s2mw0dkogkYMWKFXR0dIwcA7LZbOIX92n2VJnJZrN0dXWxYcMGVq9eTVdXF21tbQDkcrnA0YlI3FasWJFokjiWBsLLTG1tLevXr2ft2rUjZRs3buTqq69mcHAwYGQiUik0e6qCmBkDAwPMmjVrpOzw4cPMnj2bSv5dikhyNHuqgmQyGbq6usaUdXV1kclkAkUkImmiMY0ys2rVqpExjNFjGqtXrw4cmYikgZJGmRke7L766qu58soryWQyrF69WoPgIpIIjWmIiMgYGtMQEZGiUNIQEZHIlDRERCQyJQ0REYlMSUNERCKr6NlTZnYQeDx0HDGaC/wudBAB6fvr++v7x+NMd5833hMVnTQqnZntmGhaXBro++v76/sn//3VPSUiIpEpaYiISGRKGuXthtABBKbvn276/gFoTENERCJTS0NERCJT0igzZrbQzHrN7EEze8DMrggdUwhmVm1mPzWz74WOJWlmdoqZ3WJmvzSzXWb2jtAxJcnM/rrwf7/fzHrMrDZ0THEzsy1m9qSZ9Y8qqzOzu83sV4V/T00iFiWN8vMCcKW7LwHeDnzKzJYEjimEK4BdoYMI5HrgX939DcAfkaKfg5mdAXwaWOruzUA18ImwUSXiRuD8Y8rWAd9397OA7xcex05Jo8y4+wF3v79w/znyB4wzwkaVLDOrBz4AfC10LEkzs1cC/xnYDODuR9z9P4IGlbwZwCvMbAYwC9gfOJ7YufuPgKePKb4Q+Ebh/jeAi5KIRUmjjJlZA3AO8O+BQ0nal4GrgBcDxxHCYuAg8PVC99zXzGx26KCS4u77gH8EngAOAM+6+11howrm1e5+oHD/N8Crk6hUSaNMmdlJwLeBv3L334eOJylm9kHgSXe/L3QsgcwA3gz8i7ufAwyQULdEKSj0219IPnmeDsw2s/8aNqrwPD8NNpGpsEoaZcjMasgnjG+6+62h40nYucAFZvYY8C3gPDP7X2FDStReYK+7D7cubyGfRNLiPcBudz/o7keBW4FlgWMK5bdmtgCg8O+TSVSqpFFmzMzI92fvcveNoeNJmrt/1t3r3b2B/ADoNndPzZmmu/8G2GNmry8UvRt4MGBISXsCeLuZzSr8LbybFE0EOMbtwJ8V7v8Z8J0kKlXSKD/nAn9K/gz7Z4XbH4cOShKVBb5pZr8A3gSsDxtOcgotrFuA+4Gd5I9hFX9luJn1AD8GXm9me82sFfgS8F4z+xX5FtiXEolFV4SLiEhUammIiEhkShoiIhKZkoaIiESmpCEiIpEpaYiISGRKGpJKZjY0asryzwpLshzvZ1wU12KRZna6md1ynO/5czP7ShzxiAybEToAkUD+4O5vmuZnXAR8j+O4uM7MZrj7C1O9zt33AxefeGgi8VBLQ6TAzN5iZj80s/vM7M5RSzSsMrN7zeznZvbtwtXIy4ALgOsKLZXXmtkPzGxp4T1zC0udDLcAbjezbcD3zWx2YX+EnxQWHbxwnFgahvdOKLz/VjP718LeCf8w6nWfNLOHzewn5C/8HC6fV4j13sLt3EL5d8zsssL9vzCzb8b045QKpZaGpNUrzOxnhfu7gY8BOeBCdz9oZh8HOoCVwK3uvgnAzL4ItLp7zsxuB77n7rcUnpusvjcDZ7v702a2nvzyJyvN7BTgJ2b2b+4+MMn730R+RePngYfMLEd+b5VrgbcAzwK9wE8Lr78e+O/u3mdmi4A7gUbgcuAeM9sNXEl+TxaRyJQ0JK3GdE+ZWTPQDNxdOPhXk196G6C5kCxOAU4ifwA+Xne7+/B+CO8jv+jiZwqPa4FFTL6G0vfd/dlCrA8CZwJzgR+4+8FC+U3A6wqvfw+wZFQiO9nMTnL335rZ35JPMH8yKiaRSJQ0RPIMeMDdx9s69UbgInf/uZn9OfCuCT7jBV7q8j12C9LRrQgDPuLuDx1HfM+Puj/E1H+7VcDb3X1wnOfeCDxFfmlxkeOiMQ2RvIeAeVbYb9vMasysqfDcHOBAYUn6S0e957nCc8MeI99VBJMPYt8JZAurtGJm55xgzP8OvNPMTivE9tFRz91FfmFDCnW8qfDvW4H3k+/q+oyZLT7BuiWllDREyG+bSv5Av8HMfg78jJf2afgc+QP0PcAvR73tW8DfFAazX0t+R7m/NLOfku86msjfATXAL8zsgcLjE4n5APB58quf3sPY7q1PA0vN7BeF7qzVZpYBNgErC7OzrgS2mE0+GCMymla5FRGRyNTSEBGRyJQ0REQkMiUNERGJTElDREQiU9IQEZHIlDRERCQyJQ0REYlMSUNERCL7/wogjYrYf/kIAAAAAElFTkSuQmCC",
      "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": 170,
   "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": 171,
   "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>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.961240</td>\n",
       "      <td>0.651163</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>0.604651</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>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>0.744186</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>0.674419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.992248</td>\n",
       "      <td>0.674419</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>0.604651</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>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.992248</td>\n",
       "      <td>0.627907</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>0.674419</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>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.627907</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>0.674419</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>0.674419</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>0.604651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C   gamma  training accuracy  validation accuracy\n",
       "0      0.01    0.01           0.612403             0.604651\n",
       "1      0.01    0.10           0.612403             0.604651\n",
       "2      0.01    1.00           0.612403             0.604651\n",
       "3      0.01   10.00           0.612403             0.604651\n",
       "4      0.01  100.00           0.612403             0.604651\n",
       "5      1.00    0.01           0.790698             0.767442\n",
       "6      1.00    0.10           0.844961             0.720930\n",
       "7      1.00    1.00           0.961240             0.651163\n",
       "8      1.00   10.00           1.000000             0.604651\n",
       "9      1.00  100.00           1.000000             0.604651\n",
       "10    10.00    0.01           0.821705             0.744186\n",
       "11    10.00    0.10           0.930233             0.674419\n",
       "12    10.00    1.00           0.992248             0.674419\n",
       "13    10.00   10.00           1.000000             0.604651\n",
       "14    10.00  100.00           1.000000             0.604651\n",
       "15   100.00    0.01           0.875969             0.720930\n",
       "16   100.00    0.10           0.992248             0.627907\n",
       "17   100.00    1.00           1.000000             0.674419\n",
       "18   100.00   10.00           1.000000             0.604651\n",
       "19   100.00  100.00           1.000000             0.604651\n",
       "20  1000.00    0.01           0.891473             0.627907\n",
       "21  1000.00    0.10           1.000000             0.674419\n",
       "22  1000.00    1.00           1.000000             0.674419\n",
       "23  1000.00   10.00           1.000000             0.604651\n",
       "24  1000.00  100.00           1.000000             0.604651"
      ]
     },
     "execution_count": 171,
     "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": 172,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.7906976744186046\n",
      "Accuracy on testing set:  0.7758620689655172\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(C=1, 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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지역별 예측값 실제값 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>경기도의왕시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경상북도성주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>충청북도보은군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>충청북도제천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>전라남도나주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도인제군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강원도양양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경상북도봉화군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경상북도울릉군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청남도아산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경기도안산시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>경상남도양산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>대전광역시대덕구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울특별시성북구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경기도성남시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>부산광역시수영구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>충청남도부여군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>경기도시흥시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>충청남도공주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>서울특별시강서구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>부산광역시영도구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>인천광역시연수구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>전라남도담양군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>경상북도영양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>경기도연천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>서울특별시은평구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>서울특별시강남구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>광주광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>서울특별시송파구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시수성구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>경기도포천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>충청북도옥천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>광주광역시북 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도무주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>경상남도남해군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경상남도사천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>강원도양구군</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      경기도의왕시                  0                0\n",
       "1      강원도강릉시                  0                1\n",
       "2     경상북도성주군                  1                1\n",
       "3   서울특별시영등포구                  0                0\n",
       "4     충청북도보은군                  0                0\n",
       "5     충청북도제천시                  0                0\n",
       "6     전라남도나주시                  1                1\n",
       "7      경기도부천시                  0                0\n",
       "8     충청남도예산군                  0                0\n",
       "9      경기도광주시                  0                0\n",
       "10     강원도인제군                  0                1\n",
       "11     강원도양양군                  1                1\n",
       "12    경상북도봉화군                  1                1\n",
       "13    경상북도울릉군                  1                1\n",
       "14    충청남도아산시                  0                0\n",
       "15   인천광역시서 구                  0                0\n",
       "16   대구광역시중 구                  0                0\n",
       "17     경기도안산시                  0                1\n",
       "18   대구광역시남 구                  0                0\n",
       "19    경상남도양산시                  0                0\n",
       "20   대전광역시대덕구                  0                0\n",
       "21   서울특별시관악구                  0                0\n",
       "22   서울특별시성북구                  0                0\n",
       "23     경기도성남시                  0                0\n",
       "24   부산광역시수영구                  0                1\n",
       "25    충청남도부여군                  0                0\n",
       "26     경기도시흥시                  0                0\n",
       "27    충청남도공주시                  0                0\n",
       "28   서울특별시강서구                  0                0\n",
       "29   부산광역시영도구                  0                1\n",
       "30   인천광역시연수구                  0                0\n",
       "31    전라남도담양군                  1                0\n",
       "32     강원도삼척시                  1                0\n",
       "33    경상북도영양군                  1                1\n",
       "34   대전광역시중 구                  0                0\n",
       "35     경기도연천군                  0                0\n",
       "36   서울특별시은평구                  0                0\n",
       "37    충청북도충주시                  0                0\n",
       "38   서울특별시강남구                  0                0\n",
       "39   광주광역시동 구                  0                1\n",
       "40    경상북도칠곡군                  1                0\n",
       "41    경상남도김해시                  0                1\n",
       "42   서울특별시송파구                  0                0\n",
       "43    경상북도고령군                  1                1\n",
       "44   대구광역시수성구                  0                0\n",
       "45    전라남도곡성군                  1                0\n",
       "46   인천광역시옹진군                  0                1\n",
       "47    전라북도장수군                  1                1\n",
       "48     경기도포천시                  0                0\n",
       "49    충청북도옥천군                  0                0\n",
       "50   광주광역시북 구                  0                1\n",
       "51    전라북도순창군                  1                1\n",
       "52    전라북도무주군                  1                1\n",
       "53    경상남도남해군                  1                1\n",
       "54     강원도속초시                  1                1\n",
       "55   인천광역시동 구                  0                0\n",
       "56    경상남도사천시                  1                1\n",
       "57     강원도양구군                  0                0"
      ]
     },
     "execution_count": 173,
     "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": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature magnitude')"
      ]
     },
     "execution_count": 174,
     "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": 175,
   "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": 176,
   "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>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.10</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.612403</td>\n",
       "      <td>0.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.767442</td>\n",
       "      <td>0.697674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.906977</td>\n",
       "      <td>0.790698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.984496</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>0.767442</td>\n",
       "      <td>0.697674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.790698</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>0.860465</td>\n",
       "      <td>0.767442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.984496</td>\n",
       "      <td>0.744186</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>0.651163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.798450</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>0.829457</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>100.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.922481</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>100.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.992248</td>\n",
       "      <td>0.767442</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>0.651163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C   gamma  training accuracy  validation accuracy\n",
       "0     0.01    0.01           0.612403             0.604651\n",
       "1     0.01    0.10           0.612403             0.604651\n",
       "2     0.01    1.00           0.612403             0.604651\n",
       "3     0.01   10.00           0.612403             0.604651\n",
       "4     0.01  100.00           0.612403             0.604651\n",
       "5     0.10    0.01           0.612403             0.604651\n",
       "6     0.10    0.10           0.612403             0.604651\n",
       "7     0.10    1.00           0.612403             0.604651\n",
       "8     0.10   10.00           0.612403             0.604651\n",
       "9     0.10  100.00           0.612403             0.604651\n",
       "10    1.00    0.01           0.612403             0.604651\n",
       "11    1.00    0.10           0.767442             0.697674\n",
       "12    1.00    1.00           0.790698             0.813953\n",
       "13    1.00   10.00           0.906977             0.790698\n",
       "14    1.00  100.00           0.984496             0.651163\n",
       "15   10.00    0.01           0.767442             0.697674\n",
       "16   10.00    0.10           0.790698             0.813953\n",
       "17   10.00    1.00           0.860465             0.767442\n",
       "18   10.00   10.00           0.984496             0.744186\n",
       "19   10.00  100.00           1.000000             0.651163\n",
       "20  100.00    0.01           0.798450             0.813953\n",
       "21  100.00    0.10           0.829457             0.720930\n",
       "22  100.00    1.00           0.922481             0.720930\n",
       "23  100.00   10.00           0.992248             0.767442\n",
       "24  100.00  100.00           1.000000             0.651163"
      ]
     },
     "execution_count": 176,
     "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": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.7906976744186046\n",
      "Accuracy on testing set:  0.7586206896551724\n",
      "[[31  5]\n",
      " [ 9 13]]\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": 178,
   "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>경기도의왕시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경상북도성주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>충청북도보은군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>충청북도제천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>전라남도나주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도인제군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강원도양양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경상북도봉화군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경상북도울릉군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청남도아산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경기도안산시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>경상남도양산시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>대전광역시대덕구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울특별시성북구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경기도성남시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>부산광역시수영구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>충청남도부여군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>경기도시흥시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>충청남도공주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>서울특별시강서구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>부산광역시영도구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>인천광역시연수구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>전라남도담양군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>경상북도영양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>경기도연천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>서울특별시은평구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>서울특별시강남구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>광주광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>서울특별시송파구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시수성구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>경기도포천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>충청북도옥천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>광주광역시북 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도무주군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>경상남도남해군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경상남도사천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>강원도양구군</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      경기도의왕시                  0                0\n",
       "1      강원도강릉시                  0                1\n",
       "2     경상북도성주군                  1                1\n",
       "3   서울특별시영등포구                  0                0\n",
       "4     충청북도보은군                  0                0\n",
       "5     충청북도제천시                  0                0\n",
       "6     전라남도나주시                  1                1\n",
       "7      경기도부천시                  0                0\n",
       "8     충청남도예산군                  0                0\n",
       "9      경기도광주시                  0                0\n",
       "10     강원도인제군                  0                1\n",
       "11     강원도양양군                  1                1\n",
       "12    경상북도봉화군                  1                1\n",
       "13    경상북도울릉군                  1                1\n",
       "14    충청남도아산시                  0                0\n",
       "15   인천광역시서 구                  0                0\n",
       "16   대구광역시중 구                  0                0\n",
       "17     경기도안산시                  0                1\n",
       "18   대구광역시남 구                  0                0\n",
       "19    경상남도양산시                  1                0\n",
       "20   대전광역시대덕구                  0                0\n",
       "21   서울특별시관악구                  0                0\n",
       "22   서울특별시성북구                  0                0\n",
       "23     경기도성남시                  0                0\n",
       "24   부산광역시수영구                  1                1\n",
       "25    충청남도부여군                  0                0\n",
       "26     경기도시흥시                  0                0\n",
       "27    충청남도공주시                  0                0\n",
       "28   서울특별시강서구                  0                0\n",
       "29   부산광역시영도구                  0                1\n",
       "30   인천광역시연수구                  0                0\n",
       "31    전라남도담양군                  1                0\n",
       "32     강원도삼척시                  1                0\n",
       "33    경상북도영양군                  1                1\n",
       "34   대전광역시중 구                  0                0\n",
       "35     경기도연천군                  0                0\n",
       "36   서울특별시은평구                  0                0\n",
       "37    충청북도충주시                  0                0\n",
       "38   서울특별시강남구                  0                0\n",
       "39   광주광역시동 구                  0                1\n",
       "40    경상북도칠곡군                  1                0\n",
       "41    경상남도김해시                  0                1\n",
       "42   서울특별시송파구                  0                0\n",
       "43    경상북도고령군                  1                1\n",
       "44   대구광역시수성구                  0                0\n",
       "45    전라남도곡성군                  1                0\n",
       "46   인천광역시옹진군                  0                1\n",
       "47    전라북도장수군                  1                1\n",
       "48     경기도포천시                  0                0\n",
       "49    충청북도옥천군                  0                0\n",
       "50   광주광역시북 구                  0                1\n",
       "51    전라북도순창군                  1                1\n",
       "52    전라북도무주군                  1                1\n",
       "53    경상남도남해군                  1                1\n",
       "54     강원도속초시                  0                1\n",
       "55   인천광역시동 구                  0                0\n",
       "56    경상남도사천시                  1                1\n",
       "57     강원도양구군                  0                0"
      ]
     },
     "execution_count": 178,
     "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": 179,
   "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": 180,
   "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": 181,
   "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>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000.00</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.72093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          C  training accuracy  val accuracy\n",
       "0      0.01           0.674419       0.72093\n",
       "1      0.10           0.674419       0.72093\n",
       "2      1.00           0.674419       0.72093\n",
       "3     10.00           0.674419       0.72093\n",
       "4    100.00           0.674419       0.72093\n",
       "5   1000.00           0.674419       0.72093\n",
       "6  10000.00           0.674419       0.72093"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"C\":C_settings, \"training accuracy\": training_accuracy, \"val accuracy\": val_accuracy})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, max_iter=1000)"
      ]
     },
     "execution_count": 182,
     "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": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1]\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": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set:  0.5426356589147286\n",
      "Accuracy on testing set:  0.6724137931034483\n",
      "[[31  5]\n",
      " [ 9 13]]\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": 185,
   "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>경기도의왕시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강원도강릉시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경상북도성주군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시영등포구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>충청북도보은군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>충청북도제천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>전라남도나주시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>경기도부천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>충청남도예산군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경기도광주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>강원도인제군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강원도양양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경상북도봉화군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경상북도울릉군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>충청남도아산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>인천광역시서 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구광역시중 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>경기도안산시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>대구광역시남 구</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>경상남도양산시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>대전광역시대덕구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울특별시관악구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울특별시성북구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>경기도성남시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>부산광역시수영구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>충청남도부여군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>경기도시흥시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>충청남도공주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>서울특별시강서구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>부산광역시영도구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>인천광역시연수구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>전라남도담양군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>강원도삼척시</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>경상북도영양군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>대전광역시중 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>경기도연천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>서울특별시은평구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>충청북도충주시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>서울특별시강남구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>광주광역시동 구</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>경상북도칠곡군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>경상남도김해시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>서울특별시송파구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>경상북도고령군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>대구광역시수성구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>전라남도곡성군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>인천광역시옹진군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>전라북도장수군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>경기도포천시</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>충청북도옥천군</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>광주광역시북 구</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>전라북도순창군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>전라북도무주군</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>경상남도남해군</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>강원도속초시</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>인천광역시동 구</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>경상남도사천시</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>강원도양구군</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region  fine_dust_predict  fine_dust_value\n",
       "0      경기도의왕시                  0                0\n",
       "1      강원도강릉시                  0                1\n",
       "2     경상북도성주군                  0                1\n",
       "3   서울특별시영등포구                  0                0\n",
       "4     충청북도보은군                  0                0\n",
       "5     충청북도제천시                  0                0\n",
       "6     전라남도나주시                  1                1\n",
       "7      경기도부천시                  0                0\n",
       "8     충청남도예산군                  0                0\n",
       "9      경기도광주시                  0                0\n",
       "10     강원도인제군                  1                1\n",
       "11     강원도양양군                  1                1\n",
       "12    경상북도봉화군                  0                1\n",
       "13    경상북도울릉군                  1                1\n",
       "14    충청남도아산시                  0                0\n",
       "15   인천광역시서 구                  0                0\n",
       "16   대구광역시중 구                  1                0\n",
       "17     경기도안산시                  0                1\n",
       "18   대구광역시남 구                  1                0\n",
       "19    경상남도양산시                  0                0\n",
       "20   대전광역시대덕구                  0                0\n",
       "21   서울특별시관악구                  0                0\n",
       "22   서울특별시성북구                  0                0\n",
       "23     경기도성남시                  0                0\n",
       "24   부산광역시수영구                  1                1\n",
       "25    충청남도부여군                  0                0\n",
       "26     경기도시흥시                  0                0\n",
       "27    충청남도공주시                  0                0\n",
       "28   서울특별시강서구                  0                0\n",
       "29   부산광역시영도구                  0                1\n",
       "30   인천광역시연수구                  0                0\n",
       "31    전라남도담양군                  1                0\n",
       "32     강원도삼척시                  1                0\n",
       "33    경상북도영양군                  1                1\n",
       "34   대전광역시중 구                  0                0\n",
       "35     경기도연천군                  0                0\n",
       "36   서울특별시은평구                  0                0\n",
       "37    충청북도충주시                  0                0\n",
       "38   서울특별시강남구                  0                0\n",
       "39   광주광역시동 구                  1                1\n",
       "40    경상북도칠곡군                  0                0\n",
       "41    경상남도김해시                  0                1\n",
       "42   서울특별시송파구                  0                0\n",
       "43    경상북도고령군                  0                1\n",
       "44   대구광역시수성구                  0                0\n",
       "45    전라남도곡성군                  1                0\n",
       "46   인천광역시옹진군                  0                1\n",
       "47    전라북도장수군                  0                1\n",
       "48     경기도포천시                  0                0\n",
       "49    충청북도옥천군                  0                0\n",
       "50   광주광역시북 구                  0                1\n",
       "51    전라북도순창군                  0                1\n",
       "52    전라북도무주군                  0                1\n",
       "53    경상남도남해군                  1                1\n",
       "54     강원도속초시                  0                1\n",
       "55   인천광역시동 구                  0                0\n",
       "56    경상남도사천시                  1                1\n",
       "57     강원도양구군                  1                0"
      ]
     },
     "execution_count": 185,
     "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": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "회귀계수: [[-0.     0.005 -0.001 -0.    -0.     0.     0.001  0.007  0.005  0.002]]  상수항: [0.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(suppress=True, precision =3)\n",
    "print(\"회귀계수:\", LR.coef_,\" 상수항:\", LR.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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