Monthly_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<<목차>>\n",
    "\n",
    "0. 필요한 패키지 임포트\n",
    "1. 데이터 불러오기\n",
    "2. 데이터 전처리\n",
    "3. 모델에 넣을 데이터 정하기\n",
    "4. 여러가지 Regressor 중 RMSE(Root Mean Square Error;평균 제곱근 오차)가 가장 낮은 모델 찾기\n",
    "5. 모델링하여 결과값 만들기\n",
    "6. 변수 중요도(Feature Importance) 그려서 저장하기\n",
    "7. 월별 머신러닝 모델들의 성능(RMSE) 비교"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. 필요한 패키지 임포트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from xgboost import XGBClassifier, XGBRegressor\n",
    "import xgboost\n",
    "import lightgbm as lgb\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "import matplotlib\n",
    "import warnings\n",
    "\n",
    "\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.1\n",
      "1.1.1\n",
      "4.0\n",
      "0.23.2\n",
      "1.1.0\n",
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "print(np.__version__)\n",
    "print(pd.__version__)\n",
    "print(pkl.format_version)\n",
    "print(sklearn.__version__)\n",
    "print(xgboost.__version__)\n",
    "print(lgb.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 데이터 불러오기\n",
    "##### 사용한 데이터 : 그리드별 월단위 통합 탐색 (20200825_아파트 매매가 추가).pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('./데이터')   # 파일 경로 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9003120 entries, 0 to 9003119\n",
      "Data columns (total 81 columns):\n",
      " #   Column            Dtype  \n",
      "---  ------            -----  \n",
      " 0   id                float64\n",
      " 1   조사년도              int64  \n",
      " 2   조사월               int64  \n",
      " 3   단속활동유무            bool   \n",
      " 4   적발횟수_0            int64  \n",
      " 5   적발횟수_1            int64  \n",
      " 6   적발횟수_2            int64  \n",
      " 7   적발횟수_3            int64  \n",
      " 8   적발횟수_4            int64  \n",
      " 9   적발횟수_5            int64  \n",
      " 10  적발횟수_6            int64  \n",
      " 11  돼지고기              int64  \n",
      " 12  소고기               int64  \n",
      " 13  닭고기               int64  \n",
      " 14  김치                int64  \n",
      " 15  쌀                 int64  \n",
      " 16  콩                 int64  \n",
      " 17  기타                int64  \n",
      " 18  미위반               int64  \n",
      " 19  일반음식점_미위반         int64  \n",
      " 20  식육 판매업_미위반        int64  \n",
      " 21  슈퍼_미위반            int64  \n",
      " 22  가공업체_미위반          int64  \n",
      " 23  양곡상_미위반           int64  \n",
      " 24  휴게음식점_미위반         int64  \n",
      " 25  기타_미위반            int64  \n",
      " 26  일반음식점_위반          int64  \n",
      " 27  식육 판매업_위반         int64  \n",
      " 28  슈퍼_위반             int64  \n",
      " 29  가공업체_위반           int64  \n",
      " 30  양곡상_위반            int64  \n",
      " 31  휴게음식점_위반          int64  \n",
      " 32  기타_위반             int64  \n",
      " 33  도소매업_미위반          int64  \n",
      " 34  음식점업_미위반          int64  \n",
      " 35  식품제조업(가공)_미위반     int64  \n",
      " 36  업태누락_미위반          int64  \n",
      " 37  도소매업_위반           int64  \n",
      " 38  음식점업_위반           int64  \n",
      " 39  식품제조업(가공)_위반      int64  \n",
      " 40  업태누락_위반           int64  \n",
      " 41  65세이상인구           float64\n",
      " 42  총수급자              float64\n",
      " 43  독거노인수             float64\n",
      " 44  독거노인수_기초생활보장수급권자  float64\n",
      " 45  독거노인수_저소득         float64\n",
      " 46  총인구               float64\n",
      " 47  한국총인구             float64\n",
      " 48  외국인인구             float64\n",
      " 49  전체평균연령            float64\n",
      " 50  남자                float64\n",
      " 51  여자                float64\n",
      " 52  행정동               object \n",
      " 53  음식점업              int64  \n",
      " 54  도소매업              int64  \n",
      " 55  식품제조업(가공)         int64  \n",
      " 56  업태누락              int64  \n",
      " 57  쇠고기냉장_수량_톤        int64  \n",
      " 58  쇠고기냉동_수량_톤        int64  \n",
      " 59  돼지고기냉장_수량_톤       int64  \n",
      " 60  돼지고기냉동_수량_톤       int64  \n",
      " 61  닭고기_수량_톤          int64  \n",
      " 62  쇠고기가격_도매          int64  \n",
      " 63  쇠고기가격_소매          int64  \n",
      " 64  돼지고기가격_도매         int64  \n",
      " 65  돼지고기가격_소매         int64  \n",
      " 66  닭고기가격_도매          int64  \n",
      " 67  닭고기가격_소매          int64  \n",
      " 68  한우갈비_100g         int64  \n",
      " 69  한우불고기_100g        int64  \n",
      " 70  호주산갈비_냉동_100g     int64  \n",
      " 71  호주산갈비_냉장_100g     int64  \n",
      " 72  호주산불고기_냉동_100g    int64  \n",
      " 73  호주산불고기_냉장_100g    int64  \n",
      " 74  모범업소와의 거리         float64\n",
      " 75  관공서와의 거리          float64\n",
      " 76  적발지점과의 거리         float64\n",
      " 77  Q1                int64  \n",
      " 78  Q2                int64  \n",
      " 79  Q3                int64  \n",
      " 80  Q4                int64  \n",
      "dtypes: bool(1), float64(15), int64(64), object(1)\n",
      "memory usage: 5.4+ GB\n"
     ]
    }
   ],
   "source": [
    "with open('그리드별 월단위 통합 탐색 (20200825_아파트 매매가 추가).pickle', 'rb') as f:\n",
    "    dataset_original = pkl.load(f)\n",
    "dataset=dataset_original\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T0cCtWs8cM0s"
   },
   "source": [
    "# 2. 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 적발횟수_0 ~ 적발횟수_6가 전부 0인 sample은 한번도 조사나간 적 없는 곳에 대한 데이터이므로 삭제\n",
    "yes_0_index = list(dataset[(dataset['적발횟수_0'] == 0) & (dataset['적발횟수_1'] == 0) & (dataset['적발횟수_2'] == 0) & (dataset['적발횟수_3'] == 0) & (dataset['적발횟수_4'] == 0) & (dataset['적발횟수_5'] == 0) & (dataset['적발횟수_6'] == 0)].index)\n",
    "yes_0_index\n",
    "\n",
    "dataset.drop(yes_0_index,axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 적발건수 = 적발횟수1 + 적발횟수2 + 적발횟수3 + 적발횟수4 + 적발횟수5 + 적발횟수6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92614, 81)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dataset_real.pkl', 'wb') as f:\n",
    "    pkl.dump(dataset, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2341</td>\n",
       "      <td>1100</td>\n",
       "      <td>1736</td>\n",
       "      <td>2642.290162</td>\n",
       "      <td>1327.471248</td>\n",
       "      <td>2369.320204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9002702</th>\n",
       "      <td>111610.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2478</td>\n",
       "      <td>1400</td>\n",
       "      <td>2418</td>\n",
       "      <td>955.504602</td>\n",
       "      <td>1327.471248</td>\n",
       "      <td>948.801329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9002757</th>\n",
       "      <td>111610.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3249</td>\n",
       "      <td>1560</td>\n",
       "      <td>2029</td>\n",
       "      <td>6315.950726</td>\n",
       "      <td>1327.471248</td>\n",
       "      <td>959.702931</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92614 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  조사년도  조사월  단속활동유무  적발횟수_0  적발횟수_1  적발횟수_2  적발횟수_3  적발횟수_4  \\\n",
       "125         162.0  2010    6    True       4       0       0       0       0   \n",
       "24732      4397.0  2011    1    True       1       0       0       0       0   \n",
       "40812      5879.0  2011    1    True       1       0       0       0       0   \n",
       "46445      6192.0  2010    6    True       1       0       0       0       0   \n",
       "46446      6192.0  2010    7    True       1       0       0       0       0   \n",
       "...           ...   ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "8997477  110992.0  2019   10    True       1       0       0       0       0   \n",
       "9002659  111610.0  2011    8    True       1       0       0       0       0   \n",
       "9002660  111610.0  2011    9    True       1       0       0       0       0   \n",
       "9002702  111610.0  2015    3    True       2       0       0       0       0   \n",
       "9002757  111610.0  2019   10    True       1       0       0       0       0   \n",
       "\n",
       "         적발횟수_5  ...  호주산갈비_냉장_100g  호주산불고기_냉동_100g  호주산불고기_냉장_100g  \\\n",
       "125           0  ...           1645            1100            1374   \n",
       "24732         0  ...           2209            1100            1681   \n",
       "40812         0  ...           2209            1100            1681   \n",
       "46445         0  ...           1645            1100            1374   \n",
       "46446         0  ...           1469            1100            1433   \n",
       "...         ...  ...            ...             ...             ...   \n",
       "8997477       0  ...           3249            1560            2029   \n",
       "9002659       0  ...           1868            1100            1878   \n",
       "9002660       0  ...           2341            1100            1736   \n",
       "9002702       0  ...           2478            1400            2418   \n",
       "9002757       0  ...           3249            1560            2029   \n",
       "\n",
       "           모범업소와의 거리     관공서와의 거리    적발지점과의 거리  Q1  Q2  Q3  Q4  \n",
       "125      8517.740954  3900.057991  4668.864521   0   0   0   0  \n",
       "24732    5331.239728  2841.434788  2867.345386   0   0   0   0  \n",
       "40812    5942.053610  2352.696260  2340.621779   0   0   0   0  \n",
       "46445    7759.830956  1851.104842  2444.678466   0   0   0   0  \n",
       "46446    7759.830956  1851.104842  2444.678466   0   0   0   0  \n",
       "...              ...          ...          ...  ..  ..  ..  ..  \n",
       "8997477  7301.190220   884.021012  1224.179893   0   0   0   4  \n",
       "9002659  2642.290162  1327.471248  2369.320204   0   0   0   0  \n",
       "9002660  2642.290162  1327.471248  2369.320204   0   0   0   0  \n",
       "9002702   955.504602  1327.471248   948.801329   0   0   0   0  \n",
       "9002757  6315.950726  1327.471248   959.702931   0   0   0   0  \n",
       "\n",
       "[92614 rows x 81 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('dataset_real.pkl', 'rb') as f:\n",
    "    dataset=pkl.load(f)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 새로운 '적발건수' column 만들기\n",
    "dataset['적발건수'] = dataset['적발횟수_1'] + dataset['적발횟수_2'] + dataset['적발횟수_3'] + dataset['적발횟수_4'] + dataset['적발횟수_5'] + dataset['적발횟수_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>적발건수</th>\n",
       "      <th>적발횟수_1</th>\n",
       "      <th>적발횟수_2</th>\n",
       "      <th>적발횟수_3</th>\n",
       "      <th>적발횟수_4</th>\n",
       "      <th>적발횟수_5</th>\n",
       "      <th>적발횟수_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171711</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>188839</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>190500</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196641</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199462</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199489</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205656</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207565</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213681</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213855</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        적발건수  적발횟수_1  적발횟수_2  적발횟수_3  적발횟수_4  적발횟수_5  적발횟수_6\n",
       "171711     1       1       0       0       0       0       0\n",
       "188839     1       1       0       0       0       0       0\n",
       "190500     1       1       0       0       0       0       0\n",
       "196641     1       1       0       0       0       0       0\n",
       "199462     1       1       0       0       0       0       0\n",
       "199489     1       1       0       0       0       0       0\n",
       "205656     1       1       0       0       0       0       0\n",
       "207565     2       2       0       0       0       0       0\n",
       "213681     1       1       0       0       0       0       0\n",
       "213855     4       4       0       0       0       0       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# '적발건수' 칼럼이 제대로 들어갔는지 확인\n",
    "dataset[['적발건수', '적발횟수_1', '적발횟수_2', '적발횟수_3', '적발횟수_4', '적발횟수_5', '적발횟수_6']][dataset.적발건수>0].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 이후에 해당 그리드의 id와 행정동을 알아보기 위해 2개를 매칭시킨 칼럼 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125           162_공항동\n",
       "24732        4397_공항동\n",
       "40812        5879_공항동\n",
       "46445        6192_공항동\n",
       "46446        6192_공항동\n",
       "              ...    \n",
       "8997477    110992_강일동\n",
       "9002659    111610_강일동\n",
       "9002660    111610_강일동\n",
       "9002702    111610_강일동\n",
       "9002757    111610_강일동\n",
       "Name: id_행정동_match, Length: 92614, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['id_행정동_match'] = list(map(lambda x, y : str(int(x)) + '_' + y, dataset['id'], dataset['행정동']))\n",
    "dataset['id_행정동_match']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### id_행정동 매치 시킨 것을 가장 많은 순서대로 정렬시켜서 Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['92038_가락1동',\n",
       " '73220_제기동',\n",
       " '73218_제기동',\n",
       " '74556_양재2동',\n",
       " '73522_제기동',\n",
       " '72917_제기동',\n",
       " '73219_제기동',\n",
       " '73217_제기동',\n",
       " '72750_양재2동',\n",
       " '89301_잠실3동',\n",
       " '73230_마장동',\n",
       " '36493_독산3동',\n",
       " '89206_망우본동',\n",
       " '37607_망원2동',\n",
       " '73231_마장동',\n",
       " '78053_전농1동',\n",
       " '37606_망원2동',\n",
       " '72615_제기동',\n",
       " '50608_공덕동',\n",
       " '97411_암사1동',\n",
       " '97412_암사1동',\n",
       " '96284_문정2동',\n",
       " '97410_암사1동',\n",
       " '35787_성산2동',\n",
       " '72918_제기동',\n",
       " '69371_논현1동',\n",
       " '72616_제기동',\n",
       " '37911_망원1동',\n",
       " '85679_잠실본동',\n",
       " '73823_제기동',\n",
       " '98947_성내3동',\n",
       " '103509_마천2동',\n",
       " '43732_신원동',\n",
       " '97110_암사1동',\n",
       " '72928_마장동',\n",
       " '75253_창5동',\n",
       " '35292_독산1동',\n",
       " '77159_답십리1동',\n",
       " '57919_사당2동',\n",
       " '98644_성내3동',\n",
       " '69373_논현1동',\n",
       " '69372_논현1동',\n",
       " '80213_자양4동',\n",
       " '64845_반포4동',\n",
       " '40981_신길1동',\n",
       " '94457_문정2동',\n",
       " '85903_면목7동',\n",
       " '103208_마천2동',\n",
       " '78360_답십리2동',\n",
       " '52391_천연동',\n",
       " '36816_시흥1동',\n",
       " '84141_자양1동',\n",
       " '79607_자양4동',\n",
       " '73834_마장동',\n",
       " '21014_화곡본동',\n",
       " '96216_천호2동',\n",
       " '41842_서교동',\n",
       " '47839_홍제3동',\n",
       " '86798_면목본동',\n",
       " '72745_양재2동',\n",
       " '72919_제기동',\n",
       " '73845_사근동',\n",
       " '94773_문정2동',\n",
       " '73824_제기동',\n",
       " '40595_응암3동',\n",
       " '50306_공덕동',\n",
       " '81416_화양동',\n",
       " '73222_용신동',\n",
       " '33738_구로5동',\n",
       " '54008_인헌동',\n",
       " '95357_가락본동',\n",
       " '67108_수유2동',\n",
       " '84411_중곡1동',\n",
       " '68632_수유1동',\n",
       " '32838_구로5동',\n",
       " '19213_신월1동',\n",
       " '85678_잠실본동',\n",
       " '26746_목3동',\n",
       " '92318_방이2동',\n",
       " '85981_잠실본동',\n",
       " '73520_제기동',\n",
       " '80081_상계2동',\n",
       " '37416_시흥1동',\n",
       " '21316_화곡본동',\n",
       " '48229_노량진1동',\n",
       " '76536_전농1동',\n",
       " '73723_도봉1동',\n",
       " '91102_잠실4동',\n",
       " '73533_마장동',\n",
       " '44331_신림동',\n",
       " '67166_삼선동',\n",
       " '51279_행운동',\n",
       " '38554_영등포동',\n",
       " '38934_시흥5동',\n",
       " '72851_창2동',\n",
       " '25555_화곡4동',\n",
       " '72916_제기동',\n",
       " '58534_방배2동',\n",
       " '72915_제기동',\n",
       " '40897_응암3동',\n",
       " '101697_마천2동',\n",
       " '69069_논현1동',\n",
       " '85680_잠실본동',\n",
       " '98054_오륜동',\n",
       " '57617_사당2동',\n",
       " '85904_면목7동',\n",
       " '28862_목2동',\n",
       " '50007_도화동',\n",
       " '103813_마천1동',\n",
       " '73216_제기동',\n",
       " '74425_제기동',\n",
       " '80212_자양4동',\n",
       " '72914_제기동',\n",
       " '76837_전농1동',\n",
       " '80781_장안1동',\n",
       " '79823_월계1동',\n",
       " '29484_목1동',\n",
       " '64514_한남동',\n",
       " '96272_문정1동',\n",
       " '38631_시흥5동',\n",
       " '19195_발산1동',\n",
       " '72219_방학2동',\n",
       " '96818_천호2동',\n",
       " '42142_서교동',\n",
       " '72548_창2동',\n",
       " '80556_대치2동',\n",
       " '80486_장안1동',\n",
       " '85603_면목7동',\n",
       " '73532_마장동',\n",
       " '52126_용문동',\n",
       " '81172_개포2동',\n",
       " '37609_망원1동',\n",
       " '100739_천호1동',\n",
       " '40706_신사동',\n",
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       " '47960_청룡동',\n",
       " '68664_길음1동',\n",
       " '57918_사당2동',\n",
       " '36818_시흥1동',\n",
       " '35575_구로3동',\n",
       " '81003_중계2,3동',\n",
       " '85647_구의1동',\n",
       " '81716_화양동',\n",
       " '77737_회기동',\n",
       " '79879_장안1동',\n",
       " '38214_망원1동',\n",
       " '23420_화곡6동',\n",
       " '42744_서교동',\n",
       " '41767_불광2동',\n",
       " '96550_방이1동',\n",
       " '41022_미성동',\n",
       " '36491_독산3동',\n",
       " '71730_행당1동',\n",
       " '34057_구로4동',\n",
       " '56930_소공동',\n",
       " '95346_방이1동',\n",
       " '68099_황학동',\n",
       " '49103_도화동',\n",
       " '81415_화양동',\n",
       " '79240_이문1동',\n",
       " '68320_수유3동',\n",
       " '49404_도화동',\n",
       " '69928_금호1가동',\n",
       " '46126_상도4동',\n",
       " '17745_수궁동',\n",
       " '55400_청운효자동',\n",
       " '92649_문정2동',\n",
       " '73229_마장동',\n",
       " '99542_길동',\n",
       " '20732_신월2동',\n",
       " '82191_중계4동',\n",
       " '98025_천호3동',\n",
       " '58133_명동',\n",
       " '86799_면목본동',\n",
       " '99225_암사1동',\n",
       " '32851_구로2동',\n",
       " '21317_화곡본동',\n",
       " '96862_가락본동',\n",
       " '55508_사당4동',\n",
       " '35529_당산1동',\n",
       " '53320_청파동',\n",
       " '43733_신원동',\n",
       " '82534_공릉1동',\n",
       " '95056_가락본동',\n",
       " '79543_이문1동',\n",
       " '12236_공항동',\n",
       " '103210_마천1동',\n",
       " '83741_공릉2동',\n",
       " '35595_독산1동',\n",
       " '35528_당산1동',\n",
       " '77483_성수2가3동',\n",
       " '87691_상봉1동',\n",
       " '72309_제기동',\n",
       " '77752_전농1동',\n",
       " '83223_화양동',\n",
       " '59348_명동',\n",
       " '101342_명일1동',\n",
       " '72519_방학2동',\n",
       " '56933_회현동',\n",
       " '97128_성내2동',\n",
       " '68631_수유1동',\n",
       " '87977_신내2동',\n",
       " '78940_이문1동',\n",
       " '53705_인헌동',\n",
       " '57616_사당2동',\n",
       " '71429_행당1동',\n",
       " '41464_갈현1동',\n",
       " '91713_방이2동',\n",
       " '65699_약수동',\n",
       " '96808_암사2동',\n",
       " '72283_월곡1동',\n",
       " '55098_청운효자동',\n",
       " '79249_휘경2동',\n",
       " '24319_등촌1동',\n",
       " '73152_창2동',\n",
       " '94411_풍납2동',\n",
       " '38514_망원1동',\n",
       " '31350_가산동',\n",
       " '50978_행운동',\n",
       " '74621_도봉1동',\n",
       " '94406_풍납1동',\n",
       " '27082_신정6동',\n",
       " '38555_영등포동',\n",
       " '89813_망우3동',\n",
       " '76507_장위2동',\n",
       " '81285_상계2동',\n",
       " '74427_청량리동',\n",
       " '96882_장지동',\n",
       " '26745_목3동',\n",
       " '42445_서교동',\n",
       " '55103_사직동',\n",
       " '53319_청파동',\n",
       " '52764_흑석동',\n",
       " '84410_중곡1동',\n",
       " '82008_군자동',\n",
       " '76526_회기동',\n",
       " '44619_상도3동',\n",
       " '26744_목3동',\n",
       " '65979_창신2동',\n",
       " '101704_거여2동',\n",
       " '56055_한강로동',\n",
       " '85075_잠실본동',\n",
       " '81418_자양4동',\n",
       " '35172_상암동',\n",
       " '23759_신정3동',\n",
       " '57628_사당1동',\n",
       " '35235_문래동',\n",
       " '70766_송천동',\n",
       " '100186_오금동',\n",
       " '96878_문정1동',\n",
       " '75415_역삼1동',\n",
       " '77237_대치1동',\n",
       " '75228_도봉2동',\n",
       " '33775_가산동',\n",
       " '27100_고척1동',\n",
       " '97127_성내2동',\n",
       " '35563_대림3동',\n",
       " '95058_가락본동',\n",
       " '75227_도봉2동',\n",
       " '10412_방화2동',\n",
       " '84772_잠실본동',\n",
       " '38557_영등포동',\n",
       " '46063_신촌동',\n",
       " '96517_천호2동',\n",
       " '33453_구로4동',\n",
       " '47959_은천동',\n",
       " '19517_신월1동',\n",
       " '67426_수유1동',\n",
       " '37434_시흥3동',\n",
       " '49996_아현동',\n",
       " '34043_구로5동',\n",
       " '66370_서초3동',\n",
       " '52693_천연동',\n",
       " '80148_이문1동',\n",
       " '79273_답십리2동',\n",
       " '25253_화곡4동',\n",
       " '71112_용신동',\n",
       " '83434_공릉2동',\n",
       " '78574_상계6,7동',\n",
       " '80784_장안1동',\n",
       " '66805_우이동',\n",
       " '70469_길음2동',\n",
       " '79301_성수2가3동',\n",
       " '59649_명동',\n",
       " '71917_방학2동',\n",
       " '69027_금호4가동',\n",
       " '80373_상계9동',\n",
       " '95358_가락본동',\n",
       " '24091_개봉2동',\n",
       " '18910_신월1동',\n",
       " '80515_자양4동',\n",
       " '49705_도화동',\n",
       " '46464_대학동',\n",
       " '26167_신정4동',\n",
       " '66371_서초3동',\n",
       " '84470_잠실본동',\n",
       " '47536_홍은1동',\n",
       " '22235_화곡8동',\n",
       " '81284_상계5동',\n",
       " '94755_가락본동',\n",
       " '50606_공덕동',\n",
       " '74429_용신동',\n",
       " '59119_방배본동',\n",
       " '68040_삼양동',\n",
       " '41236_서교동',\n",
       " '73221_용신동',\n",
       " '73531_마장동',\n",
       " '29194_신정2동',\n",
       " '48492_염리동',\n",
       " '37953_영등포동',\n",
       " '42138_서교동',\n",
       " '37910_망원2동',\n",
       " '77131_회기동',\n",
       " '23721_화곡6동',\n",
       " '66667_서초4동',\n",
       " '77645_상계1동',\n",
       " '41796_응암2동',\n",
       " '77642_상계1동',\n",
       " '72923_용신동',\n",
       " '45775_신수동',\n",
       " '36800_독산4동',\n",
       " '78909_월계1동',\n",
       " '36482_구로3동',\n",
       " '77436_회기동',\n",
       " '36084_성산2동',\n",
       " '73947_양재2동',\n",
       " '42418_남가좌2동',\n",
       " '93529_방이2동',\n",
       " '21315_화곡본동',\n",
       " '69673_논현1동',\n",
       " '33152_구로4동',\n",
       " '39201_조원동',\n",
       " '21034_신월2동',\n",
       " '83566_잠실7동',\n",
       " '45461_신촌동',\n",
       " '38254_영등포동',\n",
       " '96521_성내2동',\n",
       " '57025_사당1동',\n",
       " '71622_쌍문4동',\n",
       " '75251_창5동',\n",
       " '47896_용강동',\n",
       " '90200_잠실6동',\n",
       " '29817_구로2동',\n",
       " '42139_서교동',\n",
       " '50054_상도1동',\n",
       " '34003_양평2동',\n",
       " '69600_용신동',\n",
       " '78669_답십리2동',\n",
       " '102906_마천2동',\n",
       " '76202_장위2동',\n",
       " '31932_구로5동',\n",
       " '20117_화곡1동',\n",
       " '41845_서교동',\n",
       " '84472_잠실본동',\n",
       " '101034_명일1동',\n",
       " '65396_다산동',\n",
       " '76027_역삼2동',\n",
       " '71111_용신동',\n",
       " '92015_방이2동',\n",
       " '32830_신도림동',\n",
       " '85047_자양1동',\n",
       " '33657_상암동',\n",
       " '21038_신월4동',\n",
       " '57916_사당2동',\n",
       " '16796_신월3동',\n",
       " '67712_수유2동',\n",
       " '68415_금호2,3가동',\n",
       " '99247_성내3동',\n",
       " '70257_신사동(강남동)',\n",
       " '91729_석촌동',\n",
       " '39412_성산1동',\n",
       " '73518_제기동',\n",
       " '95057_가락본동',\n",
       " '75846_방학1동',\n",
       " '36210_시흥1동',\n",
       " '41769_대조동',\n",
       " '72929_마장동',\n",
       " '40930_서교동',\n",
       " '69601_용신동',\n",
       " '82832_공릉1동',\n",
       " '31654_가산동',\n",
       " '104354_명일2동',\n",
       " '44034_서원동',\n",
       " '72930_마장동',\n",
       " '39962_갈현2동',\n",
       " '84444_자양1동',\n",
       " '26469_신정4동',\n",
       " '84420_능동',\n",
       " '27048_목3동',\n",
       " '53645_한강로동',\n",
       " '73544_사근동',\n",
       " '48140_홍제3동',\n",
       " '47585_대흥동',\n",
       " '44594_여의동',\n",
       " '71732_행당1동',\n",
       " '92017_방이2동',\n",
       " '74928_도봉2동',\n",
       " '47841_홍제1동',\n",
       " '85095_일원1동',\n",
       " '50379_낙성대동',\n",
       " '82980_일원2동',\n",
       " '40707_신사동',\n",
       " '91714_방이2동',\n",
       " '65996_다산동',\n",
       " '71974_송중동',\n",
       " '59429_방배4동',\n",
       " '40264_갈현2동',\n",
       " '98949_성내3동',\n",
       " '45462_서교동',\n",
       " '93525_방이2동',\n",
       " '42443_서교동',\n",
       " '37292_성산2동',\n",
       " '27047_목3동',\n",
       " '88904_망우본동',\n",
       " '75030_청량리동',\n",
       " '42986_대조동',\n",
       " '62457_방배3동',\n",
       " '62368_필동',\n",
       " '75114_역삼1동',\n",
       " '92319_방이2동',\n",
       " '29487_목1동',\n",
       " '93831_방이2동',\n",
       " '68471_반포1동',\n",
       " '33468_가산동',\n",
       " '92030_석촌동',\n",
       " '73519_제기동',\n",
       " '26493_고척1동',\n",
       " '51823_용문동',\n",
       " '46162_대학동',\n",
       " '83392_상계3,4동',\n",
       " '42782_여의동',\n",
       " '40291_응암3동',\n",
       " '46727_상도2동',\n",
       " '78366_답십리2동',\n",
       " '98387_장지동',\n",
       " '25825_가양3동',\n",
       " '55742_남영동',\n",
       " '79043_대치2동',\n",
       " '59044_명동',\n",
       " '20737_신월4동',\n",
       " '67275_서초1동',\n",
       " '49702_아현동',\n",
       " '84771_잠실본동',\n",
       " '95615_풍납1동',\n",
       " '22233_화곡8동',\n",
       " '67221_옥수동',\n",
       " '99581_오금동',\n",
       " '23419_화곡6동',\n",
       " '38256_영등포동',\n",
       " '77945_상계1동',\n",
       " '80480_장안1동',\n",
       " '67467_동선동',\n",
       " '79226_석관동',\n",
       " '53649_한강로동',\n",
       " '20431_신월4동',\n",
       " '77133_회기동',\n",
       " '48143_홍제3동',\n",
       " '35849_도림동',\n",
       " '74933_방학1동',\n",
       " '45253_삼성동',\n",
       " '37415_시흥1동',\n",
       " '94753_가락본동',\n",
       " '86228_중곡2동',\n",
       " '43045_서교동',\n",
       " '69361_신사동(강남동)',\n",
       " '86834_중곡4동',\n",
       " '92634_송파2동',\n",
       " '41461_갈현1동',\n",
       " '96282_문정2동',\n",
       " '67468_동선동',\n",
       " '33769_가산동',\n",
       " '79910_자양4동',\n",
       " '57925_사당1동',\n",
       " '95317_성내1동',\n",
       " '44920_상도3동',\n",
       " '24701_개봉3동',\n",
       " '21367_오류1동',\n",
       " '40034_합정동',\n",
       " '61549_방배1동',\n",
       " '94756_가락본동',\n",
       " '70502_용신동',\n",
       " '36796_독산3동',\n",
       " '77785_성수2가3동',\n",
       " '20739_신월4동',\n",
       " '83815_군자동',\n",
       " '103513_마천1동',\n",
       " '66675_서초3동',\n",
       " '95669_문정1동',\n",
       " '42148_서강동',\n",
       " '57933_남현동',\n",
       " '96502_암사2동',\n",
       " '37348_영등포동',\n",
       " '102300_마천2동',\n",
       " '85344_구의1동',\n",
       " '61190_이태원1동',\n",
       " '81587_상계5동',\n",
       " '95668_문정1동',\n",
       " '41834_연남동',\n",
       " '79828_석관동',\n",
       " '62948_혜화동',\n",
       " '98946_성내3동',\n",
       " '48861_중앙동',\n",
       " '38255_영등포동',\n",
       " '36821_시흥1동',\n",
       " '80487_장안1동',\n",
       " '33141_구로5동',\n",
       " '45760_신촌동',\n",
       " '79207_월계3동',\n",
       " '76861_용답동',\n",
       " '46766_대학동',\n",
       " '45238_신림동',\n",
       " '85949_구의1동',\n",
       " '103508_마천2동',\n",
       " '70805_용신동',\n",
       " '83474_면목2동',\n",
       " '41789_응암1동',\n",
       " '95055_가락본동',\n",
       " '42141_서교동',\n",
       " '92026_송파1동',\n",
       " '57917_사당2동',\n",
       " '44807_녹번동',\n",
       " '40033_합정동',\n",
       " '78365_답십리2동',\n",
       " '75339_전농1동',\n",
       " '92571_광장동',\n",
       " '59940_종로1,2,3,4가동',\n",
       " '29788_목1동',\n",
       " '84716_중곡2동',\n",
       " '25297_개봉2동',\n",
       " '95622_성내1동',\n",
       " '20380_가양1동',\n",
       " '42743_서교동',\n",
       " '79822_월계1동',\n",
       " '51205_공덕동',\n",
       " '81414_화양동',\n",
       " '79878_장안1동',\n",
       " '69237_미아동',\n",
       " '69677_논현1동',\n",
       " '72612_제기동',\n",
       " '79778_상계2동',\n",
       " '36438_당산1동',\n",
       " '95644_방이1동',\n",
       " '19488_발산1동',\n",
       " '79180_상계6,7동',\n",
       " '68416_금호2,3가동',\n",
       " '78731_삼성1동',\n",
       " '81711_화양동',\n",
       " '83472_상봉2동',\n",
       " '38251_영등포동',\n",
       " '72225_쌍문2동',\n",
       " '34991_독산1동',\n",
       " '74511_역삼1동',\n",
       " '108888_상일동',\n",
       " '26166_신정4동',\n",
       " '60241_종로1,2,3,4가동',\n",
       " '88664_광장동',\n",
       " '98678_가락2동',\n",
       " '69228_수유3동',\n",
       " '38770_신사1동',\n",
       " '72719_도곡1동',\n",
       " '34986_독산1동',\n",
       " '20090_등촌3동',\n",
       " '62066_필동',\n",
       " '97409_암사1동',\n",
       " '79911_자양4동',\n",
       " '34644_대림3동',\n",
       " '53949_한강로동',\n",
       " '69975_논현1동',\n",
       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dong_lst = list(dataset['id_행정동_match'].value_counts().index)\n",
    "dong_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92038_가락1동      106\n",
       "73220_제기동       101\n",
       "73218_제기동        70\n",
       "74556_양재2동       68\n",
       "73522_제기동        68\n",
       "               ... \n",
       "44843_연희동         1\n",
       "52158_흑석동         1\n",
       "46262_진관동         1\n",
       "76276_성수1가2동      1\n",
       "47020_노량진2동       1\n",
       "Name: id_행정동_match, Length: 16235, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['id_행정동_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       " 15375,\n",
       " 15374,\n",
       " 15373,\n",
       " 15372,\n",
       " 15371,\n",
       " 15370,\n",
       " 15369,\n",
       " 15368,\n",
       " 15367,\n",
       " 15366,\n",
       " 15365,\n",
       " 15364,\n",
       " 15363,\n",
       " 15362,\n",
       " 15361,\n",
       " 15360,\n",
       " 15359,\n",
       " 15358,\n",
       " 15357,\n",
       " 15356,\n",
       " 15355,\n",
       " 15354,\n",
       " 15353,\n",
       " 15352,\n",
       " 15351,\n",
       " 15350,\n",
       " 15349,\n",
       " 15348,\n",
       " 15347,\n",
       " 15346,\n",
       " 15345,\n",
       " 15344,\n",
       " 15343,\n",
       " 15342,\n",
       " 15341,\n",
       " 15340,\n",
       " 15339,\n",
       " 15338,\n",
       " 15337,\n",
       " 15336,\n",
       " 15335,\n",
       " 15334,\n",
       " 15333,\n",
       " 15332,\n",
       " 15331,\n",
       " 15330,\n",
       " 15329,\n",
       " 15328,\n",
       " 15327,\n",
       " 15326,\n",
       " 15325,\n",
       " 15324,\n",
       " 15323,\n",
       " 15322,\n",
       " 15321,\n",
       " 15320,\n",
       " 15319,\n",
       " 15318,\n",
       " 15317,\n",
       " 15316,\n",
       " 15315,\n",
       " 15314,\n",
       " 15313,\n",
       " 15312,\n",
       " 15311,\n",
       " 15310,\n",
       " 15309,\n",
       " 15308,\n",
       " 15307,\n",
       " 15306,\n",
       " 15305,\n",
       " 15304,\n",
       " 15303,\n",
       " 15302,\n",
       " 15301,\n",
       " 15300,\n",
       " 15299,\n",
       " 15298,\n",
       " 15297,\n",
       " 15296,\n",
       " 15295,\n",
       " 15294,\n",
       " 15293,\n",
       " 15292,\n",
       " 15291,\n",
       " 15290,\n",
       " 15289,\n",
       " 15288,\n",
       " 15287,\n",
       " 15286,\n",
       " 15285,\n",
       " 15284,\n",
       " 15283,\n",
       " 15282,\n",
       " 15281,\n",
       " 15280,\n",
       " 15279,\n",
       " 15278,\n",
       " 15277,\n",
       " 15276,\n",
       " 15275,\n",
       " 15274,\n",
       " 15273,\n",
       " 15272,\n",
       " 15271,\n",
       " 15270,\n",
       " 15269,\n",
       " 15268,\n",
       " 15267,\n",
       " 15266,\n",
       " 15265,\n",
       " 15264,\n",
       " 15263,\n",
       " 15262,\n",
       " 15261,\n",
       " 15260,\n",
       " 15259,\n",
       " 15258,\n",
       " 15257,\n",
       " 15256,\n",
       " 15255,\n",
       " 15254,\n",
       " 15253,\n",
       " 15252,\n",
       " 15251,\n",
       " 15250,\n",
       " 15249,\n",
       " 15248,\n",
       " 15247,\n",
       " 15246,\n",
       " 15245,\n",
       " 15244,\n",
       " 15243,\n",
       " 15242,\n",
       " 15241,\n",
       " 15240,\n",
       " 15239,\n",
       " 15238,\n",
       " 15237,\n",
       " 15236,\n",
       " ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(len(dong_lst), 0, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'92038_가락1동': 16235,\n",
       " '73220_제기동': 16234,\n",
       " '73218_제기동': 16233,\n",
       " '74556_양재2동': 16232,\n",
       " '73522_제기동': 16231,\n",
       " '72917_제기동': 16230,\n",
       " '73219_제기동': 16229,\n",
       " '73217_제기동': 16228,\n",
       " '72750_양재2동': 16227,\n",
       " '89301_잠실3동': 16226,\n",
       " '73230_마장동': 16225,\n",
       " '36493_독산3동': 16224,\n",
       " '89206_망우본동': 16223,\n",
       " '37607_망원2동': 16222,\n",
       " '73231_마장동': 16221,\n",
       " '78053_전농1동': 16220,\n",
       " '37606_망원2동': 16219,\n",
       " '72615_제기동': 16218,\n",
       " '50608_공덕동': 16217,\n",
       " '97411_암사1동': 16216,\n",
       " '97412_암사1동': 16215,\n",
       " '96284_문정2동': 16214,\n",
       " '97410_암사1동': 16213,\n",
       " '35787_성산2동': 16212,\n",
       " '72918_제기동': 16211,\n",
       " '69371_논현1동': 16210,\n",
       " '72616_제기동': 16209,\n",
       " '37911_망원1동': 16208,\n",
       " '85679_잠실본동': 16207,\n",
       " '73823_제기동': 16206,\n",
       " '98947_성내3동': 16205,\n",
       " '103509_마천2동': 16204,\n",
       " '43732_신원동': 16203,\n",
       " '97110_암사1동': 16202,\n",
       " '72928_마장동': 16201,\n",
       " '75253_창5동': 16200,\n",
       " '35292_독산1동': 16199,\n",
       " '77159_답십리1동': 16198,\n",
       " '57919_사당2동': 16197,\n",
       " '98644_성내3동': 16196,\n",
       " '69373_논현1동': 16195,\n",
       " '69372_논현1동': 16194,\n",
       " '80213_자양4동': 16193,\n",
       " '64845_반포4동': 16192,\n",
       " '40981_신길1동': 16191,\n",
       " '94457_문정2동': 16190,\n",
       " '85903_면목7동': 16189,\n",
       " '103208_마천2동': 16188,\n",
       " '78360_답십리2동': 16187,\n",
       " '52391_천연동': 16186,\n",
       " '36816_시흥1동': 16185,\n",
       " '84141_자양1동': 16184,\n",
       " '79607_자양4동': 16183,\n",
       " '73834_마장동': 16182,\n",
       " '21014_화곡본동': 16181,\n",
       " '96216_천호2동': 16180,\n",
       " '41842_서교동': 16179,\n",
       " '47839_홍제3동': 16178,\n",
       " '86798_면목본동': 16177,\n",
       " '72745_양재2동': 16176,\n",
       " '72919_제기동': 16175,\n",
       " '73845_사근동': 16174,\n",
       " '94773_문정2동': 16173,\n",
       " '73824_제기동': 16172,\n",
       " '40595_응암3동': 16171,\n",
       " '50306_공덕동': 16170,\n",
       " '81416_화양동': 16169,\n",
       " '73222_용신동': 16168,\n",
       " '33738_구로5동': 16167,\n",
       " '54008_인헌동': 16166,\n",
       " '95357_가락본동': 16165,\n",
       " '67108_수유2동': 16164,\n",
       " '84411_중곡1동': 16163,\n",
       " '68632_수유1동': 16162,\n",
       " '32838_구로5동': 16161,\n",
       " '19213_신월1동': 16160,\n",
       " '85678_잠실본동': 16159,\n",
       " '26746_목3동': 16158,\n",
       " '92318_방이2동': 16157,\n",
       " '85981_잠실본동': 16156,\n",
       " '73520_제기동': 16155,\n",
       " '80081_상계2동': 16154,\n",
       " '37416_시흥1동': 16153,\n",
       " '21316_화곡본동': 16152,\n",
       " '48229_노량진1동': 16151,\n",
       " '76536_전농1동': 16150,\n",
       " '73723_도봉1동': 16149,\n",
       " '91102_잠실4동': 16148,\n",
       " '73533_마장동': 16147,\n",
       " '44331_신림동': 16146,\n",
       " '67166_삼선동': 16145,\n",
       " '51279_행운동': 16144,\n",
       " '38554_영등포동': 16143,\n",
       " '38934_시흥5동': 16142,\n",
       " '72851_창2동': 16141,\n",
       " '25555_화곡4동': 16140,\n",
       " '72916_제기동': 16139,\n",
       " '58534_방배2동': 16138,\n",
       " '72915_제기동': 16137,\n",
       " '40897_응암3동': 16136,\n",
       " '101697_마천2동': 16135,\n",
       " '69069_논현1동': 16134,\n",
       " '85680_잠실본동': 16133,\n",
       " '98054_오륜동': 16132,\n",
       " '57617_사당2동': 16131,\n",
       " '85904_면목7동': 16130,\n",
       " '28862_목2동': 16129,\n",
       " '50007_도화동': 16128,\n",
       " '103813_마천1동': 16127,\n",
       " '73216_제기동': 16126,\n",
       " '74425_제기동': 16125,\n",
       " '80212_자양4동': 16124,\n",
       " '72914_제기동': 16123,\n",
       " '76837_전농1동': 16122,\n",
       " '80781_장안1동': 16121,\n",
       " '79823_월계1동': 16120,\n",
       " '29484_목1동': 16119,\n",
       " '64514_한남동': 16118,\n",
       " '96272_문정1동': 16117,\n",
       " '38631_시흥5동': 16116,\n",
       " '19195_발산1동': 16115,\n",
       " '72219_방학2동': 16114,\n",
       " '96818_천호2동': 16113,\n",
       " '42142_서교동': 16112,\n",
       " '72548_창2동': 16111,\n",
       " '80556_대치2동': 16110,\n",
       " '80486_장안1동': 16109,\n",
       " '85603_면목7동': 16108,\n",
       " '73532_마장동': 16107,\n",
       " '52126_용문동': 16106,\n",
       " '81172_개포2동': 16105,\n",
       " '37609_망원1동': 16104,\n",
       " '100739_천호1동': 16103,\n",
       " '40706_신사동': 16102,\n",
       " '73543_사근동': 16101,\n",
       " '66974_서초1동': 16100,\n",
       " '80789_장안1동': 16099,\n",
       " '39732_합정동': 16098,\n",
       " '27350_목3동': 16097,\n",
       " '101049_길동': 16096,\n",
       " '79477_상계2동': 16095,\n",
       " '37411_독산2동': 16094,\n",
       " '68100_황학동': 16093,\n",
       " '61850_방배1동': 16092,\n",
       " '73225_용신동': 16091,\n",
       " '79651_대치2동': 16090,\n",
       " '80819_자양4동': 16089,\n",
       " '102907_마천2동': 16088,\n",
       " '52429_용문동': 16087,\n",
       " '52694_천연동': 16086,\n",
       " '76535_전농1동': 16085,\n",
       " '90824_석촌동': 16084,\n",
       " '85602_면목7동': 16083,\n",
       " '45094_불광1동': 16082,\n",
       " '46747_은천동': 16081,\n",
       " '52127_용문동': 16080,\n",
       " '56642_후암동': 16079,\n",
       " '72617_제기동': 16078,\n",
       " '37910_망원1동': 16077,\n",
       " '74428_전농1동': 16076,\n",
       " '98633_천호3동': 16075,\n",
       " '104353_명일2동': 16074,\n",
       " '21658_개봉1동': 16073,\n",
       " '63639_반포4동': 16072,\n",
       " '80485_장안1동': 16071,\n",
       " '65978_창신2동': 16070,\n",
       " '71731_행당1동': 16069,\n",
       " '76633_역삼2동': 16068,\n",
       " '50897_아현동': 16067,\n",
       " '69530_수유3동': 16066,\n",
       " '81601_중계1동': 16065,\n",
       " '73835_마장동': 16064,\n",
       " '68724_금호2,3가동': 16063,\n",
       " '36817_시흥1동': 16062,\n",
       " '102307_거여2동': 16061,\n",
       " '71428_행당1동': 16060,\n",
       " '78066_답십리1동': 16059,\n",
       " '89207_망우본동': 16058,\n",
       " '73846_사근동': 16057,\n",
       " '76158_창4동': 16056,\n",
       " '82288_장안2동': 16055,\n",
       " '78063_답십리2동': 16054,\n",
       " '86505_면목3,8동': 16053,\n",
       " '75252_창5동': 16052,\n",
       " '65755_반포4동': 16051,\n",
       " '57930_사당1동': 16050,\n",
       " '55811_사당4동': 16049,\n",
       " '37912_망원1동': 16048,\n",
       " '44618_상도3동': 16047,\n",
       " '73521_제기동': 16046,\n",
       " '87402_면목본동': 16045,\n",
       " '44944_서원동': 16044,\n",
       " '48262_청룡동': 16043,\n",
       " '68934_수유1동': 16042,\n",
       " '69902_용신동': 16041,\n",
       " '85982_잠실본동': 16040,\n",
       " '84773_잠실본동': 16039,\n",
       " '45763_신촌동': 16038,\n",
       " '45250_서림동': 16037,\n",
       " '73154_창3동': 16036,\n",
       " '15564_발산1동': 16035,\n",
       " '98380_가락2동': 16034,\n",
       " '81720_자양4동': 16033,\n",
       " '58740_명동': 16032,\n",
       " '32549_구로4동': 16031,\n",
       " '78129_삼성1동': 16030,\n",
       " '79779_상계2동': 16029,\n",
       " '78367_답십리2동': 16028,\n",
       " '67496_신당동': 16027,\n",
       " '24687_고척1동': 16026,\n",
       " '43429_신원동': 16025,\n",
       " '59037_종로1,2,3,4가동': 16024,\n",
       " '23173_고척2동': 16023,\n",
       " '73833_마장동': 16022,\n",
       " '48227_노량진1동': 16021,\n",
       " '75403_삼성2동': 16020,\n",
       " '73239_행당1동': 16019,\n",
       " '44330_신림동': 16018,\n",
       " '92016_방이2동': 16017,\n",
       " '12828_방화1동': 16016,\n",
       " '41788_응암1동': 16015,\n",
       " '85378_잠실본동': 16014,\n",
       " '92643_가락1동': 16013,\n",
       " '103207_마천2동': 16012,\n",
       " '81684_장안1동': 16011,\n",
       " '41933_난곡동': 16010,\n",
       " '88368_구의3동': 16009,\n",
       " '74124_제기동': 16008,\n",
       " '90504_잠실6동': 16007,\n",
       " '96217_천호2동': 16006,\n",
       " '37952_영등포동': 16005,\n",
       " '53066_흑석동': 16004,\n",
       " '82839_공릉1동': 16003,\n",
       " '96518_천호2동': 16002,\n",
       " '47895_용강동': 16001,\n",
       " '69683_서초4동': 16000,\n",
       " '76527_회기동': 15999,\n",
       " '36686_성산2동': 15998,\n",
       " '101051_길동': 15997,\n",
       " '41901_대방동': 15996,\n",
       " '79475_상계2동': 15995,\n",
       " '68422_금호2,3가동': 15994,\n",
       " '43731_신원동': 15993,\n",
       " '78925_석관동': 15992,\n",
       " '19214_신월1동': 15991,\n",
       " '88067_구의3동': 15990,\n",
       " '74426_청량리동': 15989,\n",
       " '98086_장지동': 15988,\n",
       " '97486_장지동': 15987,\n",
       " '79241_이문1동': 15986,\n",
       " '61851_방배1동': 15985,\n",
       " '83408_중계본동': 15984,\n",
       " '50675_중앙동': 15983,\n",
       " '78040_휘경1동': 15982,\n",
       " '97126_성내2동': 15981,\n",
       " '69068_논현1동': 15980,\n",
       " '54501_사직동': 15979,\n",
       " '43598_녹번동': 15978,\n",
       " '77338_상계1동': 15977,\n",
       " '61460_광희동': 15976,\n",
       " '98948_성내3동': 15975,\n",
       " '37436_시흥3동': 15974,\n",
       " '28916_구로2동': 15973,\n",
       " '57920_사당2동': 15972,\n",
       " '72913_제기동': 15971,\n",
       " '97155_오금동': 15970,\n",
       " '62154_방배1동': 15969,\n",
       " '74135_마장동': 15968,\n",
       " '77435_회기동': 15967,\n",
       " '74333_방학1동': 15966,\n",
       " '82893_장안1동': 15965,\n",
       " '49990_북아현동': 15964,\n",
       " '91126_석촌동': 15963,\n",
       " '66676_서초3동': 15962,\n",
       " '84713_중곡1동': 15961,\n",
       " '37351_영등포동': 15960,\n",
       " '70589_역삼1동': 15959,\n",
       " '19200_화곡3동': 15958,\n",
       " '48144_홍제1동': 15957,\n",
       " '36814_시흥1동': 15956,\n",
       " '37608_망원2동': 15955,\n",
       " '32255_가산동': 15954,\n",
       " '86497_면목본동': 15953,\n",
       " '36517_시흥1동': 15952,\n",
       " '39375_신사1동': 15951,\n",
       " '86808_면목3,8동': 15950,\n",
       " '43891_대조동': 15949,\n",
       " '41489_응암1동': 15948,\n",
       " '66864_삼선동': 15947,\n",
       " '47537_홍제3동': 15946,\n",
       " '59339_종로1,2,3,4가동': 15945,\n",
       " '38558_영등포동': 15944,\n",
       " '21931_화곡8동': 15943,\n",
       " '59120_방배본동': 15942,\n",
       " '58535_방배2동': 15941,\n",
       " '99882_오금동': 15940,\n",
       " '85346_구의1동': 15939,\n",
       " '42815_신대방2동': 15938,\n",
       " '44241_연희동': 15937,\n",
       " '81975_장안2동': 15936,\n",
       " '48559_은천동': 15935,\n",
       " '74727_청량리동': 15934,\n",
       " '95019_성내1동': 15933,\n",
       " '31954_가산동': 15932,\n",
       " '82836_공릉1동': 15931,\n",
       " '34377_가산동': 15930,\n",
       " '65700_약수동': 15929,\n",
       " '18911_신월1동': 15928,\n",
       " '47029_상도2동': 15927,\n",
       " '57831_명동': 15926,\n",
       " '33153_구로4동': 15925,\n",
       " '71633_쌍문3동': 15924,\n",
       " '36802_독산4동': 15923,\n",
       " '73343_양재2동': 15922,\n",
       " '22234_화곡8동': 15921,\n",
       " '76458_창4동': 15920,\n",
       " '86531_중곡4동': 15919,\n",
       " '57931_사당1동': 15918,\n",
       " '69070_논현1동': 15917,\n",
       " '35592_독산1동': 15916,\n",
       " '80788_장안1동': 15915,\n",
       " '37954_영등포동': 15914,\n",
       " '96827_성내2동': 15913,\n",
       " '74426_제기동': 15912,\n",
       " '80814_자양4동': 15911,\n",
       " '79242_이문1동': 15910,\n",
       " '77163_용답동': 15909,\n",
       " '25260_신정4동': 15908,\n",
       " '73931_양재1동': 15907,\n",
       " '77189_성수2가1동': 15906,\n",
       " '97460_오금동': 15905,\n",
       " '68935_수유1동': 15904,\n",
       " '77130_회기동': 15903,\n",
       " '99520_암사3동': 15902,\n",
       " '90817_석촌동': 15901,\n",
       " '78751_대치1동': 15900,\n",
       " '79175_상계2동': 15899,\n",
       " '22871_고척2동': 15898,\n",
       " '53367_흑석동': 15897,\n",
       " '72238_창1동': 15896,\n",
       " '87100_면목본동': 15895,\n",
       " '42143_서교동': 15894,\n",
       " '23118_화곡6동': 15893,\n",
       " '50907_공덕동': 15892,\n",
       " '74726_청량리동': 15891,\n",
       " '52765_흑석동': 15890,\n",
       " '72921_용신동': 15889,\n",
       " '26131_가양3동': 15888,\n",
       " '40708_신사동': 15887,\n",
       " '50898_아현동': 15886,\n",
       " '107974_고덕2동': 15885,\n",
       " '79036_삼성1동': 15884,\n",
       " '68455_신사동(강남동)': 15883,\n",
       " '72614_제기동': 15882,\n",
       " '81004_중계2,3동': 15881,\n",
       " '90233_수서동': 15880,\n",
       " '45762_신촌동': 15879,\n",
       " '37090_조원동': 15878,\n",
       " '96274_문정1동': 15877,\n",
       " '45459_신촌동': 15876,\n",
       " '82327_자양3동': 15875,\n",
       " '94134_방이2동': 15874,\n",
       " '79367_개포2동': 15873,\n",
       " '53706_인헌동': 15872,\n",
       " '41163_갈현1동': 15871,\n",
       " '102604_마천2동': 15870,\n",
       " '80753_휘경1동': 15869,\n",
       " '25562_신정4동': 15868,\n",
       " '27361_목4동': 15867,\n",
       " '77738_휘경1동': 15866,\n",
       " '99578_오금동': 15865,\n",
       " '50004_공덕동': 15864,\n",
       " '74424_제기동': 15863,\n",
       " '80813_자양4동': 15862,\n",
       " '70804_용신동': 15861,\n",
       " '87412_면목3,8동': 15860,\n",
       " '42069_불광2동': 15859,\n",
       " '38632_시흥5동': 15858,\n",
       " '72613_제기동': 15857,\n",
       " '38253_영등포동': 15856,\n",
       " '37114_시흥1동': 15855,\n",
       " '73456_창3동': 15854,\n",
       " '74441_마장동': 15853,\n",
       " '74125_제기동': 15852,\n",
       " '39503_조원동': 15851,\n",
       " '70131_수유3동': 15850,\n",
       " '58735_종로1,2,3,4가동': 15849,\n",
       " '36783_구로3동': 15848,\n",
       " '47926_노량진1동': 15847,\n",
       " '67771_동선동': 15846,\n",
       " '28281_목1동': 15845,\n",
       " '50055_상도1동': 15844,\n",
       " '44032_서원동': 15843,\n",
       " '94757_가락본동': 15842,\n",
       " '65627_삼각산동': 15841,\n",
       " '43042_서교동': 15840,\n",
       " '43892_대조동': 15839,\n",
       " '73023_양재1동': 15838,\n",
       " '92018_방이2동': 15837,\n",
       " '95657_가락본동': 15836,\n",
       " '38471_신사2동': 15835,\n",
       " '25569_신정1동': 15834,\n",
       " '41010_신사동': 15833,\n",
       " '33455_구로4동': 15832,\n",
       " '101640_명일1동': 15831,\n",
       " '40311_남가좌1동': 15830,\n",
       " '63269_광희동': 15829,\n",
       " '73325_양재1동': 15828,\n",
       " '46746_보라매동': 15827,\n",
       " '57921_사당2동': 15826,\n",
       " '80099_중계2,3동': 15825,\n",
       " '32548_구로2동': 15824,\n",
       " '94752_가락본동': 15823,\n",
       " '69994_서초2동': 15822,\n",
       " '87675_신내2동': 15821,\n",
       " '82869_상봉2동': 15820,\n",
       " '88902_망우본동': 15819,\n",
       " '80382_상계2동': 15818,\n",
       " '85074_잠실본동': 15817,\n",
       " '27362_목4동': 15816,\n",
       " '55405_사직동': 15815,\n",
       " '85302_면목7동': 15814,\n",
       " '45460_신촌동': 15813,\n",
       " '37115_시흥1동': 15812,\n",
       " '44015_상도3동': 15811,\n",
       " '57326_사당1동': 15810,\n",
       " '40709_신사동': 15809,\n",
       " '50908_공덕동': 15808,\n",
       " '43347_서교동': 15807,\n",
       " '91125_석촌동': 15806,\n",
       " '68106_동화동': 15805,\n",
       " '68402_황학동': 15804,\n",
       " '35894_독산4동': 15803,\n",
       " '81717_화양동': 15802,\n",
       " '37608_망원1동': 15801,\n",
       " '83439_공릉1동': 15800,\n",
       " '40143_시흥2동': 15799,\n",
       " '96885_장지동': 15798,\n",
       " '36794_독산3동': 15797,\n",
       " '44202_녹번동': 15796,\n",
       " '95680_문정2동': 15795,\n",
       " '56071_이촌1동': 15794,\n",
       " '12829_방화1동': 15793,\n",
       " '41545_서강동': 15792,\n",
       " '75933_청량리동': 15791,\n",
       " '81310_하계1동': 15790,\n",
       " '97776_가락2동': 15789,\n",
       " '41185_응암1동': 15788,\n",
       " '38017_시흥4동': 15787,\n",
       " '83137_공릉1동': 15786,\n",
       " '73327_양재1동': 15785,\n",
       " '58837_방배2동': 15784,\n",
       " '43428_신원동': 15783,\n",
       " '47925_노량진1동': 15782,\n",
       " '72721_양재1동': 15781,\n",
       " '29787_목1동': 15780,\n",
       " '37949_영등포동': 15779,\n",
       " '45735_홍은2동': 15778,\n",
       " '82591_장안2동': 15777,\n",
       " '59118_방배본동': 15776,\n",
       " '80098_중계2,3동': 15775,\n",
       " '48803_도화동': 15774,\n",
       " '37909_망원2동': 15773,\n",
       " '76857_답십리1동': 15772,\n",
       " '92317_방이2동': 15771,\n",
       " '52159_흑석동': 15770,\n",
       " '93853_문정2동': 15769,\n",
       " '96258_가락본동': 15768,\n",
       " '80080_상계2동': 15767,\n",
       " '67798_신당동': 15766,\n",
       " '96854_오금동': 15765,\n",
       " '95010_풍납1동': 15764,\n",
       " '72531_쌍문2동': 15763,\n",
       " '42738_연남동': 15762,\n",
       " '72927_마장동': 15761,\n",
       " '77485_성수2가3동': 15760,\n",
       " '50076_청룡동': 15759,\n",
       " '74140_마장동': 15758,\n",
       " '26159_화곡4동': 15757,\n",
       " '41162_갈현1동': 15756,\n",
       " '21298_등촌3동': 15755,\n",
       " '71320_쌍문4동': 15754,\n",
       " '56632_회현동': 15753,\n",
       " '90536_수서동': 15752,\n",
       " '79476_상계2동': 15751,\n",
       " '38211_망원2동': 15750,\n",
       " '68120_금호2,3가동': 15749,\n",
       " '77493_성수2가1동': 15748,\n",
       " '65662_삼선동': 15747,\n",
       " '73427_방학1동': 15746,\n",
       " '71936_창1동': 15745,\n",
       " '48195_용강동': 15744,\n",
       " '39357_갈현2동': 15743,\n",
       " '77547_도곡2동': 15742,\n",
       " '81378_장안2동': 15741,\n",
       " '72723_양재1동': 15740,\n",
       " '50983_낙성대동': 15739,\n",
       " '38633_시흥5동': 15738,\n",
       " '36212_시흥1동': 15737,\n",
       " '71371_송중동': 15736,\n",
       " '48530_노량진1동': 15735,\n",
       " '45201_여의동': 15734,\n",
       " '81718_화양동': 15733,\n",
       " '18893_발산1동': 15732,\n",
       " '89594_잠실6동': 15731,\n",
       " '68103_신당5동': 15730,\n",
       " '70769_길음2동': 15729,\n",
       " '82589_장안2동': 15728,\n",
       " '33114_양평1동': 15727,\n",
       " '66000_약수동': 15726,\n",
       " '69688_서초2동': 15725,\n",
       " '48798_용강동': 15724,\n",
       " '47658_청룡동': 15723,\n",
       " '72304_제기동': 15722,\n",
       " '43289_대조동': 15721,\n",
       " '59939_종로1,2,3,4가동': 15720,\n",
       " '39086_증산동': 15719,\n",
       " '50075_청룡동': 15718,\n",
       " '62666_광희동': 15717,\n",
       " '52461_흑석동': 15716,\n",
       " '74141_마장동': 15715,\n",
       " '88903_망우본동': 15714,\n",
       " '89902_잠실6동': 15713,\n",
       " '79238_이문1동': 15712,\n",
       " '68113_금호2,3가동': 15711,\n",
       " '54232_청파동': 15710,\n",
       " '96519_천호2동': 15709,\n",
       " '14661_발산1동': 15708,\n",
       " '48228_노량진1동': 15707,\n",
       " '81413_화양동': 15706,\n",
       " '85376_잠실본동': 15705,\n",
       " '69828_수유3동': 15704,\n",
       " '72444_양재2동': 15703,\n",
       " '34074_가산동': 15702,\n",
       " '41902_신대방2동': 15701,\n",
       " '81079_장안1동': 15700,\n",
       " '22267_개봉1동': 15699,\n",
       " '65677_창신2동': 15698,\n",
       " '80179_장안1동': 15697,\n",
       " '72520_방학2동': 15696,\n",
       " '36174_대림2동': 15695,\n",
       " '77164_용답동': 15694,\n",
       " '60029_방배4동': 15693,\n",
       " '50305_공덕동': 15692,\n",
       " '72920_용신동': 15691,\n",
       " '57627_사당1동': 15690,\n",
       " '66001_약수동': 15689,\n",
       " '36428_당산2동': 15688,\n",
       " '99843_길동': 15687,\n",
       " '36179_구로3동': 15686,\n",
       " '25857_화곡4동': 15685,\n",
       " '68456_신사동(강남동)': 15684,\n",
       " '20684_가양1동': 15683,\n",
       " '44495_불광1동': 15682,\n",
       " '83839_자양1동': 15681,\n",
       " '57529_명동': 15680,\n",
       " '61762_광희동': 15679,\n",
       " '76829_회기동': 15678,\n",
       " '71422_왕십리도선동': 15677,\n",
       " '40716_미성동': 15676,\n",
       " '84746_자양1동': 15675,\n",
       " '32247_구로4동': 15674,\n",
       " '103751_명일2동': 15673,\n",
       " '43126_신원동': 15672,\n",
       " '76506_장위2동': 15671,\n",
       " '45475_신수동': 15670,\n",
       " '51197_북아현동': 15669,\n",
       " '47960_청룡동': 15668,\n",
       " '68664_길음1동': 15667,\n",
       " '57918_사당2동': 15666,\n",
       " '36818_시흥1동': 15665,\n",
       " '35575_구로3동': 15664,\n",
       " '81003_중계2,3동': 15663,\n",
       " '85647_구의1동': 15662,\n",
       " '81716_화양동': 15661,\n",
       " '77737_회기동': 15660,\n",
       " '79879_장안1동': 15659,\n",
       " '38214_망원1동': 15658,\n",
       " '23420_화곡6동': 15657,\n",
       " '42744_서교동': 15656,\n",
       " '41767_불광2동': 15655,\n",
       " '96550_방이1동': 15654,\n",
       " '41022_미성동': 15653,\n",
       " '36491_독산3동': 15652,\n",
       " '71730_행당1동': 15651,\n",
       " '34057_구로4동': 15650,\n",
       " '56930_소공동': 15649,\n",
       " '95346_방이1동': 15648,\n",
       " '68099_황학동': 15647,\n",
       " '49103_도화동': 15646,\n",
       " '81415_화양동': 15645,\n",
       " '79240_이문1동': 15644,\n",
       " '68320_수유3동': 15643,\n",
       " '49404_도화동': 15642,\n",
       " '69928_금호1가동': 15641,\n",
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       " '17745_수궁동': 15639,\n",
       " '55400_청운효자동': 15638,\n",
       " '92649_문정2동': 15637,\n",
       " '73229_마장동': 15636,\n",
       " '99542_길동': 15635,\n",
       " '20732_신월2동': 15634,\n",
       " '82191_중계4동': 15633,\n",
       " '98025_천호3동': 15632,\n",
       " '58133_명동': 15631,\n",
       " '86799_면목본동': 15630,\n",
       " '99225_암사1동': 15629,\n",
       " '32851_구로2동': 15628,\n",
       " '21317_화곡본동': 15627,\n",
       " '96862_가락본동': 15626,\n",
       " '55508_사당4동': 15625,\n",
       " '35529_당산1동': 15624,\n",
       " '53320_청파동': 15623,\n",
       " '43733_신원동': 15622,\n",
       " '82534_공릉1동': 15621,\n",
       " '95056_가락본동': 15620,\n",
       " '79543_이문1동': 15619,\n",
       " '12236_공항동': 15618,\n",
       " '103210_마천1동': 15617,\n",
       " '83741_공릉2동': 15616,\n",
       " '35595_독산1동': 15615,\n",
       " '35528_당산1동': 15614,\n",
       " '77483_성수2가3동': 15613,\n",
       " '87691_상봉1동': 15612,\n",
       " '72309_제기동': 15611,\n",
       " '77752_전농1동': 15610,\n",
       " '83223_화양동': 15609,\n",
       " '59348_명동': 15608,\n",
       " '101342_명일1동': 15607,\n",
       " '72519_방학2동': 15606,\n",
       " '56933_회현동': 15605,\n",
       " '97128_성내2동': 15604,\n",
       " '68631_수유1동': 15603,\n",
       " '87977_신내2동': 15602,\n",
       " '78940_이문1동': 15601,\n",
       " '53705_인헌동': 15600,\n",
       " '57616_사당2동': 15599,\n",
       " '71429_행당1동': 15598,\n",
       " '41464_갈현1동': 15597,\n",
       " '91713_방이2동': 15596,\n",
       " '65699_약수동': 15595,\n",
       " '96808_암사2동': 15594,\n",
       " '72283_월곡1동': 15593,\n",
       " '55098_청운효자동': 15592,\n",
       " '79249_휘경2동': 15591,\n",
       " '24319_등촌1동': 15590,\n",
       " '73152_창2동': 15589,\n",
       " '94411_풍납2동': 15588,\n",
       " '38514_망원1동': 15587,\n",
       " '31350_가산동': 15586,\n",
       " '50978_행운동': 15585,\n",
       " '74621_도봉1동': 15584,\n",
       " '94406_풍납1동': 15583,\n",
       " '27082_신정6동': 15582,\n",
       " '38555_영등포동': 15581,\n",
       " '89813_망우3동': 15580,\n",
       " '76507_장위2동': 15579,\n",
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       " '74427_청량리동': 15577,\n",
       " '96882_장지동': 15576,\n",
       " '26745_목3동': 15575,\n",
       " '42445_서교동': 15574,\n",
       " '55103_사직동': 15573,\n",
       " '53319_청파동': 15572,\n",
       " '52764_흑석동': 15571,\n",
       " '84410_중곡1동': 15570,\n",
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       " '76526_회기동': 15568,\n",
       " '44619_상도3동': 15567,\n",
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       " '101704_거여2동': 15564,\n",
       " '56055_한강로동': 15563,\n",
       " '85075_잠실본동': 15562,\n",
       " '81418_자양4동': 15561,\n",
       " '35172_상암동': 15560,\n",
       " '23759_신정3동': 15559,\n",
       " '57628_사당1동': 15558,\n",
       " '35235_문래동': 15557,\n",
       " '70766_송천동': 15556,\n",
       " '100186_오금동': 15555,\n",
       " '96878_문정1동': 15554,\n",
       " '75415_역삼1동': 15553,\n",
       " '77237_대치1동': 15552,\n",
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       " '33775_가산동': 15550,\n",
       " '27100_고척1동': 15549,\n",
       " '97127_성내2동': 15548,\n",
       " '35563_대림3동': 15547,\n",
       " '95058_가락본동': 15546,\n",
       " '75227_도봉2동': 15545,\n",
       " '10412_방화2동': 15544,\n",
       " '84772_잠실본동': 15543,\n",
       " '38557_영등포동': 15542,\n",
       " '46063_신촌동': 15541,\n",
       " '96517_천호2동': 15540,\n",
       " '33453_구로4동': 15539,\n",
       " '47959_은천동': 15538,\n",
       " '19517_신월1동': 15537,\n",
       " '67426_수유1동': 15536,\n",
       " '37434_시흥3동': 15535,\n",
       " '49996_아현동': 15534,\n",
       " '34043_구로5동': 15533,\n",
       " '66370_서초3동': 15532,\n",
       " '52693_천연동': 15531,\n",
       " '80148_이문1동': 15530,\n",
       " '79273_답십리2동': 15529,\n",
       " '25253_화곡4동': 15528,\n",
       " '71112_용신동': 15527,\n",
       " '83434_공릉2동': 15526,\n",
       " '78574_상계6,7동': 15525,\n",
       " '80784_장안1동': 15524,\n",
       " '66805_우이동': 15523,\n",
       " '70469_길음2동': 15522,\n",
       " '79301_성수2가3동': 15521,\n",
       " '59649_명동': 15520,\n",
       " '71917_방학2동': 15519,\n",
       " '69027_금호4가동': 15518,\n",
       " '80373_상계9동': 15517,\n",
       " '95358_가락본동': 15516,\n",
       " '24091_개봉2동': 15515,\n",
       " '18910_신월1동': 15514,\n",
       " '80515_자양4동': 15513,\n",
       " '49705_도화동': 15512,\n",
       " '46464_대학동': 15511,\n",
       " '26167_신정4동': 15510,\n",
       " '66371_서초3동': 15509,\n",
       " '84470_잠실본동': 15508,\n",
       " '47536_홍은1동': 15507,\n",
       " '22235_화곡8동': 15506,\n",
       " '81284_상계5동': 15505,\n",
       " '94755_가락본동': 15504,\n",
       " '50606_공덕동': 15503,\n",
       " '74429_용신동': 15502,\n",
       " '59119_방배본동': 15501,\n",
       " '68040_삼양동': 15500,\n",
       " '41236_서교동': 15499,\n",
       " '73221_용신동': 15498,\n",
       " '73531_마장동': 15497,\n",
       " '29194_신정2동': 15496,\n",
       " '48492_염리동': 15495,\n",
       " '37953_영등포동': 15494,\n",
       " '42138_서교동': 15493,\n",
       " '37910_망원2동': 15492,\n",
       " '77131_회기동': 15491,\n",
       " '23721_화곡6동': 15490,\n",
       " '66667_서초4동': 15489,\n",
       " '77645_상계1동': 15488,\n",
       " '41796_응암2동': 15487,\n",
       " '77642_상계1동': 15486,\n",
       " '72923_용신동': 15485,\n",
       " '45775_신수동': 15484,\n",
       " '36800_독산4동': 15483,\n",
       " '78909_월계1동': 15482,\n",
       " '36482_구로3동': 15481,\n",
       " '77436_회기동': 15480,\n",
       " '36084_성산2동': 15479,\n",
       " '73947_양재2동': 15478,\n",
       " '42418_남가좌2동': 15477,\n",
       " '93529_방이2동': 15476,\n",
       " '21315_화곡본동': 15475,\n",
       " '69673_논현1동': 15474,\n",
       " '33152_구로4동': 15473,\n",
       " '39201_조원동': 15472,\n",
       " '21034_신월2동': 15471,\n",
       " '83566_잠실7동': 15470,\n",
       " '45461_신촌동': 15469,\n",
       " '38254_영등포동': 15468,\n",
       " '96521_성내2동': 15467,\n",
       " '57025_사당1동': 15466,\n",
       " '71622_쌍문4동': 15465,\n",
       " '75251_창5동': 15464,\n",
       " '47896_용강동': 15463,\n",
       " '90200_잠실6동': 15462,\n",
       " '29817_구로2동': 15461,\n",
       " '42139_서교동': 15460,\n",
       " '50054_상도1동': 15459,\n",
       " '34003_양평2동': 15458,\n",
       " '69600_용신동': 15457,\n",
       " '78669_답십리2동': 15456,\n",
       " '102906_마천2동': 15455,\n",
       " '76202_장위2동': 15454,\n",
       " '31932_구로5동': 15453,\n",
       " '20117_화곡1동': 15452,\n",
       " '41845_서교동': 15451,\n",
       " '84472_잠실본동': 15450,\n",
       " '101034_명일1동': 15449,\n",
       " '65396_다산동': 15448,\n",
       " '76027_역삼2동': 15447,\n",
       " '71111_용신동': 15446,\n",
       " '92015_방이2동': 15445,\n",
       " '32830_신도림동': 15444,\n",
       " '85047_자양1동': 15443,\n",
       " '33657_상암동': 15442,\n",
       " '21038_신월4동': 15441,\n",
       " '57916_사당2동': 15440,\n",
       " '16796_신월3동': 15439,\n",
       " '67712_수유2동': 15438,\n",
       " '68415_금호2,3가동': 15437,\n",
       " '99247_성내3동': 15436,\n",
       " '70257_신사동(강남동)': 15435,\n",
       " '91729_석촌동': 15434,\n",
       " '39412_성산1동': 15433,\n",
       " '73518_제기동': 15432,\n",
       " '95057_가락본동': 15431,\n",
       " '75846_방학1동': 15430,\n",
       " '36210_시흥1동': 15429,\n",
       " '41769_대조동': 15428,\n",
       " '72929_마장동': 15427,\n",
       " '40930_서교동': 15426,\n",
       " '69601_용신동': 15425,\n",
       " '82832_공릉1동': 15424,\n",
       " '31654_가산동': 15423,\n",
       " '104354_명일2동': 15422,\n",
       " '44034_서원동': 15421,\n",
       " '72930_마장동': 15420,\n",
       " '39962_갈현2동': 15419,\n",
       " '84444_자양1동': 15418,\n",
       " '26469_신정4동': 15417,\n",
       " '84420_능동': 15416,\n",
       " '27048_목3동': 15415,\n",
       " '53645_한강로동': 15414,\n",
       " '73544_사근동': 15413,\n",
       " '48140_홍제3동': 15412,\n",
       " '47585_대흥동': 15411,\n",
       " '44594_여의동': 15410,\n",
       " '71732_행당1동': 15409,\n",
       " '92017_방이2동': 15408,\n",
       " '74928_도봉2동': 15407,\n",
       " '47841_홍제1동': 15406,\n",
       " '85095_일원1동': 15405,\n",
       " '50379_낙성대동': 15404,\n",
       " '82980_일원2동': 15403,\n",
       " '40707_신사동': 15402,\n",
       " '91714_방이2동': 15401,\n",
       " '65996_다산동': 15400,\n",
       " '71974_송중동': 15399,\n",
       " '59429_방배4동': 15398,\n",
       " '40264_갈현2동': 15397,\n",
       " '98949_성내3동': 15396,\n",
       " '45462_서교동': 15395,\n",
       " '93525_방이2동': 15394,\n",
       " '42443_서교동': 15393,\n",
       " '37292_성산2동': 15392,\n",
       " '27047_목3동': 15391,\n",
       " '88904_망우본동': 15390,\n",
       " '75030_청량리동': 15389,\n",
       " '42986_대조동': 15388,\n",
       " '62457_방배3동': 15387,\n",
       " '62368_필동': 15386,\n",
       " '75114_역삼1동': 15385,\n",
       " '92319_방이2동': 15384,\n",
       " '29487_목1동': 15383,\n",
       " '93831_방이2동': 15382,\n",
       " '68471_반포1동': 15381,\n",
       " '33468_가산동': 15380,\n",
       " '92030_석촌동': 15379,\n",
       " '73519_제기동': 15378,\n",
       " '26493_고척1동': 15377,\n",
       " '51823_용문동': 15376,\n",
       " '46162_대학동': 15375,\n",
       " '83392_상계3,4동': 15374,\n",
       " '42782_여의동': 15373,\n",
       " '40291_응암3동': 15372,\n",
       " '46727_상도2동': 15371,\n",
       " '78366_답십리2동': 15370,\n",
       " '98387_장지동': 15369,\n",
       " '25825_가양3동': 15368,\n",
       " '55742_남영동': 15367,\n",
       " '79043_대치2동': 15366,\n",
       " '59044_명동': 15365,\n",
       " '20737_신월4동': 15364,\n",
       " '67275_서초1동': 15363,\n",
       " '49702_아현동': 15362,\n",
       " '84771_잠실본동': 15361,\n",
       " '95615_풍납1동': 15360,\n",
       " '22233_화곡8동': 15359,\n",
       " '67221_옥수동': 15358,\n",
       " '99581_오금동': 15357,\n",
       " '23419_화곡6동': 15356,\n",
       " '38256_영등포동': 15355,\n",
       " '77945_상계1동': 15354,\n",
       " '80480_장안1동': 15353,\n",
       " '67467_동선동': 15352,\n",
       " '79226_석관동': 15351,\n",
       " '53649_한강로동': 15350,\n",
       " '20431_신월4동': 15349,\n",
       " '77133_회기동': 15348,\n",
       " '48143_홍제3동': 15347,\n",
       " '35849_도림동': 15346,\n",
       " '74933_방학1동': 15345,\n",
       " '45253_삼성동': 15344,\n",
       " '37415_시흥1동': 15343,\n",
       " '94753_가락본동': 15342,\n",
       " '86228_중곡2동': 15341,\n",
       " '43045_서교동': 15340,\n",
       " '69361_신사동(강남동)': 15339,\n",
       " '86834_중곡4동': 15338,\n",
       " '92634_송파2동': 15337,\n",
       " '41461_갈현1동': 15336,\n",
       " '96282_문정2동': 15335,\n",
       " '67468_동선동': 15334,\n",
       " '33769_가산동': 15333,\n",
       " '79910_자양4동': 15332,\n",
       " '57925_사당1동': 15331,\n",
       " '95317_성내1동': 15330,\n",
       " '44920_상도3동': 15329,\n",
       " '24701_개봉3동': 15328,\n",
       " '21367_오류1동': 15327,\n",
       " '40034_합정동': 15326,\n",
       " '61549_방배1동': 15325,\n",
       " '94756_가락본동': 15324,\n",
       " '70502_용신동': 15323,\n",
       " '36796_독산3동': 15322,\n",
       " '77785_성수2가3동': 15321,\n",
       " '20739_신월4동': 15320,\n",
       " '83815_군자동': 15319,\n",
       " '103513_마천1동': 15318,\n",
       " '66675_서초3동': 15317,\n",
       " '95669_문정1동': 15316,\n",
       " '42148_서강동': 15315,\n",
       " '57933_남현동': 15314,\n",
       " '96502_암사2동': 15313,\n",
       " '37348_영등포동': 15312,\n",
       " '102300_마천2동': 15311,\n",
       " '85344_구의1동': 15310,\n",
       " '61190_이태원1동': 15309,\n",
       " '81587_상계5동': 15308,\n",
       " '95668_문정1동': 15307,\n",
       " '41834_연남동': 15306,\n",
       " '79828_석관동': 15305,\n",
       " '62948_혜화동': 15304,\n",
       " '98946_성내3동': 15303,\n",
       " '48861_중앙동': 15302,\n",
       " '38255_영등포동': 15301,\n",
       " '36821_시흥1동': 15300,\n",
       " '80487_장안1동': 15299,\n",
       " '33141_구로5동': 15298,\n",
       " '45760_신촌동': 15297,\n",
       " '79207_월계3동': 15296,\n",
       " '76861_용답동': 15295,\n",
       " '46766_대학동': 15294,\n",
       " '45238_신림동': 15293,\n",
       " '85949_구의1동': 15292,\n",
       " '103508_마천2동': 15291,\n",
       " '70805_용신동': 15290,\n",
       " '83474_면목2동': 15289,\n",
       " '41789_응암1동': 15288,\n",
       " '95055_가락본동': 15287,\n",
       " '42141_서교동': 15286,\n",
       " '92026_송파1동': 15285,\n",
       " '57917_사당2동': 15284,\n",
       " '44807_녹번동': 15283,\n",
       " '40033_합정동': 15282,\n",
       " '78365_답십리2동': 15281,\n",
       " '75339_전농1동': 15280,\n",
       " '92571_광장동': 15279,\n",
       " '59940_종로1,2,3,4가동': 15278,\n",
       " '29788_목1동': 15277,\n",
       " '84716_중곡2동': 15276,\n",
       " '25297_개봉2동': 15275,\n",
       " '95622_성내1동': 15274,\n",
       " '20380_가양1동': 15273,\n",
       " '42743_서교동': 15272,\n",
       " '79822_월계1동': 15271,\n",
       " '51205_공덕동': 15270,\n",
       " '81414_화양동': 15269,\n",
       " '79878_장안1동': 15268,\n",
       " '69237_미아동': 15267,\n",
       " '69677_논현1동': 15266,\n",
       " '72612_제기동': 15265,\n",
       " '79778_상계2동': 15264,\n",
       " '36438_당산1동': 15263,\n",
       " '95644_방이1동': 15262,\n",
       " '19488_발산1동': 15261,\n",
       " '79180_상계6,7동': 15260,\n",
       " '68416_금호2,3가동': 15259,\n",
       " '78731_삼성1동': 15258,\n",
       " '81711_화양동': 15257,\n",
       " '83472_상봉2동': 15256,\n",
       " '38251_영등포동': 15255,\n",
       " '72225_쌍문2동': 15254,\n",
       " '34991_독산1동': 15253,\n",
       " '74511_역삼1동': 15252,\n",
       " '108888_상일동': 15251,\n",
       " '26166_신정4동': 15250,\n",
       " '60241_종로1,2,3,4가동': 15249,\n",
       " '88664_광장동': 15248,\n",
       " '98678_가락2동': 15247,\n",
       " '69228_수유3동': 15246,\n",
       " '38770_신사1동': 15245,\n",
       " '72719_도곡1동': 15244,\n",
       " '34986_독산1동': 15243,\n",
       " '20090_등촌3동': 15242,\n",
       " '62066_필동': 15241,\n",
       " '97409_암사1동': 15240,\n",
       " '79911_자양4동': 15239,\n",
       " '34644_대림3동': 15238,\n",
       " '53949_한강로동': 15237,\n",
       " '69975_논현1동': 15236,\n",
       " ...}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dong_num = range(len(dong_lst),0,-1)  # id와 행정동을 매치시킨 것들이 많은 순서대로 정렬한 게 dong_lst이므로, 거꾸로 숫자를 세서 각 id_행정동_match에 부여함(내림차순으로)\n",
    "dic = {}\n",
    "\n",
    "for number, name in zip(dong_lst, dong_num) :\n",
    "    dic[number] = name\n",
    "\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dataset['id_행정동_match'].replace(dic, inplace = True)   # id_행정동 매치된 것들을 숫자로 Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# id_행정동_match를 내림차순으로 정렬\n",
    "dataset = dataset.sort_values(by=[\"id_행정동_match\"], ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>조사년도</th>\n",
       "      <th>조사월</th>\n",
       "      <th>단속활동유무</th>\n",
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       "      <th>관공서와의 거리</th>\n",
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       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
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       "      <th>Q4</th>\n",
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       "      <th>0</th>\n",
       "      <td>92038.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1896</td>\n",
       "      <td>877.444148</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>32.442048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92038.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2253</td>\n",
       "      <td>746.531110</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>32.442048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92038.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2066</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>32.442048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>16235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92038.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2097</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>32.442048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92038.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2106</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>32.442048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92609</th>\n",
       "      <td>65445.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>1795.831736</td>\n",
       "      <td>546.658432</td>\n",
       "      <td>841.430291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92610</th>\n",
       "      <td>71624.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2264</td>\n",
       "      <td>2522.655623</td>\n",
       "      <td>451.555016</td>\n",
       "      <td>193.339897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92611</th>\n",
       "      <td>48858.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>953.246490</td>\n",
       "      <td>269.888989</td>\n",
       "      <td>242.044632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92612</th>\n",
       "      <td>84649.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2147</td>\n",
       "      <td>2616.865257</td>\n",
       "      <td>646.934523</td>\n",
       "      <td>645.021499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92613</th>\n",
       "      <td>36121.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1739</td>\n",
       "      <td>1456.575045</td>\n",
       "      <td>374.312219</td>\n",
       "      <td>446.361827</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92614 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  조사년도  조사월  단속활동유무  적발횟수_0  적발횟수_1  적발횟수_2  적발횟수_3  적발횟수_4  \\\n",
       "0      92038.0  2012    6    True       1       0       0       0       0   \n",
       "1      92038.0  2016    4    True      14       1       0       0       0   \n",
       "2      92038.0  2017    4    True      40       7       2       0       0   \n",
       "3      92038.0  2017    3    True       5       0       0       0       0   \n",
       "4      92038.0  2017    2    True      15       1       0       0       0   \n",
       "...        ...   ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "92609  65445.0  2011    7    True       1       0       0       0       0   \n",
       "92610  71624.0  2014   12    True       1       0       0       0       0   \n",
       "92611  48858.0  2019    6    True       1       0       0       0       0   \n",
       "92612  84649.0  2019    4    True       1       0       0       0       0   \n",
       "92613  36121.0  2018    7    True       1       0       0       0       0   \n",
       "\n",
       "       적발횟수_5  ...  호주산불고기_냉장_100g    모범업소와의 거리    관공서와의 거리   적발지점과의 거리  Q1  \\\n",
       "0           0  ...            1896   877.444148  668.420106   32.442048   0   \n",
       "1           0  ...            2253   746.531110  668.420106   32.442048   0   \n",
       "2           0  ...            2066   253.807243  668.420106   32.442048   0   \n",
       "3           0  ...            2097   253.807243  668.420106   32.442048   0   \n",
       "4           0  ...            2106   253.807243  668.420106   32.442048   0   \n",
       "...       ...  ...             ...          ...         ...         ...  ..   \n",
       "92609       0  ...            1852  1795.831736  546.658432  841.430291   0   \n",
       "92610       0  ...            2264  2522.655623  451.555016  193.339897   0   \n",
       "92611       0  ...            2010   953.246490  269.888989  242.044632   0   \n",
       "92612       0  ...            2147  2616.865257  646.934523  645.021499   0   \n",
       "92613       0  ...            1739  1456.575045  374.312219  446.361827   0   \n",
       "\n",
       "       Q2  Q3  Q4  적발건수  id_행정동_match  \n",
       "0       0   0   0     0         16235  \n",
       "1       0   0   0     1         16235  \n",
       "2       0   0   0     9         16235  \n",
       "3       0   0   0     0         16235  \n",
       "4       0   0   0     1         16235  \n",
       "...    ..  ..  ..   ...           ...  \n",
       "92609   0   0   0     0             5  \n",
       "92610   0   0   0     0             4  \n",
       "92611   0   0   0     0             3  \n",
       "92612   0   0   0     0             2  \n",
       "92613   0   0   0     0             1  \n",
       "\n",
       "[92614 rows x 83 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 필요한 변수들만 선택"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "예를 들어, 위에서 사용한 적발횟수_0 ~ 적발횟수_6, 단속활동유무, 돼지고기, 소고기 등등 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92614 entries, 0 to 92613\n",
      "Data columns (total 38 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   조사년도              92614 non-null  int64  \n",
      " 1   조사월               92614 non-null  int64  \n",
      " 2   id_행정동_match      92614 non-null  int64  \n",
      " 3   65세이상인구           92612 non-null  float64\n",
      " 4   총수급자              92612 non-null  float64\n",
      " 5   독거노인수             92612 non-null  float64\n",
      " 6   독거노인수_기초생활보장수급권자  92612 non-null  float64\n",
      " 7   독거노인수_저소득         92612 non-null  float64\n",
      " 8   한국총인구             92612 non-null  float64\n",
      " 9   외국인인구             92612 non-null  float64\n",
      " 10  음식점업              92614 non-null  int64  \n",
      " 11  도소매업              92614 non-null  int64  \n",
      " 12  식품제조업(가공)         92614 non-null  int64  \n",
      " 13  업태누락              92614 non-null  int64  \n",
      " 14  쇠고기냉장_수량_톤        92614 non-null  int64  \n",
      " 15  쇠고기냉동_수량_톤        92614 non-null  int64  \n",
      " 16  돼지고기냉장_수량_톤       92614 non-null  int64  \n",
      " 17  돼지고기냉동_수량_톤       92614 non-null  int64  \n",
      " 18  닭고기_수량_톤          92614 non-null  int64  \n",
      " 19  쇠고기가격_도매          92614 non-null  int64  \n",
      " 20  쇠고기가격_소매          92614 non-null  int64  \n",
      " 21  돼지고기가격_도매         92614 non-null  int64  \n",
      " 22  돼지고기가격_소매         92614 non-null  int64  \n",
      " 23  닭고기가격_도매          92614 non-null  int64  \n",
      " 24  닭고기가격_소매          92614 non-null  int64  \n",
      " 25  한우갈비_100g         92614 non-null  int64  \n",
      " 26  한우불고기_100g        92614 non-null  int64  \n",
      " 27  호주산갈비_냉동_100g     92614 non-null  int64  \n",
      " 28  호주산갈비_냉장_100g     92614 non-null  int64  \n",
      " 29  호주산불고기_냉동_100g    92614 non-null  int64  \n",
      " 30  호주산불고기_냉장_100g    92614 non-null  int64  \n",
      " 31  모범업소와의 거리         92614 non-null  float64\n",
      " 32  관공서와의 거리          92614 non-null  float64\n",
      " 33  Q1                92614 non-null  int64  \n",
      " 34  Q2                92614 non-null  int64  \n",
      " 35  Q3                92614 non-null  int64  \n",
      " 36  Q4                92614 non-null  int64  \n",
      " 37  적발건수              92614 non-null  int64  \n",
      "dtypes: float64(9), int64(29)\n",
      "memory usage: 26.9 MB\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset[['조사년도', '조사월', 'id_행정동_match', \n",
    "       '65세이상인구', '총수급자', '독거노인수', '독거노인수_기초생활보장수급권자', '독거노인수_저소득', '한국총인구', '외국인인구',\n",
    "       '음식점업', '도소매업', '식품제조업(가공)', '업태누락', \n",
    "       '쇠고기냉장_수량_톤', '쇠고기냉동_수량_톤', '돼지고기냉장_수량_톤', '돼지고기냉동_수량_톤', '닭고기_수량_톤', \n",
    "       '쇠고기가격_도매', '쇠고기가격_소매', '돼지고기가격_도매', '돼지고기가격_소매', '닭고기가격_도매', '닭고기가격_소매', \n",
    "       '한우갈비_100g', '한우불고기_100g', '호주산갈비_냉동_100g', '호주산갈비_냉장_100g', '호주산불고기_냉동_100g', '호주산불고기_냉장_100g', \n",
    "       '모범업소와의 거리', '관공서와의 거리', 'Q1', 'Q2', 'Q3', 'Q4', \n",
    "       '적발건수']]\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NaN값 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>조사년도</th>\n",
       "      <th>조사월</th>\n",
       "      <th>id_행정동_match</th>\n",
       "      <th>65세이상인구</th>\n",
       "      <th>총수급자</th>\n",
       "      <th>독거노인수</th>\n",
       "      <th>독거노인수_기초생활보장수급권자</th>\n",
       "      <th>독거노인수_저소득</th>\n",
       "      <th>한국총인구</th>\n",
       "      <th>외국인인구</th>\n",
       "      <th>...</th>\n",
       "      <th>호주산갈비_냉장_100g</th>\n",
       "      <th>호주산불고기_냉동_100g</th>\n",
       "      <th>호주산불고기_냉장_100g</th>\n",
       "      <th>모범업소와의 거리</th>\n",
       "      <th>관공서와의 거리</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>적발건수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>16235</td>\n",
       "      <td>701.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6475.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1628</td>\n",
       "      <td>1100</td>\n",
       "      <td>1896</td>\n",
       "      <td>877.444148</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>16235</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2102</td>\n",
       "      <td>1485</td>\n",
       "      <td>2253</td>\n",
       "      <td>746.531110</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2490</td>\n",
       "      <td>1478</td>\n",
       "      <td>2066</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2090</td>\n",
       "      <td>1481</td>\n",
       "      <td>2097</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2707</td>\n",
       "      <td>1462</td>\n",
       "      <td>2106</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92609</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24517.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1660</td>\n",
       "      <td>1100</td>\n",
       "      <td>1852</td>\n",
       "      <td>1795.831736</td>\n",
       "      <td>546.658432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92610</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>21224.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>2522.655623</td>\n",
       "      <td>451.555016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92611</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30968.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2547</td>\n",
       "      <td>1543</td>\n",
       "      <td>2010</td>\n",
       "      <td>953.246490</td>\n",
       "      <td>269.888989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92612</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4904.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40443.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2520</td>\n",
       "      <td>1528</td>\n",
       "      <td>2147</td>\n",
       "      <td>2616.865257</td>\n",
       "      <td>646.934523</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92613</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4144.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35740.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2185</td>\n",
       "      <td>1560</td>\n",
       "      <td>1739</td>\n",
       "      <td>1456.575045</td>\n",
       "      <td>374.312219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92612 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       조사년도  조사월  id_행정동_match  65세이상인구   총수급자   독거노인수  독거노인수_기초생활보장수급권자  \\\n",
       "0      2012    6         16235    701.0   33.0   199.0               4.0   \n",
       "1      2016    4         16235    110.0    2.0    14.0               2.0   \n",
       "2      2017    4         16235    114.0    1.0    11.0               0.0   \n",
       "3      2017    3         16235    114.0    1.0    11.0               0.0   \n",
       "4      2017    2         16235    114.0    1.0    11.0               0.0   \n",
       "...     ...  ...           ...      ...    ...     ...               ...   \n",
       "92609  2011    7             5   2095.0   10.0    80.0               0.0   \n",
       "92610  2014   12             4   2184.0   64.0   574.0              32.0   \n",
       "92611  2019    6             3   5160.0  946.0  1201.0             401.0   \n",
       "92612  2019    4             2   4904.0  592.0   981.0             182.0   \n",
       "92613  2018    7             1   4144.0  125.0   746.0              40.0   \n",
       "\n",
       "       독거노인수_저소득    한국총인구  외국인인구  ...  호주산갈비_냉장_100g  호주산불고기_냉동_100g  \\\n",
       "0            0.0   6475.0  116.0  ...           1628            1100   \n",
       "1            1.0    897.0   21.0  ...           2102            1485   \n",
       "2            1.0    889.0   16.0  ...           2490            1478   \n",
       "3            1.0    889.0   16.0  ...           2090            1481   \n",
       "4            1.0    889.0   16.0  ...           2707            1462   \n",
       "...          ...      ...    ...  ...            ...             ...   \n",
       "92609        0.0  24517.0   26.0  ...           1660            1100   \n",
       "92610      306.0  21224.0   45.0  ...           2587            1400   \n",
       "92611       52.0  30968.0  207.0  ...           2547            1543   \n",
       "92612       64.0  40443.0  628.0  ...           2520            1528   \n",
       "92613       13.0  35740.0  451.0  ...           2185            1560   \n",
       "\n",
       "       호주산불고기_냉장_100g    모범업소와의 거리    관공서와의 거리  Q1  Q2  Q3  Q4  적발건수  \n",
       "0                1896   877.444148  668.420106   0   0   0   0     0  \n",
       "1                2253   746.531110  668.420106   0   0   0   0     1  \n",
       "2                2066   253.807243  668.420106   0   0   0   0     9  \n",
       "3                2097   253.807243  668.420106   0   0   0   0     0  \n",
       "4                2106   253.807243  668.420106   0   0   0   0     1  \n",
       "...               ...          ...         ...  ..  ..  ..  ..   ...  \n",
       "92609            1852  1795.831736  546.658432   0   0   0   0     0  \n",
       "92610            2264  2522.655623  451.555016   0   0   0   0     0  \n",
       "92611            2010   953.246490  269.888989   0   0   0   0     0  \n",
       "92612            2147  2616.865257  646.934523   0   0   0   0     0  \n",
       "92613            1739  1456.575045  374.312219   0   0   0   0     0  \n",
       "\n",
       "[92612 rows x 38 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.dropna(axis = 0)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>조사년도</th>\n",
       "      <th>조사월</th>\n",
       "      <th>id_행정동_match</th>\n",
       "      <th>65세이상인구</th>\n",
       "      <th>총수급자</th>\n",
       "      <th>독거노인수</th>\n",
       "      <th>독거노인수_기초생활보장수급권자</th>\n",
       "      <th>독거노인수_저소득</th>\n",
       "      <th>한국총인구</th>\n",
       "      <th>외국인인구</th>\n",
       "      <th>...</th>\n",
       "      <th>호주산갈비_냉장_100g</th>\n",
       "      <th>호주산불고기_냉동_100g</th>\n",
       "      <th>호주산불고기_냉장_100g</th>\n",
       "      <th>모범업소와의 거리</th>\n",
       "      <th>관공서와의 거리</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>적발건수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>16235</td>\n",
       "      <td>701.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6475.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1628</td>\n",
       "      <td>1100</td>\n",
       "      <td>1896</td>\n",
       "      <td>877.444148</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>16235</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2102</td>\n",
       "      <td>1485</td>\n",
       "      <td>2253</td>\n",
       "      <td>746.531110</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2490</td>\n",
       "      <td>1478</td>\n",
       "      <td>2066</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2090</td>\n",
       "      <td>1481</td>\n",
       "      <td>2097</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2707</td>\n",
       "      <td>1462</td>\n",
       "      <td>2106</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92609</th>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24517.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1660</td>\n",
       "      <td>1100</td>\n",
       "      <td>1852</td>\n",
       "      <td>1795.831736</td>\n",
       "      <td>546.658432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92610</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>21224.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>2522.655623</td>\n",
       "      <td>451.555016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92611</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30968.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2547</td>\n",
       "      <td>1543</td>\n",
       "      <td>2010</td>\n",
       "      <td>953.246490</td>\n",
       "      <td>269.888989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92612</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4904.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40443.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2520</td>\n",
       "      <td>1528</td>\n",
       "      <td>2147</td>\n",
       "      <td>2616.865257</td>\n",
       "      <td>646.934523</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92613</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4144.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35740.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2185</td>\n",
       "      <td>1560</td>\n",
       "      <td>1739</td>\n",
       "      <td>1456.575045</td>\n",
       "      <td>374.312219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92612 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       조사년도  조사월  id_행정동_match  65세이상인구   총수급자   독거노인수  독거노인수_기초생활보장수급권자  \\\n",
       "0      2012    6         16235    701.0   33.0   199.0               4.0   \n",
       "1      2016    4         16235    110.0    2.0    14.0               2.0   \n",
       "2      2017    4         16235    114.0    1.0    11.0               0.0   \n",
       "3      2017    3         16235    114.0    1.0    11.0               0.0   \n",
       "4      2017    2         16235    114.0    1.0    11.0               0.0   \n",
       "...     ...  ...           ...      ...    ...     ...               ...   \n",
       "92609  2011    7             5   2095.0   10.0    80.0               0.0   \n",
       "92610  2014   12             4   2184.0   64.0   574.0              32.0   \n",
       "92611  2019    6             3   5160.0  946.0  1201.0             401.0   \n",
       "92612  2019    4             2   4904.0  592.0   981.0             182.0   \n",
       "92613  2018    7             1   4144.0  125.0   746.0              40.0   \n",
       "\n",
       "       독거노인수_저소득    한국총인구  외국인인구  ...  호주산갈비_냉장_100g  호주산불고기_냉동_100g  \\\n",
       "0            0.0   6475.0  116.0  ...           1628            1100   \n",
       "1            1.0    897.0   21.0  ...           2102            1485   \n",
       "2            1.0    889.0   16.0  ...           2490            1478   \n",
       "3            1.0    889.0   16.0  ...           2090            1481   \n",
       "4            1.0    889.0   16.0  ...           2707            1462   \n",
       "...          ...      ...    ...  ...            ...             ...   \n",
       "92609        0.0  24517.0   26.0  ...           1660            1100   \n",
       "92610      306.0  21224.0   45.0  ...           2587            1400   \n",
       "92611       52.0  30968.0  207.0  ...           2547            1543   \n",
       "92612       64.0  40443.0  628.0  ...           2520            1528   \n",
       "92613       13.0  35740.0  451.0  ...           2185            1560   \n",
       "\n",
       "       호주산불고기_냉장_100g    모범업소와의 거리    관공서와의 거리  Q1  Q2  Q3  Q4  적발건수  \n",
       "0                1896   877.444148  668.420106   0   0   0   0     0  \n",
       "1                2253   746.531110  668.420106   0   0   0   0     1  \n",
       "2                2066   253.807243  668.420106   0   0   0   0     9  \n",
       "3                2097   253.807243  668.420106   0   0   0   0     0  \n",
       "4                2106   253.807243  668.420106   0   0   0   0     1  \n",
       "...               ...          ...         ...  ..  ..  ..  ..   ...  \n",
       "92609            1852  1795.831736  546.658432   0   0   0   0     0  \n",
       "92610            2264  2522.655623  451.555016   0   0   0   0     0  \n",
       "92611            2010   953.246490  269.888989   0   0   0   0     0  \n",
       "92612            2147  2616.865257  646.934523   0   0   0   0     0  \n",
       "92613            1739  1456.575045  374.312219   0   0   0   0     0  \n",
       "\n",
       "[92612 rows x 38 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_copy = dataset.copy()\n",
    "dataset_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 모델에 넣을 데이터 정하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Split\n",
    "\n",
    "## 월별(1월~12월)로 나누어 train, test data로 split하고 밑의 과정 진행"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12월"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2010~2018년도를 train data로, 2019년을 test data로 사용하여 월별로 뽑아옴\n",
    "dataset_train = dataset[(dataset['조사년도'] <= 2018) & (dataset['조사월'] == 12)]\n",
    "dataset_test = dataset[(dataset['조사년도'] == 2019) & (dataset['조사월'] == 12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>조사년도</th>\n",
       "      <th>조사월</th>\n",
       "      <th>id_행정동_match</th>\n",
       "      <th>65세이상인구</th>\n",
       "      <th>총수급자</th>\n",
       "      <th>독거노인수</th>\n",
       "      <th>독거노인수_기초생활보장수급권자</th>\n",
       "      <th>독거노인수_저소득</th>\n",
       "      <th>한국총인구</th>\n",
       "      <th>외국인인구</th>\n",
       "      <th>...</th>\n",
       "      <th>호주산갈비_냉장_100g</th>\n",
       "      <th>호주산불고기_냉동_100g</th>\n",
       "      <th>호주산불고기_냉장_100g</th>\n",
       "      <th>모범업소와의 거리</th>\n",
       "      <th>관공서와의 거리</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>적발건수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>16235</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2385</td>\n",
       "      <td>1427</td>\n",
       "      <td>2289</td>\n",
       "      <td>746.531110</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>16235</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2309</td>\n",
       "      <td>1400</td>\n",
       "      <td>2132</td>\n",
       "      <td>712.563965</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>16235</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>253.807243</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>16235</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2500</td>\n",
       "      <td>1577</td>\n",
       "      <td>1963</td>\n",
       "      <td>739.616594</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>16235</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15639.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1778</td>\n",
       "      <td>1100</td>\n",
       "      <td>1522</td>\n",
       "      <td>805.066287</td>\n",
       "      <td>668.420106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92556</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>5034.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>31197.0</td>\n",
       "      <td>1432.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>1076.099509</td>\n",
       "      <td>443.207211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92561</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>53</td>\n",
       "      <td>2439.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>18639.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>642.763286</td>\n",
       "      <td>374.779727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92578</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>4841.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>32831.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>3001.387225</td>\n",
       "      <td>95.464875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92602</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8541.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2309</td>\n",
       "      <td>1400</td>\n",
       "      <td>2132</td>\n",
       "      <td>1438.488228</td>\n",
       "      <td>649.446212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92610</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>21224.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>2522.655623</td>\n",
       "      <td>451.555016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8103 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       조사년도  조사월  id_행정동_match  65세이상인구   총수급자   독거노인수  독거노인수_기초생활보장수급권자  \\\n",
       "6      2016   12         16235    110.0    2.0    14.0               2.0   \n",
       "17     2015   12         16235    109.0    1.0    20.0               0.0   \n",
       "32     2017   12         16235    114.0    1.0    11.0               0.0   \n",
       "42     2018   12         16235    130.0    2.0     6.0               0.0   \n",
       "74     2010   12         16235   1259.0   46.0   334.0               5.0   \n",
       "...     ...  ...           ...      ...    ...     ...               ...   \n",
       "92556  2014   12            58   5034.0  767.0  1487.0             333.0   \n",
       "92561  2014   12            53   2439.0  403.0   612.0             223.0   \n",
       "92578  2017   12            36   4841.0  593.0  1031.0             185.0   \n",
       "92602  2015   12            12   1217.0   96.0   211.0              37.0   \n",
       "92610  2014   12             4   2184.0   64.0   574.0              32.0   \n",
       "\n",
       "       독거노인수_저소득    한국총인구   외국인인구  ...  호주산갈비_냉장_100g  호주산불고기_냉동_100g  \\\n",
       "6            1.0    897.0    21.0  ...           2385            1427   \n",
       "17           1.0    950.0    18.0  ...           2309            1400   \n",
       "32           1.0    889.0    16.0  ...           2163            1537   \n",
       "42           0.0   1104.0    11.0  ...           2500            1577   \n",
       "74           0.0  15639.0   146.0  ...           1778            1100   \n",
       "...          ...      ...     ...  ...            ...             ...   \n",
       "92556       39.0  31197.0  1432.0  ...           2587            1400   \n",
       "92561       40.0  18639.0   150.0  ...           2587            1400   \n",
       "92578       37.0  32831.0   690.0  ...           2163            1537   \n",
       "92602        2.0   8541.0    78.0  ...           2309            1400   \n",
       "92610      306.0  21224.0    45.0  ...           2587            1400   \n",
       "\n",
       "       호주산불고기_냉장_100g    모범업소와의 거리    관공서와의 거리  Q1  Q2  Q3  Q4  적발건수  \n",
       "6                2289   746.531110  668.420106   0   0   0   0     0  \n",
       "17               2132   712.563965  668.420106   0   0   0   0     0  \n",
       "32               1882   253.807243  668.420106   0   0   0   0     0  \n",
       "42               1963   739.616594  668.420106   0   0   0   0     0  \n",
       "74               1522   805.066287  668.420106   0   0   0   0     0  \n",
       "...               ...          ...         ...  ..  ..  ..  ..   ...  \n",
       "92556            2264  1076.099509  443.207211   0   0   0   0     0  \n",
       "92561            2264   642.763286  374.779727   0   0   0   0     0  \n",
       "92578            1882  3001.387225   95.464875   0   0   0   0     0  \n",
       "92602            2132  1438.488228  649.446212   0   0   0   0     0  \n",
       "92610            2264  2522.655623  451.555016   0   0   0   0     0  \n",
       "\n",
       "[8103 rows x 38 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>조사년도</th>\n",
       "      <th>조사월</th>\n",
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       "      <th>65세이상인구</th>\n",
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       "      <th>29</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
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       "      <th>117</th>\n",
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       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>16232</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22900.0</td>\n",
       "      <td>261.0</td>\n",
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       "      <td>1568</td>\n",
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       "      <th>362</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>16231</td>\n",
       "      <td>5456.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>488.0</td>\n",
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       "      <td>1568</td>\n",
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       "      <th>539</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>16229</td>\n",
       "      <td>5456.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>488.0</td>\n",
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       "      <td>24934.0</td>\n",
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       "      <td>1568</td>\n",
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       "      <th>91735</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>879</td>\n",
       "      <td>3479.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>37005.0</td>\n",
       "      <td>331.0</td>\n",
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       "      <td>2558</td>\n",
       "      <td>1568</td>\n",
       "      <td>2030</td>\n",
       "      <td>372.270219</td>\n",
       "      <td>1070.479832</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>91784</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>7125.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>46050.0</td>\n",
       "      <td>317.0</td>\n",
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       "      <td>2558</td>\n",
       "      <td>1568</td>\n",
       "      <td>2030</td>\n",
       "      <td>2933.173566</td>\n",
       "      <td>515.028487</td>\n",
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       "    <tr>\n",
       "      <th>91831</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>783</td>\n",
       "      <td>4031.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>27444.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2558</td>\n",
       "      <td>1568</td>\n",
       "      <td>2030</td>\n",
       "      <td>1926.605064</td>\n",
       "      <td>643.266551</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>92395</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>219</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23149.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2558</td>\n",
       "      <td>1568</td>\n",
       "      <td>2030</td>\n",
       "      <td>534.492569</td>\n",
       "      <td>288.552332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>92589</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>490.0</td>\n",
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       "      <td>2558</td>\n",
       "      <td>1568</td>\n",
       "      <td>2030</td>\n",
       "      <td>2397.782436</td>\n",
       "      <td>606.040788</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       조사년도  조사월  id_행정동_match  65세이상인구    총수급자   독거노인수  독거노인수_기초생활보장수급권자  \\\n",
       "29     2019   12         16235   2776.0    61.0    33.0               7.0   \n",
       "117    2019   12         16234   5456.0  1020.0  1691.0             488.0   \n",
       "289    2019   12         16232   2720.0   366.0   741.0             137.0   \n",
       "362    2019   12         16231   5456.0  1020.0  1691.0             488.0   \n",
       "539    2019   12         16229   5456.0  1020.0  1691.0             488.0   \n",
       "...     ...  ...           ...      ...     ...     ...               ...   \n",
       "91735  2019   12           879   3479.0   287.0   506.0              55.0   \n",
       "91784  2019   12           830   7125.0   872.0  1455.0             264.0   \n",
       "91831  2019   12           783   4031.0   570.0   891.0             181.0   \n",
       "92395  2019   12           219   2869.0   368.0   571.0             117.0   \n",
       "92589  2019   12            25   3488.0   463.0   691.0             175.0   \n",
       "\n",
       "       독거노인수_저소득    한국총인구   외국인인구  ...  호주산갈비_냉장_100g  호주산불고기_냉동_100g  \\\n",
       "29           1.0  27624.0    92.0  ...           2558            1568   \n",
       "117        127.0  24934.0  2430.0  ...           2558            1568   \n",
       "289         28.0  22900.0   261.0  ...           2558            1568   \n",
       "362        127.0  24934.0  2430.0  ...           2558            1568   \n",
       "539        127.0  24934.0  2430.0  ...           2558            1568   \n",
       "...          ...      ...     ...  ...            ...             ...   \n",
       "91735       15.0  37005.0   331.0  ...           2558            1568   \n",
       "91784       77.0  46050.0   317.0  ...           2558            1568   \n",
       "91831       66.0  27444.0   458.0  ...           2558            1568   \n",
       "92395       11.0  23149.0   643.0  ...           2558            1568   \n",
       "92589       54.0  19500.0   490.0  ...           2558            1568   \n",
       "\n",
       "       호주산불고기_냉장_100g    모범업소와의 거리     관공서와의 거리  Q1  Q2  Q3  Q4  적발건수  \n",
       "29               2030   784.066642   668.420106   0   0   0   0     0  \n",
       "117              2030  1492.217327   509.240759   0   0   0   0     0  \n",
       "289              2030  2086.538154  1925.002891   0   0   0   0     0  \n",
       "362              2030  1520.004000   502.824865   0   0   0   0     0  \n",
       "539              2030  1532.454354   607.949129   0   0   0   0     1  \n",
       "...               ...          ...          ...  ..  ..  ..  ..   ...  \n",
       "91735            2030   372.270219  1070.479832   0   0   0   0     0  \n",
       "91784            2030  2933.173566   515.028487   0   0   0   0     0  \n",
       "91831            2030  1926.605064   643.266551   0   0   0   0     0  \n",
       "92395            2030   534.492569   288.552332   0   0   0   0     0  \n",
       "92589            2030  2397.782436   606.040788   0   0   0   0     0  \n",
       "\n",
       "[766 rows x 38 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### x_train, x_validation, x_test / y_train, y_validation, y_test 로 나누기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = dataset_train.iloc[:, 3:-1]   # 조사년도, 조사월, id_행정동_match 칼럼은 제외하고 변수만 사용\n",
    "y_train = pd.DataFrame(dataset_train.iloc[:, -1], index = dataset_train.index, columns = ['적발건수'])   # index를 보존한 채 data를 split하기 위해 데이터프레임 형태로 만들기\n",
    "\n",
    "x_test = dataset_test.iloc[:, 3:-1]     # 조사년도, 조사월, id_행정동_match 칼럼은 제외하고 변수만 사용\n",
    "y_test = pd.DataFrame(dataset_test.iloc[:, -1], index = dataset_test.index, columns = ['적발건수'])     # index를 보존한 채 data를 split하기 위해 데이터프레임 형태로 만들기\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size = 0.2, random_state=0)    # 2010~2018년 train data를 다시 train, validation data로 나눔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>65세이상인구</th>\n",
       "      <th>총수급자</th>\n",
       "      <th>독거노인수</th>\n",
       "      <th>독거노인수_기초생활보장수급권자</th>\n",
       "      <th>독거노인수_저소득</th>\n",
       "      <th>한국총인구</th>\n",
       "      <th>외국인인구</th>\n",
       "      <th>음식점업</th>\n",
       "      <th>도소매업</th>\n",
       "      <th>식품제조업(가공)</th>\n",
       "      <th>...</th>\n",
       "      <th>호주산갈비_냉동_100g</th>\n",
       "      <th>호주산갈비_냉장_100g</th>\n",
       "      <th>호주산불고기_냉동_100g</th>\n",
       "      <th>호주산불고기_냉장_100g</th>\n",
       "      <th>모범업소와의 거리</th>\n",
       "      <th>관공서와의 거리</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70448</th>\n",
       "      <td>2152.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19628.0</td>\n",
       "      <td>4298.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2309</td>\n",
       "      <td>1400</td>\n",
       "      <td>2132</td>\n",
       "      <td>88.154473</td>\n",
       "      <td>592.509273</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>5105.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1747.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25395.0</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>1943</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>1525.670015</td>\n",
       "      <td>509.240759</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>89109</th>\n",
       "      <td>2999.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25341.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>1628.223442</td>\n",
       "      <td>616.585248</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>81262</th>\n",
       "      <td>3804.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>337.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2309</td>\n",
       "      <td>1400</td>\n",
       "      <td>2132</td>\n",
       "      <td>591.998741</td>\n",
       "      <td>578.332975</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>24929</th>\n",
       "      <td>2477.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21961.0</td>\n",
       "      <td>1646.0</td>\n",
       "      <td>8</td>\n",
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       "      <th>60441</th>\n",
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       "      <td>618.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>303.0</td>\n",
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       "      <td>7590.0</td>\n",
       "      <td>1002.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>1943</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>239.334024</td>\n",
       "      <td>170.036592</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>41654</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19616.0</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4620.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>27189.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>2226.385517</td>\n",
       "      <td>550.949027</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>33723</th>\n",
       "      <td>3637.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31960.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>1745.691565</td>\n",
       "      <td>555.836309</td>\n",
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       "      <th>35242</th>\n",
       "      <td>4730.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29541.0</td>\n",
       "      <td>553.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1943</td>\n",
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       "      <td>1882</td>\n",
       "      <td>3372.724030</td>\n",
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      "text/plain": [
       "       65세이상인구   총수급자   독거노인수  독거노인수_기초생활보장수급권자  독거노인수_저소득    한국총인구   외국인인구  \\\n",
       "70448   2152.0  397.0   607.0             125.0       31.0  19628.0  4298.0   \n",
       "124     5105.0  850.0  1747.0             934.0       29.0  25395.0  2375.0   \n",
       "89109   2999.0  157.0   543.0              54.0       17.0  25341.0   394.0   \n",
       "81262   3804.0  144.0   611.0              40.0       18.0  30154.0   337.0   \n",
       "24929   2477.0  438.0   681.0             151.0       79.0  21961.0  1646.0   \n",
       "...        ...    ...     ...               ...        ...      ...     ...   \n",
       "60441   1865.0  618.0   838.0             303.0       25.0   7590.0  1002.0   \n",
       "41654   3155.0  452.0   950.0             182.0       25.0  19616.0  2630.0   \n",
       "22050   4620.0  689.0  1327.0             300.0       35.0  27189.0  1694.0   \n",
       "33723   3637.0  173.0   760.0              58.0       10.0  31960.0   264.0   \n",
       "35242   4730.0  839.0  1228.0             257.0       45.0  29541.0   553.0   \n",
       "\n",
       "       음식점업  도소매업  식품제조업(가공)  ...  호주산갈비_냉동_100g  호주산갈비_냉장_100g  \\\n",
       "70448     0     0          2  ...           1830           2309   \n",
       "124       3     5         18  ...           1943           2163   \n",
       "89109     6     0          2  ...           1830           2587   \n",
       "81262     0     0          0  ...           1830           2309   \n",
       "24929     8     0          0  ...           1830           2587   \n",
       "...     ...   ...        ...  ...            ...            ...   \n",
       "60441     1     3          0  ...           1943           2163   \n",
       "41654     0     0          0  ...           1830           2309   \n",
       "22050     1     2          1  ...           1830           2587   \n",
       "33723     3     0          3  ...           1830           2587   \n",
       "35242     0     0          0  ...           1943           2163   \n",
       "\n",
       "       호주산불고기_냉동_100g  호주산불고기_냉장_100g    모범업소와의 거리     관공서와의 거리  Q1  Q2  Q3  \\\n",
       "70448            1400            2132    88.154473   592.509273   0   0   0   \n",
       "124              1537            1882  1525.670015   509.240759   0   0   0   \n",
       "89109            1400            2264  1628.223442   616.585248   0   0   0   \n",
       "81262            1400            2132   591.998741   578.332975   0   0   0   \n",
       "24929            1400            2264   877.576143   478.907854   0   0   0   \n",
       "...               ...             ...          ...          ...  ..  ..  ..   \n",
       "60441            1537            1882   239.334024   170.036592   0   0   0   \n",
       "41654            1400            2132  1882.407742   236.351200   0   0   0   \n",
       "22050            1400            2264  2226.385517   550.949027   0   0   0   \n",
       "33723            1400            2264  1745.691565   555.836309   0   0   0   \n",
       "35242            1537            1882  3372.724030  1093.992496   0   0   0   \n",
       "\n",
       "       Q4  \n",
       "70448   0  \n",
       "124     0  \n",
       "89109   0  \n",
       "81262   0  \n",
       "24929   0  \n",
       "...    ..  \n",
       "60441   0  \n",
       "41654   0  \n",
       "22050   0  \n",
       "33723   0  \n",
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       "\n",
       "[6482 rows x 34 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>60441</th>\n",
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       "    <tr>\n",
       "      <th>41654</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>22050</th>\n",
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       "    <tr>\n",
       "      <th>33723</th>\n",
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       "      <th>35242</th>\n",
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       "<p>6482 rows × 1 columns</p>\n",
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       "       적발건수\n",
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       "[6482 rows x 1 columns]"
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     },
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       "      <th></th>\n",
       "      <th>65세이상인구</th>\n",
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       "      <th>독거노인수</th>\n",
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       "      <th>Q1</th>\n",
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       "      <th>289</th>\n",
       "      <td>2720.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>261.0</td>\n",
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       "      <td>2558</td>\n",
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       "      <td>372.270219</td>\n",
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       "      <td>264.0</td>\n",
       "      <td>77.0</td>\n",
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       "       65세이상인구    총수급자   독거노인수  독거노인수_기초생활보장수급권자  독거노인수_저소득    한국총인구   외국인인구  \\\n",
       "29      2776.0    61.0    33.0               7.0        1.0  27624.0    92.0   \n",
       "117     5456.0  1020.0  1691.0             488.0      127.0  24934.0  2430.0   \n",
       "289     2720.0   366.0   741.0             137.0       28.0  22900.0   261.0   \n",
       "362     5456.0  1020.0  1691.0             488.0      127.0  24934.0  2430.0   \n",
       "539     5456.0  1020.0  1691.0             488.0      127.0  24934.0  2430.0   \n",
       "...        ...     ...     ...               ...        ...      ...     ...   \n",
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       "92589   3488.0   463.0   691.0             175.0       54.0  19500.0   490.0   \n",
       "\n",
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       "29        0     0          0  ...           2090           2558   \n",
       "117       5     5         19  ...           2090           2558   \n",
       "289       0     0          0  ...           2090           2558   \n",
       "362       1     2          3  ...           2090           2558   \n",
       "539       1     7          4  ...           2090           2558   \n",
       "...     ...   ...        ...  ...            ...            ...   \n",
       "91735     5     6          0  ...           2090           2558   \n",
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       "91831     0     0          0  ...           2090           2558   \n",
       "92395     2     0          0  ...           2090           2558   \n",
       "92589     0     1          0  ...           2090           2558   \n",
       "\n",
       "       호주산불고기_냉동_100g  호주산불고기_냉장_100g    모범업소와의 거리     관공서와의 거리  Q1  Q2  Q3  \\\n",
       "29               1568            2030   784.066642   668.420106   0   0   0   \n",
       "117              1568            2030  1492.217327   509.240759   0   0   0   \n",
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       "539              1568            2030  1532.454354   607.949129   0   0   0   \n",
       "...               ...             ...          ...          ...  ..  ..  ..   \n",
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       "92589            1568            2030  2397.782436   606.040788   0   0   0   \n",
       "\n",
       "       Q4  \n",
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       "362     0  \n",
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       "...    ..  \n",
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       "[766 rows x 34 columns]"
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   "execution_count": 27,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>65세이상인구</th>\n",
       "      <th>총수급자</th>\n",
       "      <th>독거노인수</th>\n",
       "      <th>독거노인수_기초생활보장수급권자</th>\n",
       "      <th>독거노인수_저소득</th>\n",
       "      <th>한국총인구</th>\n",
       "      <th>외국인인구</th>\n",
       "      <th>음식점업</th>\n",
       "      <th>도소매업</th>\n",
       "      <th>식품제조업(가공)</th>\n",
       "      <th>...</th>\n",
       "      <th>호주산갈비_냉동_100g</th>\n",
       "      <th>호주산갈비_냉장_100g</th>\n",
       "      <th>호주산불고기_냉동_100g</th>\n",
       "      <th>호주산불고기_냉장_100g</th>\n",
       "      <th>모범업소와의 거리</th>\n",
       "      <th>관공서와의 거리</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66540</th>\n",
       "      <td>2688.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>8701.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1943</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>3028.367764</td>\n",
       "      <td>407.958960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51424</th>\n",
       "      <td>2767.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20223.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1215</td>\n",
       "      <td>1769</td>\n",
       "      <td>1100</td>\n",
       "      <td>1744</td>\n",
       "      <td>839.088220</td>\n",
       "      <td>236.754187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78865</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15286.0</td>\n",
       "      <td>2101.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1943</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>1106.713792</td>\n",
       "      <td>552.198320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29890</th>\n",
       "      <td>3444.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>27994.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>2429.042364</td>\n",
       "      <td>314.039066</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2207.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23375.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2309</td>\n",
       "      <td>1400</td>\n",
       "      <td>2132</td>\n",
       "      <td>1933.622922</td>\n",
       "      <td>1823.915782</td>\n",
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       "      <td>...</td>\n",
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       "      <th>55996</th>\n",
       "      <td>3427.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25867.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>845.268354</td>\n",
       "      <td>331.911706</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10923</th>\n",
       "      <td>4526.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>29135.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>1991.124802</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25318</th>\n",
       "      <td>2595.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33377.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>1081.857603</td>\n",
       "      <td>896.971711</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85362</th>\n",
       "      <td>4732.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>42028.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>2587</td>\n",
       "      <td>1400</td>\n",
       "      <td>2264</td>\n",
       "      <td>375.057454</td>\n",
       "      <td>491.032809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>58886</th>\n",
       "      <td>3820.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>884.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33844.0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1943</td>\n",
       "      <td>2163</td>\n",
       "      <td>1537</td>\n",
       "      <td>1882</td>\n",
       "      <td>659.279215</td>\n",
       "      <td>561.986319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "<p>1621 rows × 34 columns</p>\n",
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      ],
      "text/plain": [
       "       65세이상인구   총수급자   독거노인수  독거노인수_기초생활보장수급권자  독거노인수_저소득    한국총인구   외국인인구  \\\n",
       "66540   2688.0  279.0   694.0             167.0       29.0  13110.0  8701.0   \n",
       "51424   2767.0  375.0   297.0              91.0       12.0  20223.0   234.0   \n",
       "78865   2012.0  231.0   514.0              84.0       28.0  15286.0  2101.0   \n",
       "29890   3444.0  177.0   976.0             303.0      235.0  27994.0   202.0   \n",
       "575     2207.0  235.0   469.0              63.0       25.0  23375.0   258.0   \n",
       "...        ...    ...     ...               ...        ...      ...     ...   \n",
       "55996   3427.0  162.0  1291.0             456.0       92.0  25867.0   307.0   \n",
       "10923   4526.0  721.0  1327.0             311.0      104.0  29135.0   286.0   \n",
       "25318   2595.0  142.0   435.0              42.0       11.0  33377.0    99.0   \n",
       "85362   4732.0  248.0   762.0              88.0       88.0  42028.0   175.0   \n",
       "58886   3820.0  437.0   884.0             120.0       40.0  33844.0   826.0   \n",
       "\n",
       "       음식점업  도소매업  식품제조업(가공)  ...  호주산갈비_냉동_100g  호주산갈비_냉장_100g  \\\n",
       "66540     1     1          0  ...           1943           2163   \n",
       "51424     8     2          0  ...           1215           1769   \n",
       "78865     0     0          0  ...           1943           2163   \n",
       "29890     0     0          0  ...           1830           2587   \n",
       "575       0     0          1  ...           1830           2309   \n",
       "...     ...   ...        ...  ...            ...            ...   \n",
       "55996     2     1          0  ...           1830           2587   \n",
       "10923     0     0          0  ...           1830           2587   \n",
       "25318     0     0          0  ...           1830           2587   \n",
       "85362    10     2          1  ...           1830           2587   \n",
       "58886     0     0          0  ...           1943           2163   \n",
       "\n",
       "       호주산불고기_냉동_100g  호주산불고기_냉장_100g    모범업소와의 거리     관공서와의 거리  Q1  Q2  Q3  \\\n",
       "66540            1537            1882  3028.367764   407.958960   0   0   0   \n",
       "51424            1100            1744   839.088220   236.754187   0   0   0   \n",
       "78865            1537            1882  1106.713792   552.198320   0   0   0   \n",
       "29890            1400            2264  2429.042364   314.039066   0   0   0   \n",
       "575              1400            2132  1933.622922  1823.915782   0   0   0   \n",
       "...               ...             ...          ...          ...  ..  ..  ..   \n",
       "55996            1400            2264   845.268354   331.911706   0   0   0   \n",
       "10923            1400            2264  1991.124802   452.369397   0   0   0   \n",
       "25318            1400            2264  1081.857603   896.971711   0   0   0   \n",
       "85362            1400            2264   375.057454   491.032809   0   0   0   \n",
       "58886            1537            1882   659.279215   561.986319   0   0   0   \n",
       "\n",
       "       Q4  \n",
       "66540   0  \n",
       "51424   0  \n",
       "78865   0  \n",
       "29890   0  \n",
       "575     0  \n",
       "...    ..  \n",
       "55996   0  \n",
       "10923   0  \n",
       "25318   0  \n",
       "85362   0  \n",
       "58886   1  \n",
       "\n",
       "[1621 rows x 34 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>적발건수</th>\n",
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       "       적발건수\n",
       "66540     0\n",
       "51424     0\n",
       "78865     0\n",
       "29890     0\n",
       "575       0\n",
       "...     ...\n",
       "55996     0\n",
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       "25318     0\n",
       "85362     0\n",
       "58886     0\n",
       "\n",
       "[1621 rows x 1 columns]"
      ]
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     "execution_count": 29,
     "metadata": {},
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    }
   ],
   "source": [
    "y_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### x_train, y_train 각각에 MinMaxScaler를 fit하여 transform 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_x = MinMaxScaler(feature_range=(0,1)) # normalize the features\n",
    "scaler_x.fit(x_train)\n",
    "x_train_scaled = scaler_x.transform(x_train)\n",
    "x_test_scaled = scaler_x.transform(x_test)\n",
    "x_val_scaled = scaler_x.transform(x_val)\n",
    "\n",
    "scaler_y = MinMaxScaler(feature_range=(0,1)) # normalize the features\n",
    "scaler_y.fit(y_train)\n",
    "y_train_scaled = scaler_y.transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n",
    "y_val_scaled = scaler_y.transform(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(766, 34)\n"
     ]
    },
    {
     "data": {
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       "array([[0.37379117, 0.01793722, 0.01374046, ..., 0.        , 0.        ,\n",
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       "       [0.36594254, 0.10911809, 0.3740458 , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.54968465, 0.17010463, 0.45038168, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.38682551, 0.10971599, 0.28753181, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.47358094, 0.13811659, 0.34860051, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x_test_scaled.shape)\n",
    "x_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(766, 1)\n"
     ]
    },
    {
     "data": {
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       "       [0.  ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_test_scaled.shape)\n",
    "y_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 여러가지 Regressor 중 RMSE가 가장 낮은 모델 찾기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### random_state는 0으로 통일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0853\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.metrics import explained_variance_score, mean_squared_error, r2_score\n",
    "\n",
    "regr = AdaBoostRegressor(random_state=0, n_estimators=50)\n",
    "regr.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.1009\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "regr = BaggingRegressor(base_estimator=SVR(), n_estimators=10, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExtraTreesRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0358\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.034600\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "regr = GradientBoostingRegressor(n_estimators=100, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.6f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## StackingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0400\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.ensemble import RandomForestRegressor, StackingRegressor\n",
    "\n",
    "estimators = [('lr', RidgeCV()),\n",
    "              ('svr', LinearSVR(random_state=0))]\n",
    "regr = StackingRegressor(estimators=estimators,\n",
    "                        final_estimator=RandomForestRegressor(n_estimators=100, random_state=0)).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.033800\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "r1 = LinearRegression()\n",
    "r2 = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "regr = VotingRegressor([('lr', r1), ('rf', r2)]).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.6f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HistGradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0345\n"
     ]
    }
   ],
   "source": [
    "from sklearn.experimental import enable_hist_gradient_boosting  \n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "regr = HistGradientBoostingRegressor(random_state = 0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0343\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "regr = lgb.LGBMRegressor(random_state=0, n_jobs = -1)\n",
    "                                                \n",
    "regr.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0360\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "regr = XGBRegressor(random_state = 0, n_jobs = -1)\n",
    "\n",
    "regr.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_val_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_val_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.4f\" % (rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 모델링하여 결과값 만들기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12월"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.031718\n"
     ]
    }
   ],
   "source": [
    "# validation data로 가장 낮은 RMSE를 찾았으므로, 실제 test data를 넣기\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "regr = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred = regr.predict(x_test_scaled) \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test_scaled, y_pred))\n",
    "\n",
    "print(\"RMSE: %.6f\" % (rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaling했던 것을 다시 inverse_transform\n",
    "y_pred_df = pd.DataFrame(y_pred, index=y_test.index, columns=['적발건수'])\n",
    "y_pred_real = scaler_y.inverse_transform(y_pred_df)\n",
    "y_pred_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(y_pred_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "적발건수    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <th>적발 예측</th>\n",
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       "      <th>761</th>\n",
       "      <td>91735</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>73611_역삼2동</td>\n",
       "      <td>73611</td>\n",
       "      <td>역삼2동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>91784</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100752_길동</td>\n",
       "      <td>100752</td>\n",
       "      <td>길동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>91831</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>86839_구의2동</td>\n",
       "      <td>86839</td>\n",
       "      <td>구의2동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>92395</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>70267_논현1동</td>\n",
       "      <td>70267</td>\n",
       "      <td>논현1동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>92589</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>78658_전농2동</td>\n",
       "      <td>78658</td>\n",
       "      <td>전농2동</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  적발건수  적발 예측      id_행정동      id   행정동\n",
       "0       29     0   0.01  92038_가락1동   92038  가락1동\n",
       "1      117     0   0.00   73220_제기동   73220   제기동\n",
       "2      289     0   0.06  74556_양재2동   74556  양재2동\n",
       "3      362     0   0.08   73522_제기동   73522   제기동\n",
       "4      539     1   0.05   73219_제기동   73219   제기동\n",
       "..     ...   ...    ...         ...     ...   ...\n",
       "761  91735     0   0.00  73611_역삼2동   73611  역삼2동\n",
       "762  91784     0   0.00   100752_길동  100752    길동\n",
       "763  91831     0   0.03  86839_구의2동   86839  구의2동\n",
       "764  92395     0   0.12  70267_논현1동   70267  논현1동\n",
       "765  92589     0   0.02  78658_전농2동   78658  전농2동\n",
       "\n",
       "[766 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과물 만드는 과정\n",
    "final_result = pd.concat([y_test.reset_index(), pd.DataFrame(y_pred_real)], axis = 1) # 실제 2019년 적발건수인 y_test와, 모델을 통해 얻은 예측 적발건수 y_pred_real(scaling한 걸 다시 되돌린 것)\n",
    "final_result.rename(columns = {0 : '적발 예측'}, inplace = True)\n",
    "\n",
    "final_result['id_행정동'] = list(dataset_test['id_행정동_match'].values)   # final_result의 index와 dataset_test의 index 순서는 같으므로 id_행정동_match 부분을 가져옴\n",
    "\n",
    "# 기존에 만들었던 'id_행정동'과 내림차순으로 labeling했던 \"dic\"변수에서 key와 value 순서 바꾸기\n",
    "new_dic = {}\n",
    "for k,v in dic.items():\n",
    "    new_dic[v] = k\n",
    "\n",
    "final_result['id_행정동'].replace(new_dic, inplace = True)\n",
    "\n",
    "grid_id = []\n",
    "dong = []\n",
    "# id_행정동_match 부분을 id와 행정동 부분으로 나누어 데이터프레임에 담기\n",
    "for i in range(len(final_result)):\n",
    "    grid_id.append(str(final_result['id_행정동'][i]).split('_')[0])\n",
    "    dong.append(str(final_result['id_행정동'][i]).split('_')[1])\n",
    "final_result['id'] = grid_id\n",
    "final_result['행정동'] = dong\n",
    "final_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 예측값 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result.to_csv('./머신러닝_예측값/12월_예측값_ExtraTrees.csv', encoding='cp949')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 사용한 머신러닝 모델 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./머신러닝_모델/12월_ExtraTreesRegressor.pkl', 'wb') as f:\n",
    "    pkl.dump(regr, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 변수 중요도 그려서 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04616653, 0.04963143, 0.03820545, 0.04529088, 0.07081284,\n",
       "       0.05728863, 0.05243391, 0.10290632, 0.08040274, 0.06897124,\n",
       "       0.07322956, 0.00176786, 0.00275507, 0.00242296, 0.00277106,\n",
       "       0.00600894, 0.0024431 , 0.00244148, 0.002864  , 0.00724163,\n",
       "       0.00272901, 0.00310692, 0.00557654, 0.00193529, 0.0019344 ,\n",
       "       0.00259735, 0.002109  , 0.00312469, 0.11831335, 0.1300433 ,\n",
       "       0.00187739, 0.00026133, 0.00037866, 0.0099571 ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.family'] = 'Malgun Gothic'  # 맑은 고딕으로 폰트 설정 \n",
    "matplotlib.rcParams['axes.unicode_minus'] = False    # 마이너스(-) 폰트 깨짐 방지\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "colors = ['red','orangered', 'orange', 'gold', 'khaki','aquamarine', 'darkturquoise', 'steelblue', 'royalblue', 'purple']\n",
    "\n",
    "(pd.Series(regr.feature_importances_, index=x_train.columns)\n",
    "   .nlargest(35)\n",
    "   .plot(kind='barh', color = colors).invert_yaxis())  \n",
    "\n",
    "plt.title('변수 중요도', fontsize = 24, pad = 15)\n",
    "plt.ylabel('변수', fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.savefig('./머신러닝_시각화/12월_ExtraTreesRegressor_변수중요도.png', dpi=200, facecolor='#eeeeee', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 월별 머신러닝 모델들의 성능 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5min 47sl : 12\n"
     ]
    },
    {
     "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>월</th>\n",
       "      <th>AdaBoost</th>\n",
       "      <th>BaggingRegressor</th>\n",
       "      <th>ExtraTreesRegressor</th>\n",
       "      <th>GradientBoostingRegressor</th>\n",
       "      <th>StackingRegressor</th>\n",
       "      <th>VotingRegressor</th>\n",
       "      <th>HistGradientBoostingRegressor</th>\n",
       "      <th>LightGBM</th>\n",
       "      <th>XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.123537</td>\n",
       "      <td>0.104846</td>\n",
       "      <td>0.0581266</td>\n",
       "      <td>0.0575248</td>\n",
       "      <td>0.0640627</td>\n",
       "      <td>0.0562821</td>\n",
       "      <td>0.0567845</td>\n",
       "      <td>0.057455</td>\n",
       "      <td>0.0620003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.182309</td>\n",
       "      <td>0.109781</td>\n",
       "      <td>0.0822982</td>\n",
       "      <td>0.0760123</td>\n",
       "      <td>0.0808482</td>\n",
       "      <td>0.0755702</td>\n",
       "      <td>0.0796439</td>\n",
       "      <td>0.078531</td>\n",
       "      <td>0.0871244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.201955</td>\n",
       "      <td>0.192349</td>\n",
       "      <td>0.185996</td>\n",
       "      <td>0.185063</td>\n",
       "      <td>0.189999</td>\n",
       "      <td>0.181045</td>\n",
       "      <td>0.183556</td>\n",
       "      <td>0.184143</td>\n",
       "      <td>0.188602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.128126</td>\n",
       "      <td>0.128528</td>\n",
       "      <td>0.0972496</td>\n",
       "      <td>0.0915351</td>\n",
       "      <td>0.105449</td>\n",
       "      <td>0.0962862</td>\n",
       "      <td>0.0982115</td>\n",
       "      <td>0.0981845</td>\n",
       "      <td>0.0973981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0281544</td>\n",
       "      <td>0.0756824</td>\n",
       "      <td>0.0242979</td>\n",
       "      <td>0.0227712</td>\n",
       "      <td>0.0260337</td>\n",
       "      <td>0.0200397</td>\n",
       "      <td>0.0209164</td>\n",
       "      <td>0.0212303</td>\n",
       "      <td>0.0255155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.16015</td>\n",
       "      <td>0.106324</td>\n",
       "      <td>0.060282</td>\n",
       "      <td>0.054378</td>\n",
       "      <td>0.0661946</td>\n",
       "      <td>0.0546286</td>\n",
       "      <td>0.0572263</td>\n",
       "      <td>0.0570453</td>\n",
       "      <td>0.0563195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.110077</td>\n",
       "      <td>0.10622</td>\n",
       "      <td>0.0628621</td>\n",
       "      <td>0.059347</td>\n",
       "      <td>0.0669118</td>\n",
       "      <td>0.0586131</td>\n",
       "      <td>0.0592557</td>\n",
       "      <td>0.0591003</td>\n",
       "      <td>0.0635497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0613531</td>\n",
       "      <td>0.0963022</td>\n",
       "      <td>0.0270733</td>\n",
       "      <td>0.0242145</td>\n",
       "      <td>0.0278098</td>\n",
       "      <td>0.0242375</td>\n",
       "      <td>0.0248014</td>\n",
       "      <td>0.024772</td>\n",
       "      <td>0.0271903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.208092</td>\n",
       "      <td>0.105154</td>\n",
       "      <td>0.0650857</td>\n",
       "      <td>0.0607714</td>\n",
       "      <td>0.0686333</td>\n",
       "      <td>0.0607742</td>\n",
       "      <td>0.0611185</td>\n",
       "      <td>0.0612412</td>\n",
       "      <td>0.065183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0914601</td>\n",
       "      <td>0.11775</td>\n",
       "      <td>0.0697987</td>\n",
       "      <td>0.0628682</td>\n",
       "      <td>0.0780049</td>\n",
       "      <td>0.0703437</td>\n",
       "      <td>0.0714238</td>\n",
       "      <td>0.0717962</td>\n",
       "      <td>0.0710955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0842242</td>\n",
       "      <td>0.100822</td>\n",
       "      <td>0.0449222</td>\n",
       "      <td>0.0398833</td>\n",
       "      <td>0.0431311</td>\n",
       "      <td>0.0401897</td>\n",
       "      <td>0.0411194</td>\n",
       "      <td>0.0412534</td>\n",
       "      <td>0.0449722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0852651</td>\n",
       "      <td>0.100938</td>\n",
       "      <td>0.0358104</td>\n",
       "      <td>0.0346005</td>\n",
       "      <td>0.0400308</td>\n",
       "      <td>0.0337997</td>\n",
       "      <td>0.0345019</td>\n",
       "      <td>0.0342908</td>\n",
       "      <td>0.0360075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     월   AdaBoost BaggingRegressor ExtraTreesRegressor  \\\n",
       "0    1   0.123537         0.104846           0.0581266   \n",
       "1    2   0.182309         0.109781           0.0822982   \n",
       "2    3   0.201955         0.192349            0.185996   \n",
       "3    4   0.128126         0.128528           0.0972496   \n",
       "4    5  0.0281544        0.0756824           0.0242979   \n",
       "5    6    0.16015         0.106324            0.060282   \n",
       "6    7   0.110077          0.10622           0.0628621   \n",
       "7    8  0.0613531        0.0963022           0.0270733   \n",
       "8    9   0.208092         0.105154           0.0650857   \n",
       "9   10  0.0914601          0.11775           0.0697987   \n",
       "10  11  0.0842242         0.100822           0.0449222   \n",
       "11  12  0.0852651         0.100938           0.0358104   \n",
       "\n",
       "   GradientBoostingRegressor StackingRegressor VotingRegressor  \\\n",
       "0                  0.0575248         0.0640627       0.0562821   \n",
       "1                  0.0760123         0.0808482       0.0755702   \n",
       "2                   0.185063          0.189999        0.181045   \n",
       "3                  0.0915351          0.105449       0.0962862   \n",
       "4                  0.0227712         0.0260337       0.0200397   \n",
       "5                   0.054378         0.0661946       0.0546286   \n",
       "6                   0.059347         0.0669118       0.0586131   \n",
       "7                  0.0242145         0.0278098       0.0242375   \n",
       "8                  0.0607714         0.0686333       0.0607742   \n",
       "9                  0.0628682         0.0780049       0.0703437   \n",
       "10                 0.0398833         0.0431311       0.0401897   \n",
       "11                 0.0346005         0.0400308       0.0337997   \n",
       "\n",
       "   HistGradientBoostingRegressor   LightGBM    XGBoost  \n",
       "0                      0.0567845   0.057455  0.0620003  \n",
       "1                      0.0796439   0.078531  0.0871244  \n",
       "2                       0.183556   0.184143   0.188602  \n",
       "3                      0.0982115  0.0981845  0.0973981  \n",
       "4                      0.0209164  0.0212303  0.0255155  \n",
       "5                      0.0572263  0.0570453  0.0563195  \n",
       "6                      0.0592557  0.0591003  0.0635497  \n",
       "7                      0.0248014   0.024772  0.0271903  \n",
       "8                      0.0611185  0.0612412   0.065183  \n",
       "9                      0.0714238  0.0717962  0.0710955  \n",
       "10                     0.0411194  0.0412534  0.0449722  \n",
       "11                     0.0345019  0.0342908  0.0360075  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model_list = ['월', 'AdaBoost', 'BaggingRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'StackingRegressor', 'VotingRegressor', 'HistGradientBoostingRegressor', 'LightGBM', 'XGBoost']\n",
    "all_rmse = pd.DataFrame(columns=model_list)\n",
    "all_rmse['월'] = np.arange(1, 13)\n",
    "for i in all_rmse['월'].values:\n",
    "    print(\"Process : {} | Total : 12\".format(i), end='\\r')\n",
    "    # 2010~2018년도를 train data로, 2019년을 test data로 사용하여 월별로 뽑아옴\n",
    "    # 1~12 월별로 train, test 분리\n",
    "    dataset_train = dataset[(dataset['조사년도'] <= 2018) & (dataset['조사월'] == i)]  \n",
    "    dataset_test = dataset[(dataset['조사년도'] == 2019) & (dataset['조사월'] == i)]\n",
    "\n",
    "    # scaling\n",
    "    x_train = dataset_train.iloc[:, 3:-1]   # 조사년도, 조사월, id_행정동_match 칼럼은 제외하고 변수만 사용\n",
    "    y_train = pd.DataFrame(dataset_train.iloc[:, -1], index = dataset_train.index, columns = ['적발건수'])   # index를 보존한 채 data를 split하기 위해 데이터프레임 형태로 만들기\n",
    "\n",
    "    x_test = dataset_test.iloc[:, 3:-1]     # 조사년도, 조사월, id_행정동_match 칼럼은 제외하고 변수만 사용\n",
    "    y_test = pd.DataFrame(dataset_test.iloc[:, -1], index = dataset_test.index, columns = ['적발건수'])     # index를 보존한 채 data를 split하기 위해 데이터프레임 형태로 만들기\n",
    "\n",
    "    x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size = 0.2, random_state=0)    # 2010~2018년 train data를 다시 train, validation data로 나눔\n",
    "\n",
    "    # x_train, y_train 각각에 MinMaxScaler를 fit하여 transform 진행\n",
    "    scaler_x = MinMaxScaler(feature_range=(0,1)) # normalize the features\n",
    "    scaler_x.fit(x_train)\n",
    "    x_train_scaled = scaler_x.transform(x_train)\n",
    "    x_test_scaled = scaler_x.transform(x_test)\n",
    "    x_val_scaled = scaler_x.transform(x_val)\n",
    "\n",
    "    scaler_y = MinMaxScaler(feature_range=(0,1)) # normalize the features\n",
    "    scaler_y.fit(y_train)\n",
    "    y_train_scaled = scaler_y.transform(y_train)\n",
    "    y_test_scaled = scaler_y.transform(y_test)\n",
    "    y_val_scaled = scaler_y.transform(y_val)\n",
    "    \n",
    "    # AdaBoost\n",
    "    regr_ada = AdaBoostRegressor(random_state=0, n_estimators=50)\n",
    "    regr_ada.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_ada = regr_ada.predict(x_val_scaled) \n",
    "\n",
    "    rmse_ada = np.sqrt(mean_squared_error(y_val_scaled, y_pred_ada))\n",
    "\n",
    "    all_rmse.iloc[i-1, 1] = rmse_ada    # 월별 AdaBoost의 RMSE 입력\n",
    "\n",
    "    # BaggingRegressor\n",
    "    regr_bag = BaggingRegressor(base_estimator=SVR(), n_estimators=10, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_bag = regr_bag.predict(x_val_scaled) \n",
    "\n",
    "    rmse_bag = np.sqrt(mean_squared_error(y_val_scaled, y_pred_bag))\n",
    "\n",
    "    all_rmse.iloc[i-1, 2] = rmse_bag    # 월별 BaggingRegressor의 RMSE 입력\n",
    "\n",
    "    # ExtraTreesRegressor\n",
    "    regr_et = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_et = regr_et.predict(x_val_scaled) \n",
    "\n",
    "    rmse_et = np.sqrt(mean_squared_error(y_val_scaled, y_pred_et))\n",
    "\n",
    "    all_rmse.iloc[i-1, 3] = rmse_et    # 월별 ExtraTreesRegressor의 RMSE 입력\n",
    "\n",
    "    # GradientBoostingRegressor\n",
    "    regr_gbr = GradientBoostingRegressor(n_estimators=100, random_state=0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_gbr = regr_gbr.predict(x_val_scaled) \n",
    "\n",
    "    rmse_gbr = np.sqrt(mean_squared_error(y_val_scaled, y_pred_gbr))\n",
    "\n",
    "    all_rmse.iloc[i-1, 4] = rmse_gbr    # 월별 GradientBoostingRegressor의 RMSE 입력\n",
    "\n",
    "    # StackingRegressor\n",
    "    estimators = [('lr', RidgeCV()),\n",
    "                  ('svr', LinearSVR(random_state=0))]\n",
    "    regr_sr = StackingRegressor(estimators=estimators,\n",
    "                            final_estimator=RandomForestRegressor(n_estimators=100, random_state=0)).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_sr = regr_sr.predict(x_val_scaled) \n",
    "\n",
    "    rmse_sr = np.sqrt(mean_squared_error(y_val_scaled, y_pred_sr))\n",
    "\n",
    "    all_rmse.iloc[i-1, 5] = rmse_sr    # 월별 StackingRegressor의 RMSE 입력\n",
    "\n",
    "    # VotingRegressor\n",
    "    r1 = LinearRegression()\n",
    "    r2 = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "    regr_vr = VotingRegressor([('lr', r1), ('rf', r2)]).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_vr = regr_vr.predict(x_val_scaled) \n",
    "\n",
    "    rmse_vr = np.sqrt(mean_squared_error(y_val_scaled, y_pred_vr))\n",
    "\n",
    "    all_rmse.iloc[i-1, 6] = rmse_vr    # 월별 VotingRegressor의 RMSE 입력\n",
    "\n",
    "    # HistGradientBoostingRegressor\n",
    "    regr_hgbr = HistGradientBoostingRegressor(random_state = 0).fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_hgbr = regr_hgbr.predict(x_val_scaled) \n",
    "\n",
    "    rmse_hgbr = np.sqrt(mean_squared_error(y_val_scaled, y_pred_hgbr))\n",
    "\n",
    "    all_rmse.iloc[i-1, 7] = rmse_hgbr    # 월별 HistGradientBoostingRegressor의 RMSE 입력\n",
    "\n",
    "    # LightGBM\n",
    "    regr_lgbm = lgb.LGBMRegressor(random_state=0, n_jobs = -1)\n",
    "\n",
    "    regr_lgbm.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_lgbm = regr_lgbm.predict(x_val_scaled) \n",
    "\n",
    "    rmse_lgbm = np.sqrt(mean_squared_error(y_val_scaled, y_pred_lgbm))\n",
    "\n",
    "    all_rmse.iloc[i-1, 8] = rmse_lgbm    # 월별 LightGBM의 RMSE 입력\n",
    "\n",
    "    # XGBoost\n",
    "    regr_xgb = XGBRegressor(random_state = 0, n_jobs = -1)\n",
    "\n",
    "    regr_xgb.fit(x_train_scaled, y_train_scaled)\n",
    "\n",
    "    y_pred_xgb = regr_xgb.predict(x_val_scaled) \n",
    "\n",
    "    rmse_xgb = np.sqrt(mean_squared_error(y_val_scaled, y_pred_xgb))\n",
    "\n",
    "    all_rmse.iloc[i-1, 9] = rmse_xgb    # 월별 XGBoost의 RMSE 입력\n",
    "    \n",
    "all_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (25, 15))\n",
    "\n",
    "x = list(map(lambda x: str(x) + '월', all_rmse['월']))\n",
    "\n",
    "line1 = ax.plot(x, all_rmse['AdaBoost'], color = 'red')\n",
    "line2 = ax.plot(x, all_rmse['BaggingRegressor'], color = 'orange')\n",
    "line3 = ax.plot(x, all_rmse['ExtraTreesRegressor'],color = 'gold')\n",
    "line4 = ax.plot(x, all_rmse['GradientBoostingRegressor'],color = 'aquamarine')\n",
    "line5 = ax.plot(x, all_rmse['StackingRegressor'], color = 'darkturquoise')\n",
    "line6 = ax.plot(x, all_rmse['VotingRegressor'], color = 'purple')\n",
    "line7 = ax.plot(x, all_rmse['HistGradientBoostingRegressor'], color = 'steelblue')\n",
    "line8 = ax.plot(x, all_rmse['LightGBM'], color = 'blue')\n",
    "line9 = ax.plot(x, all_rmse['XGBoost'], color = 'gray')\n",
    "\n",
    "# x, y축 글꼴 크게\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "\n",
    "# y축 범위 설정\n",
    "ax.set_ylim(0, 0.15)\n",
    "\n",
    "# 범례 설정\n",
    "ax.legend(['AdaBoost RMSE',\n",
    "            'BaggingRegressor RMSE',\n",
    "            'ExtraTreesRegressor RMSE',\n",
    "            'GradientBoostingRegressor RMSE',\n",
    "            'StackingRegressor RMSE',\n",
    "            'VotingRegressor RMSE',\n",
    "            'HistGradientBoostingRegressor RMSE',\n",
    "            'LightGBM RMSE',\n",
    "            'XGBoost RMSE'], loc = 0, fontsize = 'large')\n",
    "\n",
    "# 제목\n",
    "fig.suptitle('머신러닝모델 RMSE 비교', fontsize = 30, y=0.915)\n",
    "plt.savefig('./머신러닝_시각화/머신러닝모델_성능비교.png', dpi=200, facecolor='#eeeeee', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "T0cCtWs8cM0s",
    "8IV0_yRYAyx_"
   ],
   "name": "기계학습 final",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}