hee_dajoong(피피티용).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('output/please_final.csv', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-34-b897385b5df8>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-34-b897385b5df8>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    df = df.iloc[[:,-0]]\u001b[0m\n\u001b[1;37m                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# 상동역\n",
    "is_sangdong = df['자전거대여소명'] == '상동역'\n",
    "sangdong= df[is_sangdong]\n",
    "#sangdong\n",
    "df = df.drop(columns=['Unnamed:0'], axis=1)\n",
    "df\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sangdong[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = sangdong[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = mlr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6907486792231796\n"
     ]
    }
   ],
   "source": [
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(df[['강수']], df[['자전거대여량']], alpha=0.4)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시청역\n",
    "is_sicheong = df['자전거대여소명'] == '시청역'\n",
    "sicheong = df[is_sicheong]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sicheong[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = sicheong[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7274958975965815\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 신중동역\n",
    "is_sinjoongdong = df['자전거대여소명'] == '신중동역'\n",
    "sinjoongdong = df[is_sinjoongdong]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sinjoongdong[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = sinjoongdong[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7957567544409467\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 부천역\n",
    "is_bucheon = df['자전거대여소명'] == '부천역'\n",
    "bucheon = df[is_bucheon]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bucheon[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = bucheon[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5935438088589753\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 송내역\n",
    "is_songnae = df['자전거대여소명'] == '송내역'\n",
    "songnae = df[is_songnae]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = songnae[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = songnae[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6585503141134079\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 부천시청역\n",
    "is_busi = df['자전거대여소명'] == '부천시청'\n",
    "busi = df[is_busi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = busi[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = busi[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43918481469489123\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 굴포천역\n",
    "is_gool = df['자전거대여소명'] == '굴포천'\n",
    "gool = df[is_gool]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = gool[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = gool[['자전거대여량']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3772805919860721\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n",
      "<ipython-input-131-48bb97dc068f>:22: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  pval = pd.Series(index=remainder) ## 변수의 p-value\n"
     ]
    }
   ],
   "source": [
    "# 전진단계별 선택법\n",
    "\n",
    "variables = df[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = df[['자전거대여량']]\n",
    "\n",
    "\n",
    "selected_variables = [] ## 선택된 변수들\n",
    "sl_enter = 0.05\n",
    "sl_remove = 0.05\n",
    " \n",
    "sv_per_step = [] ## 각 스텝별로 선택된 변수들\n",
    "adjusted_r_squared = [] ## 각 스텝별 수정된 결정계수\n",
    "steps = [] ## 스텝\n",
    "step = 0\n",
    "while len(variables) > 0:\n",
    "    remainder = list(set(variables) - set(selected_variables))\n",
    "    pval = pd.Series(index=remainder) ## 변수의 p-value\n",
    "    ## 기존에 포함된 변수와 새로운 변수 하나씩 돌아가면서 \n",
    "    ## 선형 모형을 적합한다.\n",
    "    for col in remainder: \n",
    "        X = df[selected_variables+[col]]\n",
    "        X = sm.add_constant(X)\n",
    "        model = sm.OLS(y,X).fit()\n",
    "        pval[col] = model.pvalues[col]\n",
    " \n",
    "    min_pval = pval.min()\n",
    "    if min_pval < sl_enter: ## 최소 p-value 값이 기준 값보다 작으면 포함\n",
    "        selected_variables.append(pval.idxmin())\n",
    "        ## 선택된 변수들에대해서\n",
    "        ## 어떤 변수를 제거할지 고른다.\n",
    "        while len(selected_variables) > 0:\n",
    "            selected_X = df[selected_variables]\n",
    "            selected_X = sm.add_constant(selected_X)\n",
    "            selected_pval = sm.OLS(y,selected_X).fit().pvalues[1:] ## 절편항의 p-value는 뺀다\n",
    "            max_pval = selected_pval.max()\n",
    "            if max_pval >= sl_remove: ## 최대 p-value값이 기준값보다 크거나 같으면 제외\n",
    "                remove_variable = selected_pval.idxmax()\n",
    "                selected_variables.remove(remove_variable)\n",
    "            else:\n",
    "                break\n",
    "        \n",
    "        step += 1\n",
    "        steps.append(step)\n",
    "        adj_r_squared = sm.OLS(y,sm.add_constant(df[selected_variables])).fit().rsquared_adj\n",
    "        adjusted_r_squared.append(adj_r_squared)\n",
    "        sv_per_step.append(selected_variables.copy())\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['청소년 수련시설_5분이내거리최소',\n",
       " '학원_5분이내거리최소',\n",
       " '스포츠_5분이내거리최대',\n",
       " '기온',\n",
       " '스포츠_5분이내거리평균',\n",
       " '부동산_5분이내거리최대',\n",
       " '강수형태',\n",
       " '풍속']"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Hkcfiqo0bN+qjjz4qse1Vq1ZN1apV0+rVq1W3bl2988476tmzp+rUqeNwveI+/pxBC7TgDFoouWOhBVq40vXYgkQP9OAcd+iBFmjBGbRQcsdCC7RwJVqgBVow0AIt0IKBFmiBFgy0QAu0YKAFWqAFAy3QAi0YrscWJHqgB+e4Qw+0QAvOcIcWyhITjkvQ5cuXNWnSJJ0+fVo7d+7UwIEDNXPmTCUmJkoyLrV+pe+//16rVq3SqVOnNGPGDDVu3FiSNG3aNE2cOFEBAQEaOHCggoODbe4vLy/P4p0CWVlZDo8vNzdXFy9e1KpVq8z7kqR27dpp9+7d+u6772x+w42KirK4PX/+fK1bt06pqamKjIy0Gr9r1y5J0u7du9W+fXu7x7Nw4ULFxMToiy++0J133qm1a9dq2LBhGjdunJ544gmH51IgIyPDYawRERFOX049NTXV/INHkry8vHTzzTcrPT1d9erVc2obBXJycpSXl2f3/m3btqlOnTpKSEgwf32vfjdHfHy8Fi5caL59/vx5h/v8888/9e677yo2NlabNm1SQkKCOnfurFGjRmnQoEFW46/18ecILRhogRZowUALtCDRQwF6oAdaMNACLdCCgRZogRYMtEALtGCgBVqgBQMt0AItGGiBFmjBQAu0QAsGWqAFiR4K0AM90IKBFmihPDDhuITk5+erX79+Cg4O1vr16+Xl5aUdO3bo1VdflY+Pj8VltwtUqVJFzzzzjFq1aiXJePeDp6en2rdvr2XLluk///mP1Tq5ublKTk6WyWTSxYsX9dhjjyknJ0fdu3fXhg0bHB7j7bffrvDwcLv333fffTaXf/XVV+b9nT59Wo899pheeuklm98k8vPz9e677+qjjz7SjBkz9PDDD9t950GnTp307LPPqkqVKpKkli1bau/eveZLvffv31+33Xabw3M6f/68tmzZol27dqlBgwaaNm2a/vrXvzpcx54bb7xRZ8+etViWlZWlWrVqubytLl26KCUlxebX6P/+7//08ssva/PmzapSpYoCAwMlGZetv9Lhw4fVuHFjjRs3zrzMx8fH7j6XLl0qT09Pbd26Vdu2bVNwcLB27NihTz/91Pw1vlJxHn/OoIXCrwMt0AIt0EIBd25Boocrvw704N490ELh14EWaIEWaKEALdACLRhogRZowUALtEALBlqgBVow0AIt0IKBFmiBFgzu3IJED1d+HejBvXughcKvAy24dwvlhQnHJcTT01Nr166VZAS2dOlS7dixQ+np6Vq6dKl+/fVX7d+/32Kdu+++2+J2UlKSvL29ze84uOuuu6z2069fP3388cfy8fFRtWrVVLNmzSKDLxAUFGRx+8KFCzp58qT+8pe/WCx/4YUXNGzYMElSaGioYmNj5enpKR8fH9WvX1/e3vYfNm+88YY6d+6s/v37KykpSZMmTdKMGTNsjr399tslSSNHjlRCQoLV/b///rvWrVunW265xe7+hg8fruHDh0uS/vWvf6lbt2769ddfdeDAAY0YMcJibHp6ui5fvmx1GfKlS5fq7rvvVkhIiN59913169dPkvTvf/9btWrVsvmNoChxcXE2v5Ht379f/fr107Jly3TrrbdKkho2bCjJ9jcqPz+/Ii+3XmD06NHmvy9evFj16tVTUFCQRo0aZXN8cR5/zqAFAy0YaIEWaMHgzi1I9FCAHgzu3AMtGGjBQAu0QAsGWqAFWjDQAi3QgoEWaIEWDLRAC7RgoAVaoAUDLdACLRjcuQWJHgrQg8Gde6AFAy0Y3LmF8sKE41Lw6KOPmsOoX7++0tPTtXz5cv3jH//Q3r175eXlVextz58/X5s2bVL37t1dXrfgsvFX3p49e7b5m7AtEydO1L59+xQQEKDmzZs73P748eN15MgRrVy5UpI0Z84cDRo0SC+++KLeeecdu+stWrTI5vLnnnvO4f6u1rlzZ9WuXVu///677r33XiUmJurgwYOqW7euateurUWLFikzM1Pjx4+3uf5DDz2kjz/+2PyOgpUrV2rx4sUO97l27Vr98ssvunjxorKzszVy5Eib4y5fvqywsDDt2rVL69evd/obRU5Ojs6cOaP8/Hzl5OQoOztbWVlZVj+Yrle0QAtXowVaoAWDu7Yg0QM9WHPXHmiBFq5GC7RACwZaoAVaMNACLdCCgRZogRYMtEALtGCgBVqgBQMt0AItGNy1BYke6MGau/ZAC7RwNXdtoSwx4biEHTt2TOnp6Zo2bZp5WcOGDTV+/Hh98cUXOnjwoO666y4lJiZaxZqeni5JiomJsVgeExNjvqS3JI0ZM8bmN7OUlJQSO48rbdq0Sc2aNbP6Znb1uwN69Oihtm3bmi/P7unpqZUrVyo1NdXh9jt16qRTp07ZfJfC888/73Dd9PR01a1bV5LxzoSzZ8+qcePG5vujoqLUq1cvdenSxeF2CixZskQ//PCDTpw4oX/961+68cYb7Y7t06ePOnToIC8vL1WpUkU1atSQr6+vzUvie3l5qW/fvnr77bcdXnL9SrfccovWrVunoUOHytvbW76+vqpRo4b+8pe/JtFkrwAAj4JJREFUWH0zS0tLU8+ePWUymeTh4aG0tDSNGDFC1apVk4eHhx566CGL8dfy+HMWLdACLRhogRZooRA90AM9GGiBFmjBQAu0QAsGWqAFWjDQAi3QgoEWaIEWDLRAC7RgoAVaoAUDLdACLRSiB3qgBwMt0AItlA8mHJewBg0aKC8vz+odDtu3b1daWpr50uhBQUFKTk4up6MsHe3atbNa5uHhoSZNmhS57saNG9W0aVOX97lkyRKtW7dONWrUUJUqVbRu3Tq7l5Pv0KGDcnNzi9zmPffc49S+/f395e/v7/SxPvzww06PlaSOHTuqY8eOTo2tX7++zUveX2nIkCHmv5fF448WLNFCIVow0IJjtGCtorcg0cPV6KGQu/VAC5ZooRAtGGjBMVqwRgsVFy04RgsGWnCMFqzRQsVFC47RgoEWHKMFa7RQcdGCY7RgoAXHaMFaRW9Booer0UMhd+uBFizRQiF3a6GsMeG4hHl6emrr1q2aMmWKIiIi5Onpqfz8fN12223asmWLfH19S2Q/9mazT5gwQf369SuRfVxpypQpioqKslreuXNnzZ0795q3/+ijj9p898SgQYP06quv2l1v4sSJmjhxosNtjxo1SjVq1LBa3qpVK3388ceuHyycQgvFQwuVDy0UDy1UTvRQPPRQ+dBC8dBC5UMLxUMLlQ8tFA8tVD60UDy0UPnQQvHQQuVDC8VDC5UPLRQPLVQ+tFA8tFA50UPx0EPlQwvFQwu4Vh4mk8lU3geB8pGZmalff/1VwcHBZbK/nJwceXp62vymVZSwsDCr2f6vv/66OnfuXEJHV7Ts7Gz5+fnJ09PTqfGunu+5c+dUs2ZNp4/H1fEXLlxQlSpV7L67xJ3RgmtoofKiBdfQQuVGD66hh8qLFlxDC5UXLbiGFiovWnANLVRetOAaWqi8aME1tFB50YJraKHyogXX0ELlRQuuoYXKjR5cQw+VFy24hhaub0w4BgAAAAAAAAAAAAAAAAAAAGCXc9PAAcCNJScnKycnx+nxCQkJpXg0rnP1eFw535ycHKt3NpXkeEmaOnWqS+NRemjBPlpwL7RgHy24H3qwjx7cCy3YRwvuhRbsowX3Qgv20YJ7oQX7aMG90IJ9tOBeaME+Wqg40tLSlJSUZLHszJkz+vnnn8vpiK5dRX9sX2/fK9wFLdACCtEDPbgdEwBUEKtWrTLdfffdVn/uvPNOU/fu3Ys9tijt27c3HT582Gr5pk2bTC+++KLV8iZNmri0fVf2OWjQINPevXtd2pYzx3P48GFT+/btHe67qPWKOz40NNR07733Wv3ZtGmT3eO3t07dunVN27Ztc/p4KipaoIWi1qEFWnCEFioneqCHotZxlx5ogRaKWocWaMERWqh8aIEWilqHFmjBEVqofGiBFopahxZowRFagMlkMm3dutX07LPPWizbsWOH6ZlnnrFYduzYMdPjjz9uuvfee02tW7c2Pfvss6bs7GyTyWQ8/nbs2HFNx+FOj+0rNWzY0GpZSXyvgOtogRZQiB7owd14l/eEZwBwVr9+/dSvXz+r5T/++KOmTZtW7LEFdu/erejoaMXExDh1PGlpaTp58qRTY4sjNDRUfn5+FsuSkpI0evRoq7GzZs3Sp59+KkkaMGCAJkyYYHe7Q4YM0ZAhQxQSEuLUcXTq1EmZmZnm20eOHFF0dLR69uxpc/yUKVO0ceNG8+2MjAwNGzZMERERNsd/+OGHiouLU4sWLVS/fn1t2bJFISEhql69ut1j+vDDD20uHzFihDw9K//F+2mBFq5cxxZaoAVaMLhLCxI9SPRw5Tq2uEsPtEALV65jCy3QAi0YaIEWaMFAC7RACwZaoAVaMNACLdCCwV1acNbPP/+sgQMHSpIuX76svLw8BQYGSpJGjhyp5s2bW60zcuRIDR48WE8++aQkaebMmZoyZYreeuutEjsud3lsz5gxQ3v37pUknT59Wt26dTPvNywszKnjmzVrloKDg506H9hHC47RgnuhB8fooXJjwjGACmnu3LkaPXq0fH19lZubq2rVql3zWJPJJJPJ5PQxrF+/XgkJCUpLS9Pq1au1bNkySdLx48eLXPfNN99UdHS0br75ZklSnz59rH7AL1y4UE2aNLFY9swzz1ht67PPPlNCQoKSkpLk4eGh3r17a9WqVerfv7/T5+LI9u3bLW537dpVzZo1szs+MjJSkZGR5tsxMTFKSUlxuI9Vq1ZpwIABql+/vubOnavg4GB17NhReXl5Lh3r5cuX5ePj49I6FR0tFKKFQrRAC7RgcMcWJHq4Ej0UcsceaKEQLRSiBVqgBQMt0AItGGiBFmjBQAu0QAsGWqAFWjC4YwuOtGzZUsnJydq7d682bdqkCxcu6J577tGAAQPk4+OjuLg4q3XS0tLUtm1b8+22bdtq6dKlTu+Tx3ahsWPHasSIEcrIyFCLFi00Z84c1a9f3+H3qwJHjhyRn5+f1qxZw6SyEkALjtGCe6EHx+ihcuNtaQAqpNWrV+vixYuSJB8fHwUFBV3z2OPHj+vYsWNO7X/hwoXKzc1VVFSUevfurUGDBik5OVnJycnmH+6OZGRkaPLkyYqLi1NcXJzNdxN1795dQUFBFn+2bt1qNe6TTz5RZGSkfHx85O3trenTp+ujjz5y6jxcdfbsWR06dEgtW7Ys0e36+vqa/40uXrwoPz8/vfPOO3r//fdd2s6ff/5p9W6tyo4WCtFCIVqgBVowuGMLEj1ciR4KuWMPtFCIFgrRAi3QgoEWaIEWDLRAC7RgoAVaoAUDLdACLRjcsYWibNy4UbNmzdLAgQP1yiuvKDMz02IC18aNGxUYGGieYDV79mz17dtX06ZN08SJEzVu3Di9/vrrTu+Px3ahGjVqqE6dOpo/f746deqkKVOmqFatWvL393e43v79+zVlyhQtX75cd999t6ZOnWruA8VHC86hBfdAD86hh8qHKxwDuO6lpqZq5cqVFstOnjypqKgoi3enzJ49WwMHDnR67ODBg9WoUSPzsl27dunHH39UVlaWatSoYfNYLl26pNGjRys9PV3r1q1TjRo15O/vr/bt22vTpk269dZbnTqnjIwM1alTx+79tt7tZE9qaqruvPNO8+3mzZsrNTXV6fUL5OfnKycnR/n5+XbHzJo1SyNHjlRERIROnz6trKwsqzELFy5UfHy8+fbhw4fVtWtXh/v29fVVTk6OpMInQL799lurY1m0aJEWLVpkdzspKSlKSkqSv7+/EhMTHe6zIqIFx2ihEC0YaMFAC5W3BYkeikIPhSp7D7TgGC0UogUDLRhogRZowUALtEALBlqgBVow0AIt0IKBFipvC8Wxc+dOPfXUU/rrX/8qSXr++ef13nvvme/v0aOHYmJizLc7dOig2NhYJScny8vLS1OnTlXVqlUdPi6uxGO7UFpamsLDw1WvXj1t27ZN8+bNU/v27TV+/Hh1797davz69eu1ceNG1atXT4sWLZKvr68GDRqkhIQEDRo0SK1bt9ZTTz2lxo0bu/gVgEQLEi2gED3Qg7tiwjGA617NmjUtPlZAktXtAiaTyemxVz7JcerUKe3Zs0fh4eGKiorS5MmTba7j4+OjsWPH6q677jIv69Wrl7p16yZvb+NbqjPvQMrNzVVERITeeOMNtW7dWpGRkapdu3aR69ly44036uzZs+YneDIzM3XDDTe4vJ0DBw6oS5cuOnDggM37N23apJ07dyo+Pl6HDx9Wbm6ujh8/rpkzZ1qM27dvnwYMGKAHHnjAvMzeu6Bzc3PVrl07/fHHH/r8888VGRmp1NRUBQcH629/+5tiYmIs3nU9cuRIjRw50ny7WbNm+u233yRJTZs2VWZmpsvnXZHQgmO0QAu20AItVHb04Bg9uE8PtOAYLdCCLbRAC5UZLThGC7RgCy3QQmVGC47RAi3YQgu0AEvDhg3TgAED9N1336lmzZr6+uuvNWbMGJtjH3/8cZ08eVLe3t66cOGCvL29VbNmTZlMJg0cONCp/fHYNphMJkVGRqpv377miXvh4eHq0aOHvv/+e5vr1KhRQzNnzlSDBg0kSUuWLNGjjz6qtm3bas2aNfrmm2/svhkCRaMFWkAheqAHd8WEYwDXvVq1aikkJESStHfvXi1dulSHDh2Sh4eHbr/9doWGhuqee+4xj2/SpInTYyXp8uXLevrpp/X222/rwQcfVIcOHRQcHKwuXbrYPJ6CJz+CgoKUl5dndf8vv/yi4cOHOzyngncxXb58WXPmzNGIESO0du1aLV68WIsXL7YYe+jQIdWvX1/Vq1c3L/P29lZCQoIkKSQkRGvXrtWoUaMkSZ999pk6dOjgcP+2BAYGKi4uzvy1vtLy5cs1b948bdu2TT4+Prrjjjskye7HEQQEBDh8Z1WBKlWqFPnO6D59+hR98G6CFmgBBlqgBRSiB3qAgRZoAQZaoAUYaIEWYKAFWoCBFmgBBlqgBRRfy5YttW/fPiUlJenChQt67rnnzJOWGjZsqE6dOpnHrl+/3vz36dOnq1GjRho4cKB+/vlnSdK6deuK3B+PbYOHh4c++OAD8+1JkyZpyJAhuuOOO8z7uNqV/xaS9MUXXyg4OFgNGzaUh4eHOnbsWOR+YR8t0AIK0QM9uCsmHAOoMDZv3qzIyEhFRUXp7rvvVn5+vhITEzVkyBC99957at++vctjz507p759+6pnz57mH4Cff/65OnfurKysLPXt29fu8dj7j3uzZs2cPicvLy+NGTPG/KRKaGioQkNDtXfvXrVp00aenp7q37+/wsLC7L5zfOzYsXrsscd08uRJSVJsbKy2bNnicL+RkZFasGCBcnJylJWVpUmTJtkcd+LECQ0dOlS+vr6Kj49XrVq1nDqv7OxsnTlzRnl5ecrJydG5c+fk7e2tqlWr2l1n69ateuutt5Sdna38/HyZTCY1bdpUU6dOtRr72muv2VzuLmiBFgrQAi3YQgvuiR7ooYC790ALtFCAFmjBFlpwP7RACwVogRZsoQX3Qwu0UIAWaMEWWkBRQkNDzRPDrtarVy+L22fOnDFP6pKks2fPavr06ZKkf//7307vk8c2rke0YI0W3Bc9WKOHyo8JxwAqjNWrV2vy5Mlq166deVmHDh0UHh6utWvXWjwB4uxYf39/TZ061WJc3bp1FRcXJ5PJ5PB4qlevrhYtWlgtd+aHZXp6uurWrStJWrFihe6//36L+4cOHark5GTzxxc4EhAQoNjYWO3Zs0eS8e4dRz9w33rrLZ0/f978g7lmzZo6duyYzbH16tXTa6+95tI7mQIDA7V48WJFR0erSpUq8vPzU0BAgEJCQtS6dWub6xw8eFDh4eHatm2bGjVqZF6emJio7t2767fffpOXl5d5eWxsrCZMmKD4+Hjzst27dzt9jBUdLdhGCwZaoAVaMLhTCxI92EMPBnfqgRZsowUDLdACLRhogRZowUALtEALBlqgBVow0AIt0ILBnVpw1ccff2xzeVxcnFasWGGx7NixY/rggw8UFRWlatWqqW7dulq7dq0kacSIEfL2djxVh8e2YevWrYqIiJBkXNHSZDKpXbt28vDwkKenp8aMGWMxftGiRVq0aJHFssOHD6tfv37y9fU1L/P29i7yquCwjxas0YL7ogdr9FD5MeEYQIXRpk0brVixQh06dDBfYv/s2bNatWqVBg4cWKyxnp6eFk9+FHDmSYwGDRoU+4fN8OHDlZaWJm9vbzVt2lQLFiywO/b555/Xbbfd5nB7VatWdfqHde3atVW7dm2nxnp5ebn8sQlhYWEKCwuzeV9KSorN5QVPNnl6elrt35GGDRua/37lEyeVHS3YRwu0QAsGWnCvFiR6cIQe3KsHWrCPFmiBFgy0QAu0YKAFWqAFAy3QAi0YaIEWaMFAC+7VQlmwdaXuDz/8sMj1eGwbunbtqq5duzrc3rhx48x/HzlypEaOHOnS8aBs0II1WnBf9GCNHioWJhwDqDBGjx6t/Px8de7cWR4eHpKMd6sMHTpUgwcPLvbY4jp+/LgCAwNt3jd//nyHP0A3bNhQ5PbbtGljPvYrDRs2TGPHjnX2MCuEFi1aaM6cOXrqqaeUnZ0tk8kkk8mkJk2aaNOmTTafCHnooYdsLh8+fLhGjRpVFoddbmjBQAsGWqAFWjC4cwsSPRSgB4M790ALBlow0AIt0IKBFmiBFgy0QAu0YKAFWqAFAy3QAi0Y3LmF0rZr1y67j+2iHn88tlGZ0AJQiB5QGXiYivoMEwBwc9nZ2fLz87N6N7AzQkJCrJatWbPG/DEHZeHcuXOqWbOm0+NdOd/8/HxduHDB/K72kh6P6wst2EcL7oUW7KMF90MP9tGDe6EF+2jBvdCCfbTgXmjBPlpwL7RgHy24F1qwjxbcCy3YRwsVW15eni5dumTxsezO4rF9beMl148fpYcW7KMF90MP9tFDxceEYwAAAAAAAAAAAAAAAAAAAAB2uf72QQAA4JaSk5OVk5Pj9PiEhIRSPBrXuXo8rpxvTk6OkpOTnd62q+Ol0j1+uIYW7KMF90MP9tGDe6EF+2jBvdCCfbTgXmjBPlpwL7RgHy24F1qwjxbcCy3YRwu4HvGYBwy0ABSiB0iSTAAAoEJZtWqV6e6777b6c+edd5q6d+9e7LFFad++venw4cNWyzdt2mR68cUXrZY3adLEpe27ss9BgwaZ9u7d69K2nDmew4cPm9q3b+9w30WtVxLjGzZsaLXMmeP//fffi3X8FRUt0II97taCyUQPJhM92ONuPdACLdhDC7TgDFqofGiBFuyhBVpwBi1UPrRAC/bQAi04gxZQ0ubPn2+aO3duiW6Tx3whHvMVBy20d/pYaKHyo4f2Th8LPZQf7/Ke8AwAAFzTr18/9evXz2r5jz/+qGnTphV7bIHdu3crOjpaMTExTh1PWlqaTp486dTY4ggNDZWfn5/FsqSkJI0ePdpq7KxZs/Tpp59KkgYMGKAJEybY3e6QIUM0ZMgQhYSEOHUcnTp1UmZmpvn2kSNHFB0drZ49e9ocP2XKFG3cuNF8OyMjQ8OGDVNERITN8TNmzNDevXslSadPn1a3bt3M+w0LC7N5PMePH1eVKlUkSX379tUzzzzj1LlUFrRACwXL3b0FiR4keihY7u490AItFCynBVqgBVqQaEGihYLltEALtEALEi1ItFCwnBZogRZowVnTp0+Xr6+vXnnlFav7jh49ql69ejlcf9OmTWrQoIEkKTMz0+Lxkp6eLklasWKFeVlcXJxq1ap1TcfMY57HfGmghUK0AHooRA/uiQnHAABUYHPnztXo0aPl6+ur3NxcVatW7ZrHmkwmmUwmp49h/fr1SkhIUFpamlavXq1ly5ZJko4fP17kum+++aaio6N18803S5L69Olj9UvtwoUL1aRJE4tltn7J++yzz5SQkKCkpCR5eHiod+/eWrVqlfr37+/0uTiyfft2i9tdu3ZVs2bN7I6PjIxUZGSk+XZMTIxSUlLsjh87dqxGjBihjIwMtWjRQnPmzFH9+vUd/pt++eWXatq0qfn20aNHiz6RSooWCtGCe7cg0cOV6MG9e6CFQrRAC7RgoAVaoAUDLdACLRhogRZowUALtEALBlpw7xbs+fbbb+Xj42NzUlmjRo2UmJiol156SQ8//LAef/xxSdK6dev03Xffac6cORbja9WqZfHR76NGjZK3t7feeecdp4+Hx3whHvNlixaKjxYqH3ooPnqoHDzL+wAAAEDxrV69WhcvXpQk+fj4KCgo6JrHHj9+XMeOHXNq/wsXLlRubq6ioqLUu3dvDRo0SMnJyUpOTjb/QutIRkaGJk+erLi4OMXFxdl8B1337t0VFBRk8Wfr1q1W4z755BNFRkbKx8dH3t7emj59uj766COnzsNVZ8+e1aFDh9SyZcsS22aNGjVUp04dzZ8/X506ddKUKVNUq1Yt+fv7l9g+KjNaKEQLoIdC9ODeaKEQLbg3WihEC+6NFgrRgnujhUK04N5ooRAtuDdaKEQLuFJ+fr7Gjx+vPn36qFu3bpo4caIuX75sc6y/v7+ysrLMt7Ozs1W9enWH29+5c6eSk5O1d+9e7dy50+nj4jFfiMd82aCFkkULFRs9lCx6qLi4wjEAABVEamqqVq5cabHs5MmTioqKsnhH1uzZszVw4ECnxw4ePFiNGjUyL9u1a5d+/PFHZWVlqUaNGjaP5dKlSxo9erTS09O1bt061ahRQ/7+/mrfvr02bdqkW2+91alzysjIUJ06dezeHxcX59R2JOPrc+edd5pvN2/eXKmpqU6vXyA/P185OTnKz8+3O2bWrFkaOXKkIiIidPr0aYv/LBRYuHCh4uPjzbcPHz6srl272t1mWlqawsPDVa9ePW3btk3z5s1T+/btNX78eHXv3t3p4//jjz+0YcMG/fHHH06vU9HQgmO0YHCHFiR6KAo9GNyhB1pwjBYMtEALtGCgBVqgBQMt0AItGGiBFmjBQAu0QAsGd2jBkdzcXMXGxuq9997ToEGDNGjQIEnGFR+7d++ukSNH6pFHHrHooHr16hb/hllZWXYnlWVkZGjOnDlKTEzUunXrZDKZNGjQILVq1Urh4eEWLdlbn8e8gcd86aIFWkAheqAHWGLCMQAAFUTNmjXVtm1bi2VX3y5gMpmcHnvlE36nTp3Snj17FB4erqioKE2ePNnmOj4+Pho7dqzuuusu87JevXqpW7du8vY2fr14/fXXizyn3NxcRURE6I033lDr1q0VGRmp2rVrF7meLTfeeKPOnj1r/kU+MzNTN9xwg8vbOXDggLp06aIDBw7YvH/Tpk3auXOn4uPjdfjwYeXm5ur48eOaOXOmxbh9+/ZpwIABeuCBB8zL/Pz8bG7TZDIpMjJSffv2VY8ePRQTE6Pw8HD16NFD33//vUvHf+HCBR08eFAXLlxwab2KhBYcowWDO7Qg0UNR6MHgDj3QgmO0YKAFS7RAC7RgoAVaoAUDLdACLRhogRZowUALtFCZW3Bk+fLlSk1N1T//+U/Vr1/fvPzJJ59Ux44dtWDBAp0+fVpDhw7Vnj179MILLyg9PV2StGTJEklSenq6PDw8tHz5cn344Ydq06aN+f6lS5fq+eef16xZs+TpaXwg+fbt2/Xpp5+qb9++GjBggMaOHWv3+HjMG3jMlz5aoAUUogd6gCUmHAMAUEHUqlVLISEhkqS9e/dq6dKlOnTokDw8PHT77bcrNDRU99xzj3l8kyZNnB4rSZcvX9bTTz+tt99+Ww8++KA6dOig4OBgdenSxebxFDwRGBQUpLy8PKv7f/nlFw0fPtzhOcXExJj3PWfOHI0YMUJr167V4sWLtXjxYouxhw4dUv369S3e+eft7a2EhARJUkhIiNauXatRo0ZJMt5R2KFDB4f7tyUwMFBxcXHmr/WVli9frnnz5mnbtm3y8fHRHXfcIUl2P4IjICDA4bsJC3h4eOiDDz4w3540aZKGDBmiO+64w7wPZzVu3Fjjx4/XV1995dJ6FQkt0IIz3KEFiR7owTnu0AMt0IIzaIEWaMFAC7RACwZaoAVaMNACLdCCgRZogRYM7tCCI88++6zd+2rXrm0xib5du3ZKTk423543b568vb0VFhZmc/3hw4fbfEx7enpaXDHTER7zBh7zpY8WaAGF6IEeYIkJxwAAVDCbN29WZGSkoqKidPfddys/P1+JiYkaMmSI3nvvPbVv397lsefOnVPfvn3Vs2dP8y99n3/+uTp37qysrCz17dvX7vEkJibaXN6sWTOnz8nLy0tjxowxP8EYGhqq0NBQ7d27V23atJGnp6f69++vsLAwu1dRGDt2rB577DGdPHlSkhQbG6stW7Y43G9kZKQWLFignJwcZWVladKkSTbHnThxQkOHDpWvr6/i4+NVq1Ytp84rOztbZ86cUV5ennJycnTu3Dl5e3uratWqTq1flPfff18BAQHKzc3VmTNnFB4eXiLbrShogRYKuHsLEj3QQyF374EWaKEALdCCLbRAC7RgoAVaoAUDLdACLRhogRZowUAL7tdCRcZjvnh4zFc+tFA8tFA50UPx0IPrmHAMAEAFs3r1ak2ePFnt2rUzL+vQoYPCw8O1du1aiycDnR3r7++vqVOnWoyrW7eu4uLiZDKZHB5P9erV1aJFC6vlzvyCmJ6errp160qSVqxYofvvv9/i/qFDhyo5Odn8kR2OBAQEKDY2Vnv27JFkvGPN0S+Zb731ls6fP2/+ZbRmzZo6duyYzbH16tXTa6+95tK79wIDA7V48WJFR0erSpUq8vPzU0BAgEJCQtS6dWuLsVu3blVERIQk4513JpNJ7dq1k4eHhzw9PTVmzBir7c+YMUOnT5+Wt7e3fH19VbNmTfNHrLgLWrCNFtyvBYke7KEH9+uBFmyjBVqQaEGiBVow0AIt0IKBFmiBFgy0QAu0YKAFWnDHFq6WmJio5557zqmx0dHRVmPT09MlFV5pskBMTIzy8vKc3nZMTIwCAwNt3sdj3sBjvnTRAi2gED3QA6wx4RgAgAqmTZs2WrFihTp06GD+WImzZ89q1apVGjhwYLHGenp6WjwRWMCZJ/QaNGhg9woERRk+fLjS0tLk7e2tpk2basGCBXbHPv/887rtttscbq9q1apO/4Jau3Zt1a5d26mxXl5eLn9USFhYmN2PRklJSbG43bVrV3Xt2tXh9saNG2dxu02bNlZjjh496tIxVnS0YB8tuFcLEj04Qg/u1QMt2EcLtEALBlqgBVow0AIt0IKBFmiBFgy0QAu0YKAF92rhakFBQUpOTnZ6vCtjizPeFh7zBh7zpYsWHKMF90IPjtGDe2LCMQAAFczo0aOVn5+vzp07y8PDQ5LxDq2hQ4dq8ODBxR5bXMePH7f7brr58+c7/KVxw4YNRW6/TZs25mO/0rBhwzR27FhnDxOVEC0YaAESPRSgB9CCgRZACwZaAC0YaAG0YKAF0IKBFkALBlpARcRjHjDQAlCIHlDWPExFfYYJAACApOzsbPn5+RXrIyRCQkKslq1Zs8b80R5l4dy5c6pZs6bT41053/z8fF24cMF8hYeSHi+V7vHDNbRgHy24H3qwjx7cCy3YRwvuhRbsowX3Qgv20YJ7oQX7aMG90IJ9tOBeaME+WsDVeMxf23iJx3xlQQvXNl6ihcqEHq5tvEQPJYUJxwAAAAAAAAAAAAAAAAAAAADsYvo1AAAA4ILk5GTl5OQ4PT4hIaEUj8Z1rh6PK+ebk5Oj5ORkp7ft6njp+vt6ujt6sI8e3Ast2EcL7oUW7KMF90IL9tGCe6EF+2jBvdCCfbTgXmjBPlpAeePxARhoATDQAhxhwjEAAAAqpNWrVyswMNDqT/PmzdWjR49ijy1KWFiYTp48abV88+bNCgsLs1rev39/l7ZvS0hIiFJSUqyWDx482OX/8DlzPCkpKeaP5bF3vracPHnS5teguOMbNWpktawkvp6VET3QAwy0QAsw0AItwEALtAADLdACDLRACzDQAi0AV7P1+Pjoo4/04IMPql27dmrXrp0eeughrVy5shyODig7tAAYaAGOeJf3AQAAAADF0a9fP/Xr189q+Y8//qhp06YVe2yB3bt3Kzo6WjExMU4dT1pamtNPIBdHaGio/Pz8LJYlJSVp9OjRVmNnzZqlTz/9VJI0YMAATZgwwe52hwwZoiFDhpifCC9Kp06dlJmZab595MgRRUdHq2fPnjbHT5kyRRs3bjTfzsjI0LBhwxQREWFz/IwZM7R3715J0unTp9WtWzfzfp150r1Tp06aNWuWgoODnTqfyoIe6MHe8blbD7RAC/aOjxYMtEALtGCgBVqgBQMt0AItGGiBFmjBQAu04G4tuJP169dbNJufn68///xTv/76q83xn3/+uVauXKnNmzerVq1akqQzZ86ob9++qlGjhrp3714Whw2UOFoADLSAa8GEYwAAAFR4c+fO1ejRo+Xr66vc3FxVq1btmseaTCaZTCanj2H9+vVKSEhQWlqaVq9erWXLlkmSjh8/XuS6b775pqKjo3XzzTdLkvr06WP1hPfChQvVpEkTi2XPPPOM1bY+++wzJSQkKCkpSR4eHurdu7dWrVpVYlew2L59u8Xtrl27qlmzZnbHR0ZGKjIy0nw7JibG5lVFCowdO1YjRoxQRkaGWrRooTlz5qh+/foO/00LHDlyRH5+flqzZo1bPzFOD4Xowb17oIVCtEALtGCgBVqgBQMt0AItGGiBFmjBQAu0QAsGWnDvFiq7xx9/XI8//rj59r///W+99tprdsfv27dP/fv3N08qk6QbbrhB/fr10759+5hYhgqLFgADLeBaeJb3AQAAAADXavXq1bp48aIkycfHR0FBQdc89vjx4zp27JhT+1+4cKFyc3MVFRWl3r17a9CgQUpOTlZycrL5yW5HMjIyNHnyZMXFxSkuLs7m1TW6d++uoKAgiz9bt261GvfJJ58oMjJSPj4+8vb21vTp0/XRRx85dR6uOnv2rA4dOqSWLVuW2DZr1KihOnXqaP78+erUqZOmTJmiWrVqyd/f3+F6+/fv15QpU7R8+XLdfffdmjp1qvnf2d3QQyF6cO8eaKEQLdACLRhogRZowUALtEALBlqgBVow0AIt0IKBFty7BXezYcMGPfroo3bv7969u+bPn6+dO3fq/PnzOn/+vOLj4/XOO++Yr54NVAa0ABhoAa7gCscAAACoUFJTU7Vy5UqLZSdPnlRUVJTF1Rpmz56tgQMHOj128ODBatSokXnZrl279OOPPyorK0s1atSweSyXLl3S6NGjlZ6ernXr1qlGjRry9/dX+/bttWnTJt16661OnVNGRobq1Klj9/64uDintiMZX58777zTfLt58+ZKTU11ev0C+fn5ysnJUX5+vt0xs2bN0siRIxUREaHTp08rKyvLaszChQsVHx9vvn348GF17drV7jbT0tIUHh6uevXqadu2bZo3b57at2+v8ePH23x37Pr167Vx40bVq1dPixYtkq+vrwYNGqSEhAQNGjRIrVu31lNPPaXGjRu7+BWoGOjBMXpwnx5owTFaoAVaMNACLdCCgRZogRYMtEALtGCgBVqgBQMtuE8L7i4zM1OrVq3S3r177Y558MEHtXjxYi1evFhDhw6Vh4eH2rdvr2XLlum+++4rw6MFSg8tAAZagKuYcAwAAIAKpWbNmmrbtq3FsqtvFzCZTE6PvfLJ71OnTmnPnj0KDw9XVFSUJk+ebHMdHx8fjR07VnfddZd5Wa9evdStWzd5exu/ar/++utFnlNubq4iIiL0xhtvqHXr1oqMjFTt2rWLXM+WG2+8UWfPnjU/8Z+ZmakbbrjB5e0cOHBAXbp00YEDB2zev2nTJu3cuVPx8fE6fPiwcnNzdfz4cc2cOdNi3L59+zRgwAA98MAD5mV+fn42t2kymRQZGam+ffuqR48eiomJUXh4uHr06KHvv//e5jo1atTQzJkz1aBBA0nSkiVL9Oijj6pt27Zas2aNvvnmG7svbFQG9OAYPbhPD7TgGC3Qgi20QAu0YKAFWqAFAy3QAi0YaIEWaMFAC7RQmVtwZ7m5uRo8eLCmTJlS5L9v27Zt1bZtW82bN0/e3t4KCwsrm4MEygAtAAZaQHEw4RgAAAAVSq1atRQSEiJJ2rt3r5YuXapDhw7Jw8NDt99+u0JDQ3XPPfeYxzdp0sTpsZJ0+fJlPf3003r77bf14IMPqkOHDgoODlaXLl1sHk/Bk+JBQUHKy8uzuv+XX37R8OHDHZ5TTEyMed9z5szRiBEjtHbtWvM7Ra906NAh1a9fX9WrVzcv8/b2VkJCgiQpJCREa9eu1ahRoyRJn332mTp06OBw/7YEBgYqLi7O/LW+0vLlyzVv3jxt27ZNPj4+uuOOOyTJ7sfzBQQEOLzSSAEPDw998MEH5tuTJk3SkCFDdMcdd5j3cbVOnTpZ3P7iiy8UHByshg0bysPDQx07dixyvxUZPdDDldy5B1qghSvRQogkWihAC4VogRZowUALtEALBlqgBVow0AIt0ILBnVpwV+fOnVPv3r3VoUMHPfnkkzbHJCUlacSIETbvW7FihcXtpUuX6u677y7x4wRKGy0ABlpAcTHhGAAAABXS5s2bFRkZqaioKN19993Kz89XYmKihgwZovfee0/t27d3eey5c+fUt29f9ezZ0/yE8Oeff67OnTsrKytLffv2tXs8iYmJNpc3a9bM6XPy8vLSmDFjzE+2h4aGKjQ0VHv37lWbNm3k6emp/v37KywszO4VRcaOHavHHntMJ0+elCTFxsZqy5YtDvcbGRmpBQsWKCcnR1lZWZo0aZLNcSdOnNDQoUPl6+ur+Ph41apVy6nzys7O1pkzZ5SXl6ecnBydO3dO3t7eqlq1qlPro2j0QA8w0AItwEALtAADLdACDLRACzDQAi3AQAu0APezfPlyRUREaOrUqXr66aftjrv33nuVmJioY8eOyc/Pr1hX+QauZ7QAGGgB14IJxwAAAKiQVq9ercmTJ6tdu3bmZR06dFB4eLjWrl1r8cS4s2P9/f01depUi3F169ZVXFycTCaTw+OpXr26WrRoYbXcmSeP09PTVbduXUnGu0Hvv/9+i/uHDh2q5ORk88f5ORIQEKDY2Fjt2bNHknE1C0dPQL/11ls6f/68+YnqmjVr6tixYzbH1qtXT6+99ppLV/YIDAzU4sWLFR0drSpVqsjPz08BAQEKCQlR69atLcZu3bpVERERkoyrcphMJrVr104eHh7y9PTUmDFjLMYvWrRIixYtslh2+PBh9evXT76+vuZl3t7edl+4qCzowTZ6cL8eaME2WqAFiRYkWqAFAy3QAi0YaIEWaMFAC7RACwZaoAV3bMGd/PWvf1V8fLwaNWrk1Pj33ntPgYGB6t+/fykfGVC2aAEw0AKuBROOAQAAUCG1adNGK1asUIcOHcwfOXf27FmtWrVKAwcOLNZYT09PiyfFCzjz5HaDBg2K/eTr8OHDlZaWJm9vbzVt2lQLFiywO/b555/Xbbfd5nB7VatWdfrJ69q1a6t27dpOjfXy8nL5YwTDwsIUFhZm876UlBSL2127dlXXrl0dbm/cuHHmv48cOVIjR4506XgqK3qwjx7cCy3YRwvuhRbsowX3Qgv20YJ7oQX7aMG90IJ9tOBeaME+WkBldfUkdcBd0QJgoAVcCyYcAwAAoEIaPXq08vPz1blzZ3l4eEgyrt4wdOhQDR48uNhji+v48eMKDAy0ed/8+fMdPqG8YcOGIrffpk0b87FfadiwYRo7dqyzh4lKih4M9ABaMNACaMFAC6AFAy2AFgy0AFow0AJowUALgGPjx4/X7NmzrZYHBQUpOjq6HI4IKB+0ABhoAQU8TEV9hgkAAAAAs+zsbPn5+cnT09PldUNCQqyWrVmzxvyxf2Xh3LlzqlmzptPjXTnf/Px8XbhwwXy1k5IeL7l+/Chd9GAfPbgXWrCPFtwLLdhHC+6FFuyjBfdCC/bRgnuhBftowb3Qgn20gPLG4wMw0AJgoAU4woRjAAAAAAAAAAAAAAAAAAAAAHa5/vZBAAAAAAAAAAAAAAAAAJXS//3f/2n16tUWy44cOaJNmzaV0xEB5YMWgEIJCQkujU9OTlZOTo5TY3NycpScnOz0tl0dL0lTp051aTxsY8IxAAAAAJSxrKws7d6922r5/v37S3W/rj4RcK1SUlJ08uTJUht/8OBBZWZmFuPISocrT5xIrp+vq/9+ro5/5513lJGR4dI614oWSmY8LZTseFooPbTgGC3QQkmNp4WSHU8LpYcWHKMFWiip8bRQsuNpofTQgmO0gGvVpk0bBQYG2vzTrl07SdKlS5eUkpKilJQUff/991qzZo359pkzZ3T48GGtW7eunM8EuDa0gOtZVFSU5s2bV6LbDAkJUUpKitXywYMHu/zzv3///kWOSUlJUUhIiCQpLCzM6d9fTp48qbCwMKePxdb4ESNGKCgoyOrP5s2bJUkfffSR1XbsrVOvXj19/fXXTh+PO2HCMQAAAACUsWPHjmn69OlWyx9//PFS3a8zTwRcKSEhQb169XI45tFHHzU/GdeyZUvzkwiSFBMTo6+++srp/dkbn52drcDAQKvls2fPdvndy1eLiIhQTEyM1fLo6GjNnj3bpW05+8RJ06ZNJbn+9XH13+/q8YsXL7b5pMm0adMkSevXr9e5c+ecWue2227TxIkTXToeW2jBNlpwjBZKDi1YowVacAYt0AItGGiBFmjBQAu0QAsGWqCF0m4B12bfvn0aNmyYIiMjlZycrOTkZEVERCg0NFR79uyRJKWnp2v8+PEaP368NmzYIG9vb/PtgsffiRMntH37dp06dao8TwcoNlrAtZo+fbreeustm/cdPXrU5s/CK/+cOHHCPD4zM9Ni0vvcuXM1f/58i2Ul8Yap0NBQ9erVy+JPfHy8zbGzZs1Sq1at1KpVK82aNcvhdocMGaK4uDinj6NTp04WX4u6devqiy++sDt+ypQpFl+Lxo0bKyIiwu74Dz/8UPPmzdOWLVuUmJioadOmKT4+Xt26dXO4TmJiotWfxx9/XJ6eTK21xbu8DwAAAAAA3MWIESOUlJSknJwc/f777woKCpIkvffee7r//vuLXH/69OmqWrWqXn31VbtjmjZtavFO5T/++MP89/z8fJeOd9u2bUpISFBWVpZq1Khhc8yXX35p/ntKSoqGDBmimJgYbd68WT///LPGjRtnc71mzZrpt99+c+o40tLSXLqCi6veffddbdiwwWLZkSNH1K9fP6uxX375pSZNmqS8vDwFBgZqwYIF8vf3t7ndmJgYpaSkOHzy40qvvfaaYmNjzbczMjLUs2dPzZ8/3+b4jRs3asqUKebbubm5qlevnt0nd0JDQxUSEqLz58+rdevW2r9/v/z9/XX77bfbPabQ0FCFhoZaLf/000/1n//8x6nzsoUWCtGCNVqghaLQAi1cjRZKBy3QQlFogRZowUALtEALBlqghdJqASUnJydHR48eNd/+/ffflZeXZ7598803a9WqVYqJidGaNWvME/gnTJigm266SXFxcTp58qTi4uLUoEED1atXrzxOA7hmtIBr8e2338rHx0evvPKK1X2NGjVSYmKiXnrpJT388MPmN4itW7dO3333nebMmWMxvlatWhZviBo1apS8vb31zjvvOH08b775pqKjo3XzzTdLkvr06aPRo0dbjFm4cKGaNGliseyZZ56x2tZnn32mhIQEJSUlycPDQ71799aqVatcfiOTPdu3b7e43bVrVzVr1szu+MjISEVGRppvF/y+5MiqVas0YMAA1a9fX3PnzlVwcLA6duxo0bgzLl++LB8fH5fWcRdMOAYAAACAMvLhhx9KMt5B/+KLL7r0UUWXLl3STz/9JEnKy8uTt3fR/527ePGixccJOfsxhyaTSUuXLlVsbKzefPNNPfHEE1q2bJluueUWq7Hx8fH6888/JRkv6EjG1Wratm2r999/3+E+nLV+/Xr98ccf+vrrr+Xv768XXnhBkpSamqohQ4Y4XPe7775Tt27d1LJlS0nGC1TR0dEWY0aPHq2nn37aYtmyZcusvl5Hjx7VK6+8oh07duimm27S3LlzFR4erkWLFjl9Lo5c/UTTnDlz7L4IJUk9evRQjx49zLcLXqhz5IcfftB///tftW7dWl988YVatmypqKgo7du3T//973+dPtZrfaKFFiz34SxasI0WaIEWDLRAC7RgoAVaoAUDLdACLRhogRZowVCRW8C1O3XqlB599FGdOnVKJpNJy5Ytk2R8HL2Xl5c++eQTbdu2TbVr19bKlSu1ZcsWLVmyRDfccIO2bNmi3r1769tvv5UktW7d2uZV14GKgBZwLfLz8zVx4kT16dNHly9f1sSJE/XGG2/Iy8vLaqy/v7+ysrLMt7Ozs1W9enWH29+5c6eSk5N16dIl7dy5Uw8//LBTx5WRkaHJkyfrqaeesjume/fuqlKlisWy1NRUjRkzxmLZJ598osjISPPP7enTp2vcuHElNuH4SmfPntWhQ4fMv3eVFF9fX128eFGS8futn5+f3nnnHZlMJpfO488//5Sfn1+JHltlwYRjAAAAAChjixYt0k8//aRvv/1WzZs31z/+8Q9JsvgYpSulpaVp4sSJev3112UymTRy5EhNnz5dN910k8P9+Pj4aPz48ebbO3bscDj+yJEj2rhxoz799FMFBwfr66+/VrVq1dSsWTMNGDBALVq00N///ncNGDDAvM4zzzxj8XGZffr0UfXq1eXp6Wn3P+JpaWk6evSoLl++bPOJmCt99913io6O1vbt2/XCCy9oxYoV5nd7F/WiiGQ80dK9e3ctXbrU7piIiAi9++67VusVvCBVYM2aNRo5cqT56/7yyy+rWbNmMplM8vDwKPJYXLVx40Z99NFHJbpNW0+0jB49Wk899ZTV+TpSUk+00AItOIMWrNECLZQUWjDQgm20QAtXowXL9WjBPlqgBVow0AIt0IKBCTvlr169ekpMTHRq7H//+1917tzZfKXMrl27WlzFc/PmzQoKCtKbb76pv//976VyvEBpoQUUR25urmJjY/Xee+9p0KBBGjRokCTjasDdu3fXyJEj9cgjj6hatWrmdapXr24x4TgrK8vuhOOMjAzNmTNHiYmJWrdunUwmkwYNGqRWrVopPDxcjRo1cnh8GRkZqlOnjt377X2agS2pqam68847zbebN2+u1NRUp9cvkJ+fr5ycHIefmDFr1iyNHDlSEREROn36tMXXq8DChQsVHx9vvn348GF17drV4b59fX2Vk5MjqfD3l2+//dbqWBYtWuTwjWApKSlKSkqSv7+/09833AUTjgEAAACgjFy+fFmTJk3S6dOntXPnTg0cOFAzZ840/0e1adOmFuO///57rVq1SqdOndKMGTPUuHFjSdK0adM0ceJEBQQEaODAgQoODra5v7y8PIursNj6z/qVcnNzdfHiRa1atcq8L0lq166ddu/ere+++87mi1lRUVEWt+fPn69169YpNTXV4qOOCuzatUuStHv3brVv397u8SxcuFAxMTH64osvdOedd2rt2rUaNmyYxo0bpyeeeMLhuRQo6omWiIgIpz+qMjU11fyiniR5eXnp5ptvVnp6ussfmZaTk+Pw45u2bdumOnXqKCEhwfz1vfpKOfHx8Vq4cKH59vnz54vc75VPtOTk5MjPz0///ve/deTIEZ09e9Y8LjExUc8995zd7RR8xOonn3yimJgYBQYGFrnvK9GCgRZogRYMtEALtGCgBVqgBQMt0AItGGiBFmjBQAu0gGtj698nIyNDHh4euuGGGyyWx8TEaPjw4erRo4e+/PJL1apVS99//70mTJhgHtOtWzfFxMSUxaEDJYoWUFzLly9Xamqq/vnPf6p+/frm5U8++aQ6duyoBQsW6PTp0xo6dKj27NmjF154Qenp6ZKkJUuWSJLS09Pl4eGh5cuX68MPP1SbNm3M9y9dulTPP/+8Zs2aJU9PT0nS9u3b9emnn6pv374aMGCAxo4da/f4cnNzFRERoTfeeEOtW7dWZGSkateuXaxzvfHGG3X27Fnz5OnMzEyrPpxx4MABdenSRQcOHLB5/6ZNm7Rz507Fx8fr8OHDys3N1fHjxzVz5kyLcfv27dOAAQP0wAMPmJfZexNTbm6u2rVrpz/++EOff/65IiMjlZqaquDgYP3tb39TTEyMxSdqjBw5UiNHjjTfbtasmX777TdJxu/ZmZmZLp+3u2DCMQAAAACUgfz8fPXr10/BwcFav369vLy8tGPHDr366qvy8fGx+EjDAlWqVNEzzzyjVq1aSTKuLOPp6an27dtr2bJl+s9//mO1Tm5urpKTk2UymXTx4kU99thjysnJUffu3bVhwwaHx3j77bcrPDzc7v333XefzeVfffWVeX+nT5/WY489ppdeesnmCzD5+fl699139dFHH2nGjBl6+OGH7V7VpVOnTnr22WfNH/PUsmVL7d271/wxmv3799dtt93m8JzOnz+vLVu2aNeuXWrQoIGmTZumv/71rw7XsafgiZYrZWVlqVatWi5vq0uXLkpJSbH5Nfq///s/vfzyy9q8ebOqVKlifhFm/fr1FuMOHz6sxo0ba9y4ceZljj6ecujQodq3b5+ys7O1c+dOpaWladu2bfL391d8fLw2b95sHhsUFGS+ApAkPffccxo8eLBCQkLUq1eva3pxiBYKvw60QAu0QAsFaIEWaMFAC7RACwZaoAVaMNACLdCCwZ1bwLW7+t9Hkt588015e3vr5ZdftrlOYmKiDh48qPPnz+uOO+5QQECAJGOSV4MGDUr7kIFSQQsormeffdbufbVr19bkyZPNt9u1a2fxOJs3b568vb0VFhZmc/3hw4dr+PDhVss9PT0trqbsSMHE98uXL2vOnDkaMWKE1q5dq8WLF2vx4sUWYw8dOqT69etbXG3Z29tbCQkJkqSQkBCtXbtWo0aNkmRcxblDhw5FHsPVAgMDFRcXp5CQEKv7li9frnnz5mnbtm3y8fHRHXfcIUny9/e3ua2AgACHbwgrUKVKlSKvRNynT5+iDx5FYsIxAAAAAJQBT09PrV27VpLx4sXSpUu1Y8cOpaena+nSpfr111+1f/9+i3Xuvvtui9tJSUny9vY2X83lrrvustpPv3799PHHH8vHx0fVqlVTzZo1i3wxpUBQUJDF7QsXLujkyZP6y1/+YrH8hRde0LBhwyRJoaGhio2Nlaenp3x8fFS/fn15e9v/r+Ybb7yhzp07q3///kpKStKkSZM0Y8YMm2Nvv/12Sca7jAue7LjS77//rnXr1umWW26xu78rn6z517/+pW7duunXX3/VgQMHNGLECIux6enpunz5stVHjC5dulR33323QkJC9O6776pfv36SpH//+9+qVauW+YUsV8TFxdl8kWj//v3q16+fli1bpltvvVWS1LBhQ0m2XwTy8/Nz6okWSfrnP//p8P4uXbrYfUKnJNGCgRYMtEALtGCgBVqgBQMt0AItGGiBFmjBQAu0QAsGd24BJefChQsaP3684uPj5ePjIw8PD3388cf6+9//rpkzZ5qvZilJHh4e2rJliz7//HPl5+fLZDIpPz9f9913n9XVJ4GKhhZQWXl5eWnMmDHm3/lCQ0MVGhqqvXv3qk2bNvL09FT//v0VFhamtm3b2tzG2LFj9dhjj+nkyZOSpNjYWG3ZssXhfiMjI7VgwQLl5OQoKytLkyZNsjnuxIkTGjp0qHx9fRUfH+/0m7Cys7N15swZ5eXlKScnR+fOnZO3t7eqVq1qd52tW7fqrbfeUnZ2trndpk2baurUqVZjX3vtNZvLYR8TjgEAAACgjD366KPmFx3q16+v9PR0LV++XP/4xz+0d+9eeXl5FXvb8+fP16ZNm9S9e3eX1736nb+JiYmaPXu2+QUuWyZOnKh9+/YpICBAzZs3d7j98ePH68iRI1q5cqUkac6cORo0aJBefPFFvfPOO3bXW7Rokc3ljj6q0ZbOnTurdu3a+v3333Xvvfear05Qt25d1a5dW4sWLVJmZqbGjx9vc/2HHnpIH3/8sflqQStXrrR6d/jV1q5dq19++UUXL15Udna2xcczXeny5csKCwvTrl27tH79epsvAtqSk5OjM2fOKD8/Xzk5OcrOzlZWVpbVi35XOnjwoCIiIvTbb7/J09NTnp6e8vPzU1hYmNWLTh9//LFatGjh1LEUBy3QwtVogRZowUALtEALBlqgBVow0AIt0IKBFmiBFgzu2gKuzfTp0xUQEKDk5GTzlcJNJpOmTJmiWbNmadq0aeaxS5YsUXJysuLj482T5E0mkz788EO98MIL5i6BiogW4IrExESnf4eIjo62Gpueni6p8CrEBWJiYpSXl+f0th19WkB6errq1q0rSVqxYoXuv/9+i/uHDh2q5ORki8n09gQEBCg2NlZ79uyRJE2aNMnhxN633npL58+fN08Arlmzpo4dO2ZzbL169fTaa6+5dMXkwMBALV68WNHR0apSpYr8/PwUEBCgkJAQtW7d2uY6Bw8eVHh4uLZt26ZGjRqZlycmJqp79+767bffLH5/jo2N1YQJExQfH29etnv3bqeP0R0x4RgAAAAAytCxY8eUnp5u8aRVw4YNNX78eH3xxRc6ePCg7rrrLptPYjh6YuLKJxrGjBlj84WilJSUEjuPK23atEnNmjWzeqHo6iuv9OjRQ23btjU/iefp6amVK1cqNTXV4fY7deqkU6dO2bwCzPPPP+9w3SufaNm/f7/Onj2rxo0bm++PiopSr1691KVLF4fbKbBkyRL98MMPOnHihP71r3/pxhtvtDu2T58+6tChg7y8vFSlShXVqFFDvr6+Nj9u1MvLS3379tXbb7/t8OMsr3TLLbdo3bp1Gjp0qLy9veXr66saNWroL3/5i90Xikwmkx599FGtWLFC7dq1My8/ceKEHn30UTVp0sTiikgHDhxQzZo1NWfOHPNHbC1evLhYH/95NVqgBVow0AIt0IKBFmiBFgy0QAu0YKAFWqAFAy3QAi2gpFStWlV//vmn8vPzzROt8vPzdenSJdWoUcNibG5urnx8fCwmZHl4eKhatWrKzc0t0+MGShotwBVBQUFKTk52erwrY4sz3pbhw4crLS1N3t7eatq0qRYsWGB37PPPP1/kp1lUrVrV6UnBtWvXVu3atZ0a6+Xl5dJkY0kKCwtTWFiYzfvs/a5qMpkkGb87Xr1/Rwo+JUKSxURlWGPCMQAAAACUoQYNGigvL8/q6jHbt29XWlqa+WMnXX0SoyK48oWJAh4eHmrSpEmR627cuFFNmzZ1eZ9LlizRunXrVKNGDVWpUkXr1q2z+1GdHTp0cOpJwnvuucepffv7+7v00ZIPP/yw02MlqWPHjurYsaNL60jGky1XP7FS8MRLwYt4V7vyCaN69eq5vE9baMESLRSiBQMtOEYL1mih4qIFx2jBQAuO0YI1Wqi4aMExWjDQgmO0YK2it4BrN2HCBE2dOlVt27aVl5eXTCaTTCaTOnfurHHjxlmMff755zVz5kw9/PDD8vHxUX5+vvLy8tSyZUu7VxEHKgpaQGWzYcOGIse0adPG5s/tYcOGaezYsaVwVOWnRYsWmjNnjp566illZ2ebG2/SpIk2bdpkc+LxQw89ZHP58OHDNWrUqLI47AqFCccAAAAAUIY8PT21detWTZkyRREREfL09FR+fr5uu+02bdmyRb6+viWyH3sfrTRhwgT169evRPZxpSlTpigqKspqeefOnTV37txr3v6jjz5q88o0gwYN0quvvmp3vYkTJ2rixIkOtz1q1CirKxdIUqtWrfTxxx+7frDXMQ8PD23ZskVTp05VSkqKPDw8zB+FGRERoVatWlmt89JLL1ldZUiSWrdurX/+85/FPhZaKB5aKBm0UIgWrNECLZQkWqgYaKEQLVijBVooSbRQMdBCIVqwRgvl0wKuXZUqVTRr1izNmjWryLFeXl6aPHmyJk+eXAZHBpQtWkBFFRISYrVszZo15k9nsOfgwYMldgwHDhwockzTpk0VFxcnSdq8ebP8/Pyc2nbjxo21efNmp4/F0fhu3bqpW7duTm0nMTHR6X3C4GEquI40AAAAAABXyMzM1K+//qrg4OAy2V9OTo48PT1tviBUlLCwMKsr+bz++uvq3LlzCR1d0bKzs+Xn52f1MU32uHq+586dU82aNZ0+HlfHwz5acA0tVF604BpaqLxowTW0UHnRgmtoofKiBdfQQuVFC66hBQAAAFRETDgGAAAAAAAAAAAAAAAAIElKSEhQ27ZtnR6fnJys5s2bq1q1akWOzcnJ0cGDB+1eXf1ax0vS1KlTNW3aNKfHA/bQAsrLuXPnlJ6erttuu628D8WsovdQmsfvTpx7uxwAAAAAAEAJy8rK0u7du62W79+/v1T3m5CQUKrbv1pKSopOnjxZauMPHjyozMzMYhxZ6UhOTlZOTo7T4109X1f//Vwd7+rX09Xjt4UWSmY8LZTseFooPbTgGC3QQkmNp4WSHU8LpYcWHKMFWiip8bRQsuPLo4XKJioqSvPmzSvRbYaEhCglJcVq+eDBg13+N+7fv3+RY1JSUhQSEiLJuHK4s//GJ0+eVFhYmNPHYmv8iBEjFBQUZPVn8+bNkqSPPvrIajv21qlXr56+/vprp48HJYsWwpw+FlooP2FhYdqwYYPV8g0bNig8PNxiWUREhBo3bqzAwECrPx988IHN7a9YsULTp0+3WPbDDz/ojTfeuOZjd6certSoUSOrZc4c/9GjR4t1/O6ECccAAAAAAKBcHDt2zOpJNEl6/PHHS3W/zjypdKWEhAT16tXL4ZhHH33U/KRhy5YtzU9ISVJMTIy++uorp/dnb3x2drbNd+vPnj3b6qNgXRUREaGYmBir5dHR0Zo9e7ZL23L2SbimTZtKcv3r4+q/n6PxvXr1UmJiosUyZ7+ezZo1k+T68dtCC7bRgmO0UHJowRotFKIF+2iBFmyhBdtooRAtuI4WbKMFxypjC+Vh+vTpeuutt2zed/ToUZsT9q78c+LECfP4zMxMi0lnc+fO1fz58y2WlcSk+NDQUPXq1cviT3x8vM2xs2bNUqtWrdSqVSvNmjXL4XaHDBmiuLg4p4+jU6dOFl+LunXr6osvvrA7fsqUKRZfi8aNGysiIsLu+A8//FDz5s3Tli1blJiYqGnTpik+Pl7dunVzuE5iYqLVn8cff1yenkyfcoQWCtECnDFlyhQlJydb/Rk1apTN8Xl5ecrLyyu143GXHmbMmKFu3bqpW7duOn36tPnvUVFRdo+nZcuW5u3PmDHD6XNxZ97lfQAAAAAAAMC9jBgxQklJScrJydHvv/+uoKAgSdJ7772n+++/v8j1p0+frqpVq+rVV1+1O6Zp06YW79r/448/zH/Pz8936Xi3bdumhIQEZWVlqUaNGjbHfPnll+a/p6SkaMiQIYqJidHmzZv1888/a9y4cTbXa9asmX777TenjiMtLa1U303/7rvvWl2l4ciRI+rXr5/V2C+//FKTJk1SXl6eAgMDtWDBAvn7+9vcbkxMjFJSUhw+EXil1157TbGxsebbGRkZ6tmzp+bPn29z/MaNGzVlyhTz7dzcXNWrV8/uE50bN27U4sWLJUmJiYl66aWXFBAQoPr162vp0qVW46dPn67FixfrxhtvlCTdeuutWr9+vVPnUhRaKEQL1miBFopCC7Rg69hooeTRAi0UhRZogRYMtEALpdVCefr222/l4+OjV155xeq+Ro0amb82Dz/8sPlNAOvWrdN3332nOXPmWIyvVauWxSTtUaNGydvbW++8847Tx/Pmm28qOjpaN998sySpT58+Gj16tMWYhQsXqkmTJhbLnnnmGattffbZZ0pISFBSUpI8PDzUu3dvrVq1yuXJ6vZs377d4nbXrl3NE9BtiYyMVGRkpPl2QROOrFq1SgMGDFD9+vU1d+5cBQcHq2PHji5P2rt8+bJ8fHxcWsfd0ELx0ULlER4ebvUmsMzMzCLfcOWMX3/9VYcOHSrWuvRQaOzYsRoxYoQyMjLUokULzZkzR/Xr11e1atXsrvPll1+a3+AlGW+igGNMOAYAAAAAAGXqww8/lCTt27dPL774oksf23Xp0iX99NNPkox3/Xt7F/3UxsWLFy0+WisjI8OpfZlMJi1dulSxsbF688039cQTT2jZsmW65ZZbrMbGx8frzz//lGS8uCkZV25q27at3n//fYf7cNb69ev1xx9/6Ouvv5a/v79eeOEFSVJqaqqGDBnicN3vvvtO3bp1U8uWLSUZL9ZGR0dbjBk9erSefvppi2XLli2z+nodPXpUr7zyinbs2KGbbrpJc+fOVXh4uBYtWuT0uThy9YsQc+bMsfuCrCT16NFDPXr0MN8ueNHankceeUTt2rWTJLVv315PP/20Hn/8cXl5edldJzIyssivcXHQguU+nEULttECLdCCgRZogRYMtEALtGCgBVqgBUNFbqE85Ofna+LEierTp48uX76siRMn6o033rB5/v7+/srKyjLfzs7OVvXq1R1uf+fOnUpOTtalS5e0c+dOPfzww04dV0ZGhiZPnqynnnrK7pju3burSpUqFstSU1M1ZswYi2WffPKJIiMjzZMLp0+frnHjxpXYpLIrnT17VocOHTK3VVJ8fX118eJFScb3MD8/P73zzjsymUwunceff/4pPz+/Ej22yoIWShYtVGzz5s2zmly8YcMG7d6922LZTTfdpPfff1/vv/++0tPTJUl169aVJHXp0sXqUxEuXbqkDRs2yGQy6ffff7f4Heqbb75Rr1699Nprr9l9wxk9FKpRo4Zq1KihyZMnq1OnTpoyZYpWr17t1O/CcB5fTQAAAAAAUC4WLVqkn376Sd9++62aN2+uf/zjH5Jk8RF7V0pLS9PEiRP1+uuvy2QyaeTIkZo+fbpuuukmh/vx8fHR+PHjzbd37NjhcPyRI0e0ceNGffrppwoODtbXX3+tatWqqVmzZhowYIBatGihv//97xowYIB5nWeeecbiycY+ffqoevXq8vT0tPskbVpamo4eParLly87fMFOMl70jI6O1vbt2/XCCy9oxYoV5iuBOPNiXkZGhrp3727zKkQFIiIi9O6771qtV/DibIE1a9Zo5MiR5q/7yy+/rGbNmslkMsnDw6PIY3HVxo0b9dFHH5XY9qpVq6Zq1app9erVqlu3rt555x317NlTderUKbF9uIoWaMEZtGCNFmihJNACLdCCgRZogRYMtEALtGC4HlsoC7m5uYqNjdV7772nQYMGadCgQZKMKz52795dI0eO1COPPGJxpcTq1atbTLLMysqyO8kyIyNDc+bMUWJiotatWyeTyaRBgwapVatWCg8PV6NGjRweX0ZGhsN/A1c+1j41NVV33nmn+Xbz5s2Vmprq9PoF8vPzlZOT4/Cq6LNmzdLIkSMVERGh06dPW3y9CixcuFDx8fHm24cPH1bXrl0d7tvX11c5OTmSCidZfvvtt1bHsmjRIoeT/VNSUpSUlCR/f38lJiY63Ke7oAVaoAVLjRs31vTp062ucCzJaqLvyJEjNXLkSEnGJGVvb2+LN3Vdbdq0aerbt686dOigwYMHa+vWrebfi+69917NmjVLDRs2tLs+PRRKS0tTeHi46tWrp23btmnevHlq3769xo8fr+7duzt9/H/88Yc2bNhg8ekfKMSEYwAAAAAAUKYuX76sSZMm6fTp09q5c6cGDhyomTNnmp/EvPLjqyTp+++/16pVq3Tq1CnNmDFDjRs3lmQ8ETdx4kQFBARo4MCBCg4Otrm/vLw8iysS2Xri6kq5ubm6ePGiVq1aZd6XJLVr1067d+/Wd999Z/OF3aioKIvb8+fP17p165SammrxsV8Fdu3aJUnavXu32rdvb/d4Fi5cqJiYGH3xxRe68847tXbtWg0bNkzjxo3TE0884fBcChT1pGNERITTH9uamppqfoFbkry8vHTzzTcrPT1d9erVc2obBXJychx+tN+2bdtUp04dJSQkmL++V181Kj4+XgsXLjTfPn/+vMN9/vnnn3r33XcVGxurTZs2KSEhQZ07d9aoUaPML544a8OGDTp48KDVY9ZZtGCgBVqgBQMt0AItGGiBFmjBQAu0QAsGWqCFsrR8+XKlpqbqn//8p+rXr29e/uSTT6pjx45asGCBTp8+raFDh2rPnj164YUXzFevXLJkiSQpPT1dHh4eWr58uT788EO1adPGfP/SpUv1/PPPa9asWfL09JRkfLz8p59+qr59+2rAgAEaO3as3ePLzc1VRESE3njjDbVu3VqRkZGqXbt2sc71xhtv1NmzZ80TRjMzM3XDDTe4vJ0DBw6oS5cuOnDggM37N23apJ07dyo+Pl6HDx9Wbm6ujh8/rpkzZ1qM27dvnwYMGKAHHnjAvMzeGxFyc3PVrl07/fHHH/r8888VGRmp1NRUBQcH629/+5tiYmIsrpp+5eQ/ybh6+W+//SbJ+F6amZnp8nlXdrRAC7D08ssv6+WXX3Y45uDBg1ZXL/7Pf/4jDw8P8xugCrz++uu69dZbNW3aNCUnJ2vDhg3y9vbW2LFj9fDDD2vLli2SpICAADVv3tzhfunBYDKZFBkZqb59+6pHjx6KiYlReHi4evTooe+//96l479w4YIOHjyoCxcuuLSeu2DCMQAAAAAAKDP5+fnq16+fgoODtX79enl5eWnHjh169dVX5ePjY/HxngWqVKmiZ555Rq1atZJkXGXJ09NT7du317Jly/Sf//zHap3c3FwlJyfLZDLp4sWLeuyxx5STk6Pu3btrw4YNDo/x9ttvV3h4uN3777vvPpvLv/rqK/P+Tp8+rccee0wvvfSSzRcj8/Pz9e677+qjjz7SjBkz9PDDD9u9wlGnTp307LPPmj/yrGXLltq7d6/5I2X79++v2267zeE5nT9/Xlu2bNGuXbvUoEEDTZs2TX/9618drmNPwZOOV8rKylKtWrVc3laXLl2UkpJi82v0f//3f3r55Ze1efNmValSRYGBgZKMj8e90uHDh9W4cWONGzfOvKzgI99sWbp0qTw9PbV161Zt27ZNwcHB2rFjhz799FOrj5UrysGDB4t9lQNaKPw60AIt0AItFKAFWqAFAy3QAi0YaIEWaMHgzi2UtWeffdbufbVr19bkyZPNt9u1a2cxgayoq1gOHz5cw4cPt1ru6elpcQVZR2JiYiQZb0iYM2eORowYobVr12rx4sVavHixxdhDhw6pfv36FleY9fb2VkJCgiQpJCREa9eu1ahRoyQZV67t0KFDkcdwtcDAQMXFxSkkJMTqvuXLl2vevHnatm2bfHx8dMcdd0iS/P39bW4rICDAqatoV6lSpcirr/bp06fog4ddtEALMCQmJuq5556zWFbwu8XVv5O89957Gj16tFPbbdCggS5duqSAgACtW7dO3t7GFM7evXurbdu2ql+/vn755RentkUPBg8PD33wwQfm25MmTdKQIUN0xx13mPfhrMaNG2v8+PH66quvXFrPXTDhGAAAAAAAlBlPT0+tXbtWkvFC3tKlS7Vjxw6lp6dr6dKl+vXXX7V//36Lde6++26L20lJSfL29jZf2eiuu+6y2k+/fv308ccfy8fHR9WqVVPNmjWLfGGxQFBQkMXtCxcu6OTJk/rLX/5isfyFF17QsGHDJEmhoaGKjY2Vp6enfHx8VL9+ffOThLa88cYb6ty5s/r376+kpCRNmjRJM2bMsDn29ttvl2RcgaLgib8r/f7771q3bp1uueUWu/u78on8f/3rX+rWrZt+/fVXHThwQCNGjLAYm56ersuXL1t93O7SpUt19913KyQkRO+++6769esnSfr3v/+tWrVqufyCo2R8nJutF0z379+vfv36admyZbr11lslyfyxcbZeEPXz83P6Y12vfNJ38eLFqlevnoKCgsxPorpi/Pjx5o8rdBUtGGjBQAu0QAsGWqAFWjDQAi3QgoEWaIEWDO7cAmzz8vLSmDFjzF2HhoYqNDRUe/fuVZs2beTp6an+/fsrLCxMbdu2tbmNsWPH6rHHHtPJkyclSbGxseYratoTGRmpBQsWKCcnR1lZWZo0aZLNcSdOnNDQoUPl6+ur+Ph4pyfaZ2dn68yZM8rLy1NOTo7OnTsnb29vVa1a1e46W7du1VtvvaXs7Gzl5+fLZDKpadOmmjp1qtXY1157zeZyVFy0UIgWSkdQUJDV1YnnzZunatWq2Z1cfPHiRb3xxhvatm2b+Qre+fn5+sc//qEpU6ZY/C4QHh6uQ4cO6fvvvzf/zlDws9xV9FA877//vgICApSbm6szZ844fEMdDEw4BgAAAAAA5eLRRx81vwBXv359paena/ny5frHP/6hvXv3ysvLq9jbnj9/vjZt2qTu3bu7vO7VV4VITEzU7NmzzS/22jJx4kTt27fPqY84Gz9+vI4cOaKVK1dKkubMmaNBgwbpxRdf1DvvvGN3vUWLFtlcfvUVForSuXNn1a5dW7///rvuvfdeJSYm6uDBg6pbt65q166tRYsWKTMzU+PHj7e5/kMPPaSPP/7YfOWslStXWl0p4Wpr167VL7/8oosXLyo7O9vio/uudPnyZYWFhWnXrl1av369zRfEbcnJydGZM2eUn5+vnJwcZWdnKysry+oF8OLatGmTjh49qkuXLuncuXMuf82LQgu0cDVaoAVaMNACLdCCgRZogRYMtEALtGBw1xZKm62rWNoTHR1tNTY9PV1S4ZUmC8TExCgvL8/pbcfExJivVH219PR01a1bV5K0YsUK3X///Rb3Dx06VMnJyapWrVqR+wkICFBsbKz27NkjybgSpKPJW2+99ZbOnz9vnuRVs2ZNHTt2zObYevXq6bXXXnPpqpiBgYFavHixoqOjVaVKFfn5+SkgIEAhISFq3bq1zXUOHjyo8PBwbdu2TY0aNTIvT0xMVPfu3fXbb79ZfI+MjY3VhAkTFB8fb162e/dup4/RXdACLeDaREREKD8/X3v27DG/AejSpUsaP368pk6dqlmzZlmMT01N1aZNm8wTjguEhITYvELw1ejBsHXrVvMbtDw8PGQymdSuXTt5eHjI09NTY8aMsdr+jBkzdPr0aXl7e8vX11c1a9Y0TxKHfUw4BgAAAAAAZe7YsWNKT0/XtGnTzMsaNmyo8ePH64svvtDBgwd111132XyC29GT1lc+CT1mzBibL5qmpKSU2HlcadOmTWrWrJnVi6ZXX4WoR48eatu2rfkj1zw9PbVy5UqlpqY63H6nTp106tQpm1dDev755x2ue+WTjvv379fZs2fVuHFj8/1RUVHq1auXunTp4nA7BZYsWaIffvhBJ06c0L/+9S/deOONdsf26dNHHTp0kJeXl6pUqaIaNWrI19fX5pUCvLy81LdvX7399tsOP9r1SrfccovWrVunoUOHmp8YrFGjhv7yl79YvWialpamnj17ymQyycPDQ2lpaRoxYoSqVasmDw8PPfTQQ1bb79+/v+655x55eXmpWrVq8vf3V5MmTZw6NmfQAi3QgoEWaIEWDLRAC7RgoAVaoAUDLdACLZQdW1exdMSVscUZb8vw4cOVlpYmb29vNW3aVAsWLLA79vnnny/yiuVVq1Z1euJX7dq1Vbt2bafGenl5uTShTJLCwsIUFhZm8z57349MJpMkWU0OK+qNGFdePfTKyZkw0IJjtICimEwm87/J1csLfq8pSfRg6Nq1q7p27epwe+PGjbO43aZNG6sxR48edekY3RETjgEAAAAAQJlr0KCB8vLyrK6ktH37dqWlpZk/gtXVJ7grgnbt2lkt8/DwcOqFuI0bN6pp06Yu73PJkiVat26datSooSpVqmjdunV2P7a2Q4cOys3NLXKb99xzj1P79vf3l7+/v9PH+vDDDzs9VpI6duyojh07OjW2fv36Nj9a90pDhgyxuN2sWTM1a9bMpWNyBS1YooVCtGCgBcdowRotVFy04BgtGGjBMVqwRgsVFy045m4twLBhw4Yix7Rp08bmhLZhw4Zp7NixpXBU5adFixaaM2eOnnrqKWVnZ5sn+TVp0kSbNm2yOdnyoYcesrl8+PDhGjVqVFkcNkoALViihevLtGnTNG3aNN1///3mr/Hly5fVpUsXTZkyxeY6X375pd0rehf1Ox49oKwx4RgAAAAAAJQ5T09Pbd26VVOmTFFERIQ8PT2Vn5+v2267TVu2bJGvr2+J7Mfek3QTJkyw+oiykjBlyhRFRUVZLe/cubPmzp17zdt/9NFHbV6ladCgQXr11Vftrjdx4kRNnDjR4bZHjRqlGjVqWC1v1aqVPv74Y9cPFk6hheKhhcqHFoqHFiofWigeWqh8aKF4aKHyoYXioQVUdCEhIVbL1qxZY74Ctz0HDx4ssWM4cOBAkWOaNm2quLg4SdLmzZvl5+fn1LYbN26szZs3O30sjsZ369ZN3bp1c2o7iYmJTu8T1wdacH48LZSdUaNGObxScdWqVTVz5kzNnDnTqe2FhIQoIyPDqXFXowfnx0vOHX+jRo2KdfzuxMNk6xreAAAAAAAAkCRlZmbq119/VXBwcJnsLycnR56enjZfHC1KWFiY1RUPXn/9dXXu3LmEjq5o2dnZ8vPzs/oIP3tcPd9z586pZs2aTh+Pq+MvXLigKlWq2L2K1dWu5d+roqEF19BC5UULrqGFyosWXEMLlRctuIYWKi9acA0tAAAAoCJiwjEAAAAAAAAAAAAAAABQDs6dO6f09HTddttt5X0oZgkJCWrbtq3T45OTk9W8eXNVq1atyLE5OTk6ePCg3aurX+t4qXSPH6WHFkp2vEQLkH7++WfFx8fr+eefv6btpKam6rvvvlOfPn0slq9evVpdu3Z1+o1LKSkpqlatmm666aZrOh6UH+feLgcAAAAAAACg0kpLS1NSUpLFsjNnzujnn38upyO6dgkJCS6NT05OVk5OjlNjc3JyrK6IVpLjJdePHyWDFmgBBlqgBRhogRZ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      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font',family='Malgun Gothic')\n",
    "plt.rc('axes', unicode_minus = False)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(50,30))\n",
    "fig.set_facecolor('white')\n",
    " \n",
    "font_size = 15\n",
    "plt.xticks(steps,[f'step {s}\\n'+'\\n'.join(sv_per_step[i]) for i,s in enumerate(steps)], fontsize=12)\n",
    "plt.plot(steps,adjusted_r_squared, marker='o')\n",
    "    \n",
    "plt.ylabel('Adjusted R Squared',fontsize=font_size)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>학원_5분이내거리최소</th>\n",
       "      <th>스포츠_5분이내거리최대</th>\n",
       "      <th>기온</th>\n",
       "      <th>스포츠_5분이내거리평균</th>\n",
       "      <th>부동산_5분이내거리최대</th>\n",
       "      <th>강수형태</th>\n",
       "      <th>풍속</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>6.563172</td>\n",
       "      <td>0.002745</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.036290</td>\n",
       "      <td>0.944220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>13.501667</td>\n",
       "      <td>0.002745</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>18.493280</td>\n",
       "      <td>0.002745</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.037634</td>\n",
       "      <td>0.996102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>22.890278</td>\n",
       "      <td>0.002745</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.988056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>26.819086</td>\n",
       "      <td>0.002745</td>\n",
       "      <td>0.003916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.983333</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>26.125672</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.034946</td>\n",
       "      <td>0.310081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>21.795417</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.086111</td>\n",
       "      <td>0.453750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>14.989516</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.044355</td>\n",
       "      <td>0.413441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>5.799861</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.061111</td>\n",
       "      <td>0.443889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.376075</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.525672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     학원_5분이내거리최소  스포츠_5분이내거리최대         기온  스포츠_5분이내거리평균  부동산_5분이내거리최대  \\\n",
       "0       0.000709      0.003543   6.563172      0.002745      0.003916   \n",
       "1       0.000709      0.003543  13.501667      0.002745      0.003916   \n",
       "2       0.000709      0.003543  18.493280      0.002745      0.003916   \n",
       "3       0.000709      0.003543  22.890278      0.002745      0.003916   \n",
       "4       0.000709      0.003543  26.819086      0.002745      0.003916   \n",
       "..           ...           ...        ...           ...           ...   \n",
       "205     0.002500      0.002500  26.125672      0.002500      0.003719   \n",
       "206     0.002500      0.002500  21.795417      0.002500      0.003719   \n",
       "207     0.002500      0.002500  14.989516      0.002500      0.003719   \n",
       "208     0.002500      0.002500   5.799861      0.002500      0.003719   \n",
       "209     0.002500      0.002500   0.376075      0.002500      0.003719   \n",
       "\n",
       "         강수형태        풍속  \n",
       "0    0.036290  0.944220  \n",
       "1    0.055556  1.083333  \n",
       "2    0.037634  0.996102  \n",
       "3    0.000000  0.988056  \n",
       "4    0.000000  0.983333  \n",
       "..        ...       ...  \n",
       "205  0.034946  0.310081  \n",
       "206  0.086111  0.453750  \n",
       "207  0.044355  0.413441  \n",
       "208  0.061111  0.443889  \n",
       "209  0.072581  0.525672  \n",
       "\n",
       "[210 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 전체역\n",
    "x = df[['강수','강수형태','기온','풍속','풍향','학원_5분이내수', '학원_5분이내거리최소','학원_5분이내거리평균','학원_5분이내거리최대','버스_5분이내수','버스_5분이내거리최소','버스_5분이내거리평균','버스_5분이내거리최대',\n",
    "               '콘서트장_5분이내수','콘서트장_5분이내거리최소','콘서트장_5분이내거리평균','콘서트장_5분이내거리최대','공공시설_5분이내수','공공시설_5분이내거리최소','공공시설_5분이내거리평균','공공시설_5분이내거리최대',\n",
    "               '홀_5분이내수','홀_5분이내거리최소','홀_5분이내거리평균','홀_5분이내거리최대','미술관박물관(전시시설)_5분이내수','미술관박물관(전시시설)_5분이내거리최소','미술관박물관(전시시설)_5분이내거리평균','미술관박물관(전시시설)_5분이내거리최대','학교_5분이내수','학교_5분이내거리최소','학교_5분이내거리평균','학교_5분이내거리최대','체육시설_5분이내수','체육시설_5분이내거리최소',\n",
    "               '체육시설_5분이내거리평균','체육시설_5분이내거리최대','스타벅스_5분이내수','스타벅스_5분이내거리최소','스타벅스_5분이내거리평균','스타벅스_5분이내거리최대','부동산_5분이내수','부동산_5분이내거리최소','부동산_5분이내거리평균','부동산_5분이내거리최대','학문/교육_5분이내수','학문/교육_5분이내거리최소','학문/교육_5분이내거리평균','학문/교육_5분이내거리최대',\n",
    "               '음식점_5분이내수','음식점_5분이내거리최소','음식점_5분이내거리평균','음식점_5분이내거리최대','생활서비스_5분이내수','생활서비스_5분이내거리최소','생활서비스_5분이내거리평균','생활서비스_5분이내거리최대','소매점_5분이내수','소매점_5분이내거리최소','소매점_5분이내거리평균','소매점_5분이내거리최대','스포츠_5분이내수','스포츠_5분이내거리최소','스포츠_5분이내거리평균',\n",
    "               '스포츠_5분이내거리최대','숙박_5분이내수','숙박_5분이내거리최소','숙박_5분이내거리평균','숙박_5분이내거리최대','관광/여가/오락_5분이내수','관광/여가/오락_5분이내거리최소','관광/여가/오락_5분이내거리평균','관광/여가/오락_5분이내거리최대','지하철 역_5분이내수','지하철 역_5분이내거리최소','지하철 역_5분이내거리평균','지하철 역_5분이내거리최대','청소년 수련시설_5분이내수','청소년 수련시설_5분이내거리최소','청소년 수련시설_5분이내거리평균','청소년 수련시설_5분이내거리최대']]\n",
    "y = df[['자전거대여량']]\n",
    "\n",
    "x_adjust = df[[\n",
    " '학원_5분이내거리최소',\n",
    " '스포츠_5분이내거리최대',\n",
    " '기온',\n",
    " '스포츠_5분이내거리평균',\n",
    " '부동산_5분이내거리최대',\n",
    " '강수형태',\n",
    " '풍속'\n",
    "]] # 선택\n",
    "\n",
    "display(x_adjust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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       "192     0.002500      0.002500  17.784409      0.002500      0.003719   \n",
       "183     0.002500      0.002500  22.890278      0.002500      0.003719   \n",
       "\n",
       "         강수형태        풍속  \n",
       "36   0.000000  0.874861  \n",
       "92   0.037634  0.996102  \n",
       "169  0.045699  0.951210  \n",
       "192  0.170699  1.004032  \n",
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       "      <td>1.186250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.002914</td>\n",
       "      <td>28.147849</td>\n",
       "      <td>0.001586</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>0.065860</td>\n",
       "      <td>0.464651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.001165</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>28.147849</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.065860</td>\n",
       "      <td>0.464651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     학원_5분이내거리최소  스포츠_5분이내거리최대         기온  스포츠_5분이내거리평균  부동산_5분이내거리최대  \\\n",
       "97      0.000715      0.003997  15.671909      0.002616      0.004012   \n",
       "185     0.002500      0.002500  25.996102      0.002500      0.003719   \n",
       "101     0.000715      0.003997  12.871111      0.002616      0.004012   \n",
       "45      0.000192      0.002914  28.147849      0.001586      0.003824   \n",
       "165     0.001165      0.003951  28.147849      0.002718      0.004029   \n",
       "\n",
       "         강수형태        풍속  \n",
       "97   0.043011  0.870699  \n",
       "185  0.000000  1.053629  \n",
       "101  0.137500  1.186250  \n",
       "45   0.065860  0.464651  \n",
       "165  0.065860  0.464651  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_adjust, y, train_size=0.8, test_size=0.2)\n",
    "mlr = LinearRegression()\n",
    "mlr.fit(x_train, y_train) \n",
    "y_predict = mlr.predict(x_test)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()\n",
    "print(mlr.score(x_train, y_train))\n",
    "display(x_train.head())\n",
    "display(x_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>자전거대여량</td>      <th>  R-squared (uncentered):</th>      <td>   0.936</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.934</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   426.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 25 Apr 2021</td> <th>  Prob (F-statistic):</th>          <td>1.06e-117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:56:45</td>     <th>  Log-Likelihood:    </th>          <td> -1209.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   210</td>      <th>  AIC:               </th>          <td>   2432.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   203</td>      <th>  BIC:               </th>          <td>   2456.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[0]</th> <td>-3.197e+05</td> <td> 1.62e+04</td> <td>  -19.697</td> <td> 0.000</td> <td>-3.52e+05</td> <td>-2.88e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[1]</th> <td>-3.667e+05</td> <td> 2.59e+04</td> <td>  -14.161</td> <td> 0.000</td> <td>-4.18e+05</td> <td>-3.16e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[2]</th> <td>    5.9361</td> <td>    0.647</td> <td>    9.170</td> <td> 0.000</td> <td>    4.660</td> <td>    7.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[3]</th> <td> 2.366e+05</td> <td> 3.14e+04</td> <td>    7.533</td> <td> 0.000</td> <td> 1.75e+05</td> <td> 2.99e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[4]</th> <td> 3.213e+05</td> <td> 1.58e+04</td> <td>   20.372</td> <td> 0.000</td> <td>  2.9e+05</td> <td> 3.52e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[5]</th> <td>  400.4830</td> <td>  128.537</td> <td>    3.116</td> <td> 0.002</td> <td>  147.044</td> <td>  653.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x_adjust[6]</th> <td> -137.7556</td> <td>   19.274</td> <td>   -7.147</td> <td> 0.000</td> <td> -175.759</td> <td>  -99.752</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.179</td> <th>  Durbin-Watson:     </th> <td>   1.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  32.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.221</td> <th>  Prob(JB):          </th> <td>6.92e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.890</td> <th>  Cond. No.          </th> <td>1.39e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 1.39e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                 자전거대여량   R-squared (uncentered):                   0.936\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.934\n",
       "Method:                 Least Squares   F-statistic:                              426.6\n",
       "Date:                Sun, 25 Apr 2021   Prob (F-statistic):                   1.06e-117\n",
       "Time:                        12:56:45   Log-Likelihood:                         -1209.2\n",
       "No. Observations:                 210   AIC:                                      2432.\n",
       "Df Residuals:                     203   BIC:                                      2456.\n",
       "Df Model:                           7                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "x_adjust[0] -3.197e+05   1.62e+04    -19.697      0.000   -3.52e+05   -2.88e+05\n",
       "x_adjust[1] -3.667e+05   2.59e+04    -14.161      0.000   -4.18e+05   -3.16e+05\n",
       "x_adjust[2]     5.9361      0.647      9.170      0.000       4.660       7.213\n",
       "x_adjust[3]  2.366e+05   3.14e+04      7.533      0.000    1.75e+05    2.99e+05\n",
       "x_adjust[4]  3.213e+05   1.58e+04     20.372      0.000     2.9e+05    3.52e+05\n",
       "x_adjust[5]   400.4830    128.537      3.116      0.002     147.044     653.922\n",
       "x_adjust[6]  -137.7556     19.274     -7.147      0.000    -175.759     -99.752\n",
       "==============================================================================\n",
       "Omnibus:                       14.179   Durbin-Watson:                   1.454\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               32.974\n",
       "Skew:                           0.221   Prob(JB):                     6.92e-08\n",
       "Kurtosis:                       4.890   Cond. No.                     1.39e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 1.39e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최종 변수 선택\n",
    "\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "\n",
    "model = smf.ols(formula = '자전거대여량 ~ x_adjust -1', data = df)\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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