analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\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 lightgbm as lgb\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#import geopandas as gpd\n",
    "#from shapely.geometry import Polygon, LineString, Point\n",
    "# import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager, rc\n",
    "font_path = \"data/THEdog.ttf\"   #폰트파일의 위치\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>gid</th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
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       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
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       "      <td>67.0</td>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "           gid  acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차  \\\n",
       "0     다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "1     다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "2     다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00   \n",
       "3     다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25   \n",
       "4     다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "...        ...       ...      ...    ...    ...  ...      ...      ...   \n",
       "8983  다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8984  다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8985  다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "8986  다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00   \n",
       "8987  다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00   \n",
       "\n",
       "           버스저녁승차       버스저녁하차  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "0        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       46      201   \n",
       "1        0.000000     0.000000  ...  0.0  0.0  0.0  0.0       53      137   \n",
       "2        0.000000     0.000000  ...  0.0  0.0  0.0  0.0        2       10   \n",
       "3     1914.833333  3168.333333  ...  0.0  1.0  0.0  0.0        3        9   \n",
       "4        0.000000     0.000000  ...  0.0  3.0  0.0  0.0       16       54   \n",
       "...           ...          ...  ...  ...  ...  ...  ...      ...      ...   \n",
       "8983     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       23      105   \n",
       "8984     0.000000     0.000000  ...  0.0  0.0  0.0  0.0        0        0   \n",
       "8985     0.000000     0.000000  ...  0.0  0.0  0.0  0.0      135      886   \n",
       "8986     0.000000     0.000000  ...  0.0  0.0  0.0  1.0      219      303   \n",
       "8987     0.000000     0.000000  ...  0.0  0.0  0.0  0.0       17       26   \n",
       "\n",
       "      지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "0         0.0      0.0      0.0      0.0  \n",
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       "3         0.0      0.0      0.0      0.0  \n",
       "4         0.0      0.0      0.0      0.0  \n",
       "...       ...      ...      ...      ...  \n",
       "8983      0.0      0.0      0.0      0.0  \n",
       "8984      0.0      0.0      0.0      0.0  \n",
       "8985      0.0      0.0      0.0      0.0  \n",
       "8986      0.0      0.0      0.0      0.0  \n",
       "8987      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[8988 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pd.read_csv(\"output/그리드 전처리 완료/final_data2.csv\")\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
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       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
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       "      <th>퇴근시간자전거</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2486.75</td>\n",
       "      <td>3392.25</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>다사559438</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사618387</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사559464</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사585417</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사548430</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8988 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차       버스저녁승차  \\\n",
       "gid                                                                             \n",
       "다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25  1914.833333   \n",
       "다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "...            ...      ...    ...    ...  ...      ...      ...          ...   \n",
       "다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00     0.000000   \n",
       "다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "\n",
       "               버스저녁하차  관광여가오락  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "gid                            ...                                         \n",
       "다사542434     0.000000     0.0  ...  0.0  0.0  0.0  0.0       46      201   \n",
       "다사547430     0.000000     0.0  ...  0.0  0.0  0.0  0.0       53      137   \n",
       "다사549419     0.000000     0.0  ...  0.0  0.0  0.0  0.0        2       10   \n",
       "다사551417  3168.333333     0.0  ...  0.0  1.0  0.0  0.0        3        9   \n",
       "다사564435     0.000000     0.0  ...  0.0  3.0  0.0  0.0       16       54   \n",
       "...               ...     ...  ...  ...  ...  ...  ...      ...      ...   \n",
       "다사559438     0.000000     0.0  ...  0.0  0.0  0.0  0.0       23      105   \n",
       "다사618387     0.000000     0.0  ...  0.0  0.0  0.0  0.0        0        0   \n",
       "다사559464     0.000000     0.0  ...  0.0  0.0  0.0  0.0      135      886   \n",
       "다사585417     0.000000     0.0  ...  0.0  0.0  0.0  1.0      219      303   \n",
       "다사548430     0.000000     0.0  ...  0.0  0.0  0.0  0.0       17       26   \n",
       "\n",
       "          지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                           \n",
       "다사542434      0.0      0.0      0.0      0.0  \n",
       "다사547430      0.0      0.0      0.0      0.0  \n",
       "다사549419      0.0      0.0      0.0      0.0  \n",
       "다사551417      0.0      0.0      0.0      0.0  \n",
       "다사564435      0.0      0.0      0.0      0.0  \n",
       "...           ...      ...      ...      ...  \n",
       "다사559438      0.0      0.0      0.0      0.0  \n",
       "다사618387      0.0      0.0      0.0      0.0  \n",
       "다사559464      0.0      0.0      0.0      0.0  \n",
       "다사585417      0.0      0.0      0.0      0.0  \n",
       "다사548430      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[8988 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = final_data.set_index(\"gid\")\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gid</th>\n",
       "      <th>2030val</th>\n",
       "      <th>layer</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>다사579454</td>\n",
       "      <td>77.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.02366 37.50691, 127.02365 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>다사613448</td>\n",
       "      <td>66.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.06216 37.50166, 127.06215 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>다사585452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.03046 37.50514, 127.03045 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>다사583456</td>\n",
       "      <td>227.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.02817 37.50874, 127.02817 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>다사592432</td>\n",
       "      <td>85.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.03849 37.48714, 127.03848 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>다사559438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9129</th>\n",
       "      <td>다사618387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>다사559464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9131</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>다사548430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid  2030val           layer  \\\n",
       "0     다사579454     77.0  강남구 20,30대 인구수   \n",
       "1     다사613448     66.0  강남구 20,30대 인구수   \n",
       "2     다사585452      0.0  강남구 20,30대 인구수   \n",
       "3     다사583456    227.0  강남구 20,30대 인구수   \n",
       "4     다사592432     85.0  강남구 20,30대 인구수   \n",
       "...        ...      ...             ...   \n",
       "9128  다사559438      NaN  서초구 20,30대 인구수   \n",
       "9129  다사618387      NaN  서초구 20,30대 인구수   \n",
       "9130  다사559464      NaN  서초구 20,30대 인구수   \n",
       "9131  다사585417      NaN  서초구 20,30대 인구수   \n",
       "9132  다사548430      NaN  서초구 20,30대 인구수   \n",
       "\n",
       "                                               geometry  \n",
       "0     MULTIPOLYGON (((127.02366 37.50691, 127.02365 ...  \n",
       "1     MULTIPOLYGON (((127.06216 37.50166, 127.06215 ...  \n",
       "2     MULTIPOLYGON (((127.03046 37.50514, 127.03045 ...  \n",
       "3     MULTIPOLYGON (((127.02817 37.50874, 127.02817 ...  \n",
       "4     MULTIPOLYGON (((127.03849 37.48714, 127.03848 ...  \n",
       "...                                                 ...  \n",
       "9128  MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...  \n",
       "9129  MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...  \n",
       "9130  MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...  \n",
       "9131  MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...  \n",
       "9132  MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...  \n",
       "\n",
       "[9133 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gpd.read_file('data/서울시 격자 인구 데이터/강남서초2030대인구수 - 복사본.geojson')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.02366 37.50691, 127.02365 ...</td>\n",
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       "      <td>다사613448</td>\n",
       "      <td>66.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.06216 37.50166, 127.06215 ...</td>\n",
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       "      <td>227.0</td>\n",
       "      <td>강남구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.02817 37.50874, 127.02817 ...</td>\n",
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       "      <td>강남구 20,30대 인구수</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
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       "      <td>MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...</td>\n",
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       "      <th>9131</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
       "      <td>MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>서초구 20,30대 인구수</td>\n",
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      "text/plain": [
       "           gid  2030val           layer  \\\n",
       "0     다사579454     77.0  강남구 20,30대 인구수   \n",
       "1     다사613448     66.0  강남구 20,30대 인구수   \n",
       "2     다사585452      0.0  강남구 20,30대 인구수   \n",
       "3     다사583456    227.0  강남구 20,30대 인구수   \n",
       "4     다사592432     85.0  강남구 20,30대 인구수   \n",
       "...        ...      ...             ...   \n",
       "9128  다사559438      0.0  서초구 20,30대 인구수   \n",
       "9129  다사618387      0.0  서초구 20,30대 인구수   \n",
       "9130  다사559464      0.0  서초구 20,30대 인구수   \n",
       "9131  다사585417      0.0  서초구 20,30대 인구수   \n",
       "9132  다사548430      0.0  서초구 20,30대 인구수   \n",
       "\n",
       "                                               geometry  \n",
       "0     MULTIPOLYGON (((127.02366 37.50691, 127.02365 ...  \n",
       "1     MULTIPOLYGON (((127.06216 37.50166, 127.06215 ...  \n",
       "2     MULTIPOLYGON (((127.03046 37.50514, 127.03045 ...  \n",
       "3     MULTIPOLYGON (((127.02817 37.50874, 127.02817 ...  \n",
       "4     MULTIPOLYGON (((127.03849 37.48714, 127.03848 ...  \n",
       "...                                                 ...  \n",
       "9128  MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...  \n",
       "9129  MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...  \n",
       "9130  MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...  \n",
       "9131  MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...  \n",
       "9132  MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...  \n",
       "\n",
       "[9133 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['2030val'].apply(pd.isna), '2030val'] = 0\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 격자 데이터\n",
    "data_gid = data[['gid', 'geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9131</th>\n",
       "      <td>다사585417</td>\n",
       "      <td>MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...</td>\n",
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       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>다사548430</td>\n",
       "      <td>MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...</td>\n",
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       "  </tbody>\n",
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       "<p>9133 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gid                                           geometry\n",
       "0     다사579454  MULTIPOLYGON (((127.02366 37.50691, 127.02365 ...\n",
       "1     다사613448  MULTIPOLYGON (((127.06216 37.50166, 127.06215 ...\n",
       "2     다사585452  MULTIPOLYGON (((127.03046 37.50514, 127.03045 ...\n",
       "3     다사583456  MULTIPOLYGON (((127.02817 37.50874, 127.02817 ...\n",
       "4     다사592432  MULTIPOLYGON (((127.03849 37.48714, 127.03848 ...\n",
       "...        ...                                                ...\n",
       "9128  다사559438  MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...\n",
       "9129  다사618387  MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...\n",
       "9130  다사559464  MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...\n",
       "9131  다사585417  MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...\n",
       "9132  다사548430  MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...\n",
       "\n",
       "[9133 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gid.to_csv(\"output/그리드 전처리 완료/data_gid.csv\", encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.merge(final_data, data_gid, on = 'gid', how = 'left')\n",
    "final_data = gpd.GeoDataFrame(final_data, geometry='geometry')\n",
    "final_data.to_file('output/그리드 전처리 완료/analysis1_dataset.geojson', driver=\"GeoJSON\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사고가 발생한 지역의 데이터를 NEW_FINAL_DATA에 따로 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_final_data = final_data[final_data[\"acci_cnt\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>다사612444</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>831.416667</td>\n",
       "      <td>1542.00000</td>\n",
       "      <td>3227.583333</td>\n",
       "      <td>1667.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "      <td>326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1483.406250</td>\n",
       "      <td>2391.90625</td>\n",
       "      <td>3284.010417</td>\n",
       "      <td>1545.020833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>166</td>\n",
       "      <td>375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt  2030val  과속방지턱  무인카메라  신호등       버스아침승차      버스아침하차  \\\n",
       "gid                                                                       \n",
       "다사542434       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사547430       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사549419       1.0     67.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사551417       1.0     26.0    0.0    0.0  0.0  2486.750000  3392.25000   \n",
       "다사564435       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "...            ...      ...    ...    ...  ...          ...         ...   \n",
       "다사612444       1.0      0.0    0.0    0.0  4.0     0.000000     0.00000   \n",
       "다사613445       1.0      0.0    0.0    0.0  4.0   831.416667  1542.00000   \n",
       "다사613446       1.0     42.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사619457       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사648432       1.0     24.0    0.0    0.0  5.0  1483.406250  2391.90625   \n",
       "\n",
       "               버스저녁승차       버스저녁하차  관광여가오락  ...  스포츠   음식점   학교   교육  출근시간자전거  \\\n",
       "gid                                         ...                                 \n",
       "다사542434     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0       46   \n",
       "다사547430     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0       53   \n",
       "다사549419     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0        2   \n",
       "다사551417  1914.833333  3168.333333     0.0  ...  0.0   1.0  0.0  0.0        3   \n",
       "다사564435     0.000000     0.000000     0.0  ...  0.0   3.0  0.0  0.0       16   \n",
       "...               ...          ...     ...  ...  ...   ...  ...  ...      ...   \n",
       "다사612444     0.000000     0.000000     0.0  ...  0.0   9.0  0.0  6.0       35   \n",
       "다사613445  3227.583333  1667.083333     1.0  ...  0.0  15.0  0.0  6.0       90   \n",
       "다사613446     0.000000     0.000000     0.0  ...  0.0   6.0  0.0  5.0       65   \n",
       "다사619457     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0      302   \n",
       "다사648432  3284.010417  1545.020833     1.0  ...  0.0  67.0  0.0  4.0      166   \n",
       "\n",
       "          퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                    \n",
       "다사542434      201      0.0      0.0      0.0      0.0  \n",
       "다사547430      137      0.0      0.0      0.0      0.0  \n",
       "다사549419       10      0.0      0.0      0.0      0.0  \n",
       "다사551417        9      0.0      0.0      0.0      0.0  \n",
       "다사564435       54      0.0      0.0      0.0      0.0  \n",
       "...           ...      ...      ...      ...      ...  \n",
       "다사612444      114      0.0      0.0      0.0      0.0  \n",
       "다사613445      326      0.0      0.0      0.0      0.0  \n",
       "다사613446      233      0.0      0.0      0.0      0.0  \n",
       "다사619457      845      0.0      0.0      0.0      0.0  \n",
       "다사648432      375      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acci_cnt    float64\n",
       "2030val     float64\n",
       "과속방지턱       float64\n",
       "무인카메라       float64\n",
       "신호등         float64\n",
       "버스아침승차      float64\n",
       "버스아침하차      float64\n",
       "버스저녁승차      float64\n",
       "버스저녁하차      float64\n",
       "관광여가오락      float64\n",
       "부동산         float64\n",
       "생활서비스       float64\n",
       "소매점         float64\n",
       "숙박          float64\n",
       "스포츠         float64\n",
       "음식점         float64\n",
       "학교          float64\n",
       "교육          float64\n",
       "출근시간자전거       int64\n",
       "퇴근시간자전거       int64\n",
       "지하철아침승차     float64\n",
       "지하철아침하차     float64\n",
       "지하철저녁승차     float64\n",
       "지하철저녁하차     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
       "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
       "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
       "       '지하철저녁하차'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 회귀모형 적합도 판단"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-cb1b6942c8ea>:20: 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 = new_final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
    "       '지하철저녁하차']]\n",
    "y = new_final_data[['acci_cnt']]\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 = new_final_data[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 = new_final_data[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(new_final_data[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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>...</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118557</td>\n",
       "      <td>0.089175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((126.98192 37.48871, 126.98192 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136598</td>\n",
       "      <td>0.060781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((126.98760 37.48513, 126.98760 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.067269</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((126.98993 37.47523, 126.98993 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.026104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080009</td>\n",
       "      <td>0.268385</td>\n",
       "      <td>0.186548</td>\n",
       "      <td>0.430229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007732</td>\n",
       "      <td>0.003993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((126.99221 37.47343, 126.99220 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041237</td>\n",
       "      <td>0.023957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((127.00680 37.48972, 127.00680 ...</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>다사559438</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059278</td>\n",
       "      <td>0.046584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사618387</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사559464</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347938</td>\n",
       "      <td>0.393079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사585417</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.564433</td>\n",
       "      <td>0.134428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사548430</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043814</td>\n",
       "      <td>0.011535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9225 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt   2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차  \\\n",
       "gid                                                                        \n",
       "다사542434       0.5  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사547430       0.5  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사549419       0.5  0.067269    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사551417       0.5  0.026104    0.0    0.0  0.000000  0.080009  0.268385   \n",
       "다사564435       0.5  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "...            ...       ...    ...    ...       ...       ...       ...   \n",
       "다사559438       0.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사618387       0.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사559464       0.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사585417       0.0  0.000000    0.0    0.0  0.233333  0.000000  0.000000   \n",
       "다사548430       0.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "\n",
       "            버스저녁승차    버스저녁하차  관광여가오락  ...       음식점   학교        교육   출근시간자전거  \\\n",
       "gid                                   ...                                      \n",
       "다사542434  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.118557   \n",
       "다사547430  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.136598   \n",
       "다사549419  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.005155   \n",
       "다사551417  0.186548  0.430229     0.0  ...  0.003984  0.0  0.000000  0.007732   \n",
       "다사564435  0.000000  0.000000     0.0  ...  0.011952  0.0  0.000000  0.041237   \n",
       "...            ...       ...     ...  ...       ...  ...       ...       ...   \n",
       "다사559438  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.059278   \n",
       "다사618387  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.000000   \n",
       "다사559464  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.347938   \n",
       "다사585417  0.000000  0.000000     0.0  ...  0.000000  0.0  0.019231  0.564433   \n",
       "다사548430  0.000000  0.000000     0.0  ...  0.000000  0.0  0.000000  0.043814   \n",
       "\n",
       "           퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \\\n",
       "gid                                                      \n",
       "다사542434  0.089175      0.0      0.0      0.0      0.0   \n",
       "다사547430  0.060781      0.0      0.0      0.0      0.0   \n",
       "다사549419  0.004437      0.0      0.0      0.0      0.0   \n",
       "다사551417  0.003993      0.0      0.0      0.0      0.0   \n",
       "다사564435  0.023957      0.0      0.0      0.0      0.0   \n",
       "...            ...      ...      ...      ...      ...   \n",
       "다사559438  0.046584      0.0      0.0      0.0      0.0   \n",
       "다사618387  0.000000      0.0      0.0      0.0      0.0   \n",
       "다사559464  0.393079      0.0      0.0      0.0      0.0   \n",
       "다사585417  0.134428      0.0      0.0      0.0      0.0   \n",
       "다사548430  0.011535      0.0      0.0      0.0      0.0   \n",
       "\n",
       "                                                   geometry  \n",
       "gid                                                          \n",
       "다사542434  MULTIPOLYGON (((126.98192 37.48871, 126.98192 ...  \n",
       "다사547430  MULTIPOLYGON (((126.98760 37.48513, 126.98760 ...  \n",
       "다사549419  MULTIPOLYGON (((126.98993 37.47523, 126.98993 ...  \n",
       "다사551417  MULTIPOLYGON (((126.99221 37.47343, 126.99220 ...  \n",
       "다사564435  MULTIPOLYGON (((127.00680 37.48972, 127.00680 ...  \n",
       "...                                                     ...  \n",
       "다사559438  MULTIPOLYGON (((127.00113 37.49240, 127.00112 ...  \n",
       "다사618387  MULTIPOLYGON (((127.06813 37.44669, 127.06813 ...  \n",
       "다사559464  MULTIPOLYGON (((127.00097 37.51583, 127.00097 ...  \n",
       "다사585417  MULTIPOLYGON (((127.03066 37.47359, 127.03065 ...  \n",
       "다사548430  MULTIPOLYGON (((126.98873 37.48514, 126.98873 ...  \n",
       "\n",
       "[9225 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = final_data.set_index(\"gid\")\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
       "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
       "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
       "       '지하철저녁하차', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>      <td>   0.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   7.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 21 May 2021</td> <th>  Prob (F-statistic):</th>          <td>1.46e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:25:15</td>     <th>  Log-Likelihood:    </th>          <td> -43.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th>          <td>   124.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th>          <td>   164.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    19</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>2030val</th> <td>    0.0068</td> <td>    0.002</td> <td>    3.481</td> <td> 0.001</td> <td>    0.003</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>과속방지턱</th>   <td> 2.972e-13</td> <td> 1.17e-12</td> <td>    0.254</td> <td> 0.801</td> <td>-2.07e-12</td> <td> 2.66e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>   -0.2675</td> <td>    1.121</td> <td>   -0.239</td> <td> 0.813</td> <td>   -2.531</td> <td>    1.996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.0353</td> <td>    0.021</td> <td>    1.649</td> <td> 0.107</td> <td>   -0.008</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침승차</th>  <td> 5.314e-05</td> <td>    0.000</td> <td>    0.233</td> <td> 0.817</td> <td>   -0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침하차</th>  <td>-6.267e-05</td> <td>    0.000</td> <td>   -0.262</td> <td> 0.794</td> <td>   -0.001</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁승차</th>  <td>-6.566e-05</td> <td>    0.000</td> <td>   -0.392</td> <td> 0.697</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td>    0.0002</td> <td>    0.000</td> <td>    0.374</td> <td> 0.711</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>   -0.1793</td> <td>    0.150</td> <td>   -1.192</td> <td> 0.240</td> <td>   -0.483</td> <td>    0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>    0.0423</td> <td>    0.080</td> <td>    0.526</td> <td> 0.601</td> <td>   -0.120</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>   -0.0089</td> <td>    0.031</td> <td>   -0.290</td> <td> 0.773</td> <td>   -0.071</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>   -0.0059</td> <td>    0.025</td> <td>   -0.237</td> <td> 0.814</td> <td>   -0.056</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>    0.3253</td> <td>    0.241</td> <td>    1.351</td> <td> 0.184</td> <td>   -0.161</td> <td>    0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>    0.4748</td> <td>    0.374</td> <td>    1.270</td> <td> 0.211</td> <td>   -0.280</td> <td>    1.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>음식점</th>     <td>    0.0112</td> <td>    0.011</td> <td>    1.053</td> <td> 0.298</td> <td>   -0.010</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>   -0.1470</td> <td>    0.653</td> <td>   -0.225</td> <td> 0.823</td> <td>   -1.466</td> <td>    1.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>    0.0618</td> <td>    0.029</td> <td>    2.152</td> <td> 0.037</td> <td>    0.004</td> <td>    0.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>출근시간자전거</th> <td>    0.0011</td> <td>    0.003</td> <td>    0.375</td> <td> 0.709</td> <td>   -0.005</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>    0.0011</td> <td>    0.001</td> <td>    1.588</td> <td> 0.120</td> <td>   -0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침승차</th> <td>-2.207e-09</td> <td> 4.69e-08</td> <td>   -0.047</td> <td> 0.963</td> <td>-9.68e-08</td> <td> 9.24e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침하차</th> <td>-2.753e-08</td> <td> 5.85e-07</td> <td>   -0.047</td> <td> 0.963</td> <td>-1.21e-06</td> <td> 1.15e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁승차</th> <td>-2.262e-08</td> <td>  4.8e-07</td> <td>   -0.047</td> <td> 0.963</td> <td>-9.92e-07</td> <td> 9.47e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁하차</th> <td>-3.864e-09</td> <td> 8.21e-08</td> <td>   -0.047</td> <td> 0.963</td> <td> -1.7e-07</td> <td> 1.62e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.719</td> <th>  Durbin-Watson:     </th> <td>   1.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.035</td> <th>  Jarque-Bera (JB):  </th> <td>   8.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.357</td> <th>  Prob(JB):          </th> <td>  0.0141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.686</td> <th>  Cond. No.          </th> <td>5.38e+18</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 smallest eigenvalue is 3.95e-26. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared (uncentered):                   0.782\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.683\n",
       "Method:                 Least Squares   F-statistic:                              7.906\n",
       "Date:                Fri, 21 May 2021   Prob (F-statistic):                    1.46e-08\n",
       "Time:                        09:25:15   Log-Likelihood:                         -43.027\n",
       "No. Observations:                  61   AIC:                                      124.1\n",
       "Df Residuals:                      42   BIC:                                      164.2\n",
       "Df Model:                          19                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val        0.0068      0.002      3.481      0.001       0.003       0.011\n",
       "과속방지턱       2.972e-13   1.17e-12      0.254      0.801   -2.07e-12    2.66e-12\n",
       "무인카메라         -0.2675      1.121     -0.239      0.813      -2.531       1.996\n",
       "신호등            0.0353      0.021      1.649      0.107      -0.008       0.079\n",
       "버스아침승차      5.314e-05      0.000      0.233      0.817      -0.000       0.001\n",
       "버스아침하차     -6.267e-05      0.000     -0.262      0.794      -0.001       0.000\n",
       "버스저녁승차     -6.566e-05      0.000     -0.392      0.697      -0.000       0.000\n",
       "버스저녁하차         0.0002      0.000      0.374      0.711      -0.001       0.001\n",
       "관광여가오락        -0.1793      0.150     -1.192      0.240      -0.483       0.124\n",
       "부동산            0.0423      0.080      0.526      0.601      -0.120       0.204\n",
       "생활서비스         -0.0089      0.031     -0.290      0.773      -0.071       0.053\n",
       "소매점           -0.0059      0.025     -0.237      0.814      -0.056       0.044\n",
       "숙박             0.3253      0.241      1.351      0.184      -0.161       0.811\n",
       "스포츠            0.4748      0.374      1.270      0.211      -0.280       1.229\n",
       "음식점            0.0112      0.011      1.053      0.298      -0.010       0.033\n",
       "학교            -0.1470      0.653     -0.225      0.823      -1.466       1.172\n",
       "교육             0.0618      0.029      2.152      0.037       0.004       0.120\n",
       "출근시간자전거        0.0011      0.003      0.375      0.709      -0.005       0.007\n",
       "퇴근시간자전거        0.0011      0.001      1.588      0.120      -0.000       0.003\n",
       "지하철아침승차    -2.207e-09   4.69e-08     -0.047      0.963   -9.68e-08    9.24e-08\n",
       "지하철아침하차    -2.753e-08   5.85e-07     -0.047      0.963   -1.21e-06    1.15e-06\n",
       "지하철저녁승차    -2.262e-08    4.8e-07     -0.047      0.963   -9.92e-07    9.47e-07\n",
       "지하철저녁하차    -3.864e-09   8.21e-08     -0.047      0.963    -1.7e-07    1.62e-07\n",
       "==============================================================================\n",
       "Omnibus:                        6.719   Durbin-Watson:                   1.388\n",
       "Prob(Omnibus):                  0.035   Jarque-Bera (JB):                8.518\n",
       "Skew:                           0.357   Prob(JB):                       0.0141\n",
       "Kurtosis:                       4.686   Cond. No.                     5.38e+18\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 smallest eigenvalue is 3.95e-26. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = new_final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
    "       '지하철저녁하차']] \n",
    "target = new_final_data[[\"acci_cnt\"]]\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>      <td>   0.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   31.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 21 May 2021</td> <th>  Prob (F-statistic):</th>          <td>3.05e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:42:38</td>     <th>  Log-Likelihood:    </th>          <td> -48.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th>          <td>   106.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    56</td>      <th>  BIC:               </th>          <td>   117.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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>2030val</th> <td>    0.0072</td> <td>    0.002</td> <td>    4.315</td> <td> 0.000</td> <td>    0.004</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.0276</td> <td>    0.014</td> <td>    1.917</td> <td> 0.060</td> <td>   -0.001</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td>    0.0001</td> <td> 5.28e-05</td> <td>    2.617</td> <td> 0.011</td> <td> 3.24e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>    0.0621</td> <td>    0.024</td> <td>    2.601</td> <td> 0.012</td> <td>    0.014</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>    0.0014</td> <td>    0.000</td> <td>    5.426</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.966</td> <th>  Durbin-Watson:     </th> <td>   1.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.227</td> <th>  Jarque-Bera (JB):  </th> <td>   2.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.014</td> <th>  Prob(JB):          </th> <td>   0.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.016</td> <th>  Cond. No.          </th> <td>    531.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared (uncentered):                   0.740\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.717\n",
       "Method:                 Least Squares   F-statistic:                              31.96\n",
       "Date:                Fri, 21 May 2021   Prob (F-statistic):                    3.05e-15\n",
       "Time:                        09:42:38   Log-Likelihood:                         -48.275\n",
       "No. Observations:                  61   AIC:                                      106.6\n",
       "Df Residuals:                      56   BIC:                                      117.1\n",
       "Df Model:                           5                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val        0.0072      0.002      4.315      0.000       0.004       0.011\n",
       "신호등            0.0276      0.014      1.917      0.060      -0.001       0.056\n",
       "버스저녁하차         0.0001   5.28e-05      2.617      0.011    3.24e-05       0.000\n",
       "교육             0.0621      0.024      2.601      0.012       0.014       0.110\n",
       "퇴근시간자전거        0.0014      0.000      5.426      0.000       0.001       0.002\n",
       "==============================================================================\n",
       "Omnibus:                        2.966   Durbin-Watson:                   1.360\n",
       "Prob(Omnibus):                  0.227   Jarque-Bera (JB):                2.625\n",
       "Skew:                          -0.014   Prob(JB):                        0.269\n",
       "Kurtosis:                       4.016   Cond. No.                         531.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = new_final_data[['2030val', '신호등', '버스저녁하차','교육', '퇴근시간자전거']] \n",
    "target = new_final_data[[\"acci_cnt\"]]\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processSubset(X,y,feature_set):\n",
    "    model = sm.OLS(y.X[list(feature_set)])\n",
    "    regr =  model.fit()\n",
    "    AIC = regr.aic\n",
    "    return {\"model\":regr,\"AIC\":AIC}\n",
    "\n",
    "print(processSubset(X=x_data,y=target,feature_set=feature_columns[0:-1]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2486.750000</td>\n",
       "      <td>3392.25000</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</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>다사612444</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>831.416667</td>\n",
       "      <td>1542.00000</td>\n",
       "      <td>3227.583333</td>\n",
       "      <td>1667.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "      <td>326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1483.406250</td>\n",
       "      <td>2391.90625</td>\n",
       "      <td>3284.010417</td>\n",
       "      <td>1545.020833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>166</td>\n",
       "      <td>375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt  2030val  과속방지턱  무인카메라  신호등       버스아침승차      버스아침하차  \\\n",
       "gid                                                                       \n",
       "다사542434       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사547430       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사549419       1.0     67.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사551417       1.0     26.0    0.0    0.0  0.0  2486.750000  3392.25000   \n",
       "다사564435       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "...            ...      ...    ...    ...  ...          ...         ...   \n",
       "다사612444       1.0      0.0    0.0    0.0  4.0     0.000000     0.00000   \n",
       "다사613445       1.0      0.0    0.0    0.0  4.0   831.416667  1542.00000   \n",
       "다사613446       1.0     42.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사619457       1.0      0.0    0.0    0.0  0.0     0.000000     0.00000   \n",
       "다사648432       1.0     24.0    0.0    0.0  5.0  1483.406250  2391.90625   \n",
       "\n",
       "               버스저녁승차       버스저녁하차  관광여가오락  ...  스포츠   음식점   학교   교육  출근시간자전거  \\\n",
       "gid                                         ...                                 \n",
       "다사542434     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0       46   \n",
       "다사547430     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0       53   \n",
       "다사549419     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0        2   \n",
       "다사551417  1914.833333  3168.333333     0.0  ...  0.0   1.0  0.0  0.0        3   \n",
       "다사564435     0.000000     0.000000     0.0  ...  0.0   3.0  0.0  0.0       16   \n",
       "...               ...          ...     ...  ...  ...   ...  ...  ...      ...   \n",
       "다사612444     0.000000     0.000000     0.0  ...  0.0   9.0  0.0  6.0       35   \n",
       "다사613445  3227.583333  1667.083333     1.0  ...  0.0  15.0  0.0  6.0       90   \n",
       "다사613446     0.000000     0.000000     0.0  ...  0.0   6.0  0.0  5.0       65   \n",
       "다사619457     0.000000     0.000000     0.0  ...  0.0   0.0  0.0  0.0      302   \n",
       "다사648432  3284.010417  1545.020833     1.0  ...  0.0  67.0  0.0  4.0      166   \n",
       "\n",
       "          퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                    \n",
       "다사542434      201      0.0      0.0      0.0      0.0  \n",
       "다사547430      137      0.0      0.0      0.0      0.0  \n",
       "다사549419       10      0.0      0.0      0.0      0.0  \n",
       "다사551417        9      0.0      0.0      0.0      0.0  \n",
       "다사564435       54      0.0      0.0      0.0      0.0  \n",
       "...           ...      ...      ...      ...      ...  \n",
       "다사612444      114      0.0      0.0      0.0      0.0  \n",
       "다사613445      326      0.0      0.0      0.0      0.0  \n",
       "다사613446      233      0.0      0.0      0.0      0.0  \n",
       "다사619457      845      0.0      0.0      0.0      0.0  \n",
       "다사648432      375      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_new_final_data = new_final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
    "       '지하철저녁하차']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
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       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>부동산</th>\n",
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       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
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       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
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       "      <th>gid</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>다사547430</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2486.750000</td>\n",
       "      <td>3392.25000</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사612444</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>831.416667</td>\n",
       "      <td>1542.00000</td>\n",
       "      <td>3227.583333</td>\n",
       "      <td>1667.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90</td>\n",
       "      <td>326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1483.406250</td>\n",
       "      <td>2391.90625</td>\n",
       "      <td>3284.010417</td>\n",
       "      <td>1545.020833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>166</td>\n",
       "      <td>375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2030val  과속방지턱  무인카메라  신호등       버스아침승차      버스아침하차       버스저녁승차  \\\n",
       "gid                                                                          \n",
       "다사542434      0.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "다사547430      0.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "다사549419     67.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "다사551417     26.0    0.0    0.0  0.0  2486.750000  3392.25000  1914.833333   \n",
       "다사564435      0.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "...           ...    ...    ...  ...          ...         ...          ...   \n",
       "다사612444      0.0    0.0    0.0  4.0     0.000000     0.00000     0.000000   \n",
       "다사613445      0.0    0.0    0.0  4.0   831.416667  1542.00000  3227.583333   \n",
       "다사613446     42.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "다사619457      0.0    0.0    0.0  0.0     0.000000     0.00000     0.000000   \n",
       "다사648432     24.0    0.0    0.0  5.0  1483.406250  2391.90625  3284.010417   \n",
       "\n",
       "               버스저녁하차  관광여가오락   부동산  ...  스포츠   음식점   학교   교육  출근시간자전거  \\\n",
       "gid                                  ...                                 \n",
       "다사542434     0.000000     0.0   0.0  ...  0.0   0.0  0.0  0.0       46   \n",
       "다사547430     0.000000     0.0   0.0  ...  0.0   0.0  0.0  0.0       53   \n",
       "다사549419     0.000000     0.0   0.0  ...  0.0   0.0  0.0  0.0        2   \n",
       "다사551417  3168.333333     0.0   3.0  ...  0.0   1.0  0.0  0.0        3   \n",
       "다사564435     0.000000     0.0   0.0  ...  0.0   3.0  0.0  0.0       16   \n",
       "...               ...     ...   ...  ...  ...   ...  ...  ...      ...   \n",
       "다사612444     0.000000     0.0   1.0  ...  0.0   9.0  0.0  6.0       35   \n",
       "다사613445  1667.083333     1.0   1.0  ...  0.0  15.0  0.0  6.0       90   \n",
       "다사613446     0.000000     0.0   0.0  ...  0.0   6.0  0.0  5.0       65   \n",
       "다사619457     0.000000     0.0   0.0  ...  0.0   0.0  0.0  0.0      302   \n",
       "다사648432  1545.020833     1.0  13.0  ...  0.0  67.0  0.0  4.0      166   \n",
       "\n",
       "          퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                    \n",
       "다사542434      201      0.0      0.0      0.0      0.0  \n",
       "다사547430      137      0.0      0.0      0.0      0.0  \n",
       "다사549419       10      0.0      0.0      0.0      0.0  \n",
       "다사551417        9      0.0      0.0      0.0      0.0  \n",
       "다사564435       54      0.0      0.0      0.0      0.0  \n",
       "...           ...      ...      ...      ...      ...  \n",
       "다사612444      114      0.0      0.0      0.0      0.0  \n",
       "다사613445      326      0.0      0.0      0.0      0.0  \n",
       "다사613446      233      0.0      0.0      0.0      0.0  \n",
       "다사619457      845      0.0      0.0      0.0      0.0  \n",
       "다사648432      375      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_new_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-0f7cc6727fea>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  norm_new_final_data[:] = scaler.fit_transform(norm_new_final_data[:])\n",
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1736: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value[:, i].tolist())\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "norm_new_final_data[:] = scaler.fit_transform(norm_new_final_data[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
       "      <th>버스아침승차</th>\n",
       "      <th>버스아침하차</th>\n",
       "      <th>버스저녁승차</th>\n",
       "      <th>버스저녁하차</th>\n",
       "      <th>관광여가오락</th>\n",
       "      <th>부동산</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
       "      <th>지하철저녁하차</th>\n",
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       "      <th>gid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.183732</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.122488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>0.326829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217674</td>\n",
       "      <td>0.642015</td>\n",
       "      <td>0.247114</td>\n",
       "      <td>0.430773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014925</td>\n",
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       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사564435</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <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",
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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",
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       "    <tr>\n",
       "      <th>다사612444</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.100478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.072777</td>\n",
       "      <td>0.291838</td>\n",
       "      <td>0.416527</td>\n",
       "      <td>0.226660</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.293333</td>\n",
       "      <td>0.303349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>0.204878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.214354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>0.117073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>0.452690</td>\n",
       "      <td>0.423809</td>\n",
       "      <td>0.210064</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>0.350239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차    버스저녁승차  \\\n",
       "gid                                                                        \n",
       "다사542434  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사547430  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사549419  0.326829    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사551417  0.126829    0.0    0.0  0.000000  0.217674  0.642015  0.247114   \n",
       "다사564435  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "...            ...    ...    ...       ...       ...       ...       ...   \n",
       "다사612444  0.000000    0.0    0.0  0.181818  0.000000  0.000000  0.000000   \n",
       "다사613445  0.000000    0.0    0.0  0.181818  0.072777  0.291838  0.416527   \n",
       "다사613446  0.204878    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사619457  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사648432  0.117073    0.0    0.0  0.227273  0.129848  0.452690  0.423809   \n",
       "\n",
       "            버스저녁하차    관광여가오락       부동산  ...  스포츠       음식점   학교        교육  \\\n",
       "gid                                     ...                                 \n",
       "다사542434  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사547430  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사549419  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사551417  0.430773  0.000000  0.230769  ...  0.0  0.014925  0.0  0.000000   \n",
       "다사564435  0.000000  0.000000  0.000000  ...  0.0  0.044776  0.0  0.000000   \n",
       "...            ...       ...       ...  ...  ...       ...  ...       ...   \n",
       "다사612444  0.000000  0.000000  0.076923  ...  0.0  0.134328  0.0  0.333333   \n",
       "다사613445  0.226660  0.333333  0.076923  ...  0.0  0.223881  0.0  0.333333   \n",
       "다사613446  0.000000  0.000000  0.000000  ...  0.0  0.089552  0.0  0.277778   \n",
       "다사619457  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사648432  0.210064  0.333333  1.000000  ...  0.0  1.000000  0.0  0.222222   \n",
       "\n",
       "           출근시간자전거   퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                               \n",
       "다사542434  0.146667  0.183732      0.0      0.0      0.0      0.0  \n",
       "다사547430  0.170000  0.122488      0.0      0.0      0.0      0.0  \n",
       "다사549419  0.000000  0.000957      0.0      0.0      0.0      0.0  \n",
       "다사551417  0.003333  0.000000      0.0      0.0      0.0      0.0  \n",
       "다사564435  0.046667  0.043062      0.0      0.0      0.0      0.0  \n",
       "...            ...       ...      ...      ...      ...      ...  \n",
       "다사612444  0.110000  0.100478      0.0      0.0      0.0      0.0  \n",
       "다사613445  0.293333  0.303349      0.0      0.0      0.0      0.0  \n",
       "다사613446  0.210000  0.214354      0.0      0.0      0.0      0.0  \n",
       "다사619457  1.000000  0.800000      0.0      0.0      0.0      0.0  \n",
       "다사648432  0.546667  0.350239      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_new_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_final_result = new_final_data[[\"acci_cnt\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acci_cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사612444</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613445</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사613446</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사619457</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사648432</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt\n",
       "gid               \n",
       "다사542434       1.0\n",
       "다사547430       1.0\n",
       "다사549419       1.0\n",
       "다사551417       1.0\n",
       "다사564435       1.0\n",
       "...            ...\n",
       "다사612444       1.0\n",
       "다사613445       1.0\n",
       "다사613446       1.0\n",
       "다사619457       1.0\n",
       "다사648432       1.0\n",
       "\n",
       "[61 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-0f7cc6727fea>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  norm_new_final_data[:] = scaler.fit_transform(norm_new_final_data[:])\n",
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3047: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.iloc._setitem_with_indexer(key, value)\n",
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3032: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._setitem_slice(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "norm_new_final_data[:] = scaler.fit_transform(norm_new_final_data[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>      <td>   0.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   7.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 May 2021</td> <th>  Prob (F-statistic):</th>          <td>1.46e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:14:18</td>     <th>  Log-Likelihood:    </th>          <td> -43.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th>          <td>   124.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th>          <td>   164.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    19</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>2030val</th> <td>    0.0068</td> <td>    0.002</td> <td>    3.481</td> <td> 0.001</td> <td>    0.003</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>과속방지턱</th>   <td> 4.706e-13</td> <td> 1.93e-12</td> <td>    0.244</td> <td> 0.809</td> <td>-3.42e-12</td> <td> 4.37e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>   -0.2675</td> <td>    1.121</td> <td>   -0.239</td> <td> 0.813</td> <td>   -2.531</td> <td>    1.996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.0353</td> <td>    0.021</td> <td>    1.649</td> <td> 0.107</td> <td>   -0.008</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침승차</th>  <td> 5.314e-05</td> <td>    0.000</td> <td>    0.233</td> <td> 0.817</td> <td>   -0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침하차</th>  <td>-6.267e-05</td> <td>    0.000</td> <td>   -0.262</td> <td> 0.794</td> <td>   -0.001</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁승차</th>  <td>-6.566e-05</td> <td>    0.000</td> <td>   -0.392</td> <td> 0.697</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td>    0.0002</td> <td>    0.000</td> <td>    0.374</td> <td> 0.711</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>   -0.1793</td> <td>    0.150</td> <td>   -1.192</td> <td> 0.240</td> <td>   -0.483</td> <td>    0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>    0.0423</td> <td>    0.080</td> <td>    0.526</td> <td> 0.601</td> <td>   -0.120</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>   -0.0089</td> <td>    0.031</td> <td>   -0.290</td> <td> 0.773</td> <td>   -0.071</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>   -0.0059</td> <td>    0.025</td> <td>   -0.237</td> <td> 0.814</td> <td>   -0.056</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>    0.3253</td> <td>    0.241</td> <td>    1.351</td> <td> 0.184</td> <td>   -0.161</td> <td>    0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>    0.4748</td> <td>    0.374</td> <td>    1.270</td> <td> 0.211</td> <td>   -0.280</td> <td>    1.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>음식점</th>     <td>    0.0112</td> <td>    0.011</td> <td>    1.053</td> <td> 0.298</td> <td>   -0.010</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>   -0.1470</td> <td>    0.653</td> <td>   -0.225</td> <td> 0.823</td> <td>   -1.466</td> <td>    1.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>    0.0618</td> <td>    0.029</td> <td>    2.152</td> <td> 0.037</td> <td>    0.004</td> <td>    0.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>출근시간자전거</th> <td>    0.0011</td> <td>    0.003</td> <td>    0.375</td> <td> 0.709</td> <td>   -0.005</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>    0.0011</td> <td>    0.001</td> <td>    1.588</td> <td> 0.120</td> <td>   -0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침승차</th> <td>-2.207e-09</td> <td> 4.69e-08</td> <td>   -0.047</td> <td> 0.963</td> <td>-9.68e-08</td> <td> 9.24e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침하차</th> <td>-2.754e-08</td> <td> 5.85e-07</td> <td>   -0.047</td> <td> 0.963</td> <td>-1.21e-06</td> <td> 1.15e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁승차</th> <td> -2.26e-08</td> <td>  4.8e-07</td> <td>   -0.047</td> <td> 0.963</td> <td>-9.92e-07</td> <td> 9.47e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁하차</th> <td>-3.863e-09</td> <td> 8.21e-08</td> <td>   -0.047</td> <td> 0.963</td> <td> -1.7e-07</td> <td> 1.62e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.719</td> <th>  Durbin-Watson:     </th> <td>   1.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.035</td> <th>  Jarque-Bera (JB):  </th> <td>   8.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.357</td> <th>  Prob(JB):          </th> <td>  0.0141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.686</td> <th>  Cond. No.          </th> <td>3.30e+19</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 smallest eigenvalue is 1.05e-27. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared (uncentered):                   0.782\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.683\n",
       "Method:                 Least Squares   F-statistic:                              7.906\n",
       "Date:                Thu, 20 May 2021   Prob (F-statistic):                    1.46e-08\n",
       "Time:                        13:14:18   Log-Likelihood:                         -43.027\n",
       "No. Observations:                  61   AIC:                                      124.1\n",
       "Df Residuals:                      42   BIC:                                      164.2\n",
       "Df Model:                          19                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val        0.0068      0.002      3.481      0.001       0.003       0.011\n",
       "과속방지턱       4.706e-13   1.93e-12      0.244      0.809   -3.42e-12    4.37e-12\n",
       "무인카메라         -0.2675      1.121     -0.239      0.813      -2.531       1.996\n",
       "신호등            0.0353      0.021      1.649      0.107      -0.008       0.079\n",
       "버스아침승차      5.314e-05      0.000      0.233      0.817      -0.000       0.001\n",
       "버스아침하차     -6.267e-05      0.000     -0.262      0.794      -0.001       0.000\n",
       "버스저녁승차     -6.566e-05      0.000     -0.392      0.697      -0.000       0.000\n",
       "버스저녁하차         0.0002      0.000      0.374      0.711      -0.001       0.001\n",
       "관광여가오락        -0.1793      0.150     -1.192      0.240      -0.483       0.124\n",
       "부동산            0.0423      0.080      0.526      0.601      -0.120       0.204\n",
       "생활서비스         -0.0089      0.031     -0.290      0.773      -0.071       0.053\n",
       "소매점           -0.0059      0.025     -0.237      0.814      -0.056       0.044\n",
       "숙박             0.3253      0.241      1.351      0.184      -0.161       0.811\n",
       "스포츠            0.4748      0.374      1.270      0.211      -0.280       1.229\n",
       "음식점            0.0112      0.011      1.053      0.298      -0.010       0.033\n",
       "학교            -0.1470      0.653     -0.225      0.823      -1.466       1.172\n",
       "교육             0.0618      0.029      2.152      0.037       0.004       0.120\n",
       "출근시간자전거        0.0011      0.003      0.375      0.709      -0.005       0.007\n",
       "퇴근시간자전거        0.0011      0.001      1.588      0.120      -0.000       0.003\n",
       "지하철아침승차    -2.207e-09   4.69e-08     -0.047      0.963   -9.68e-08    9.24e-08\n",
       "지하철아침하차    -2.754e-08   5.85e-07     -0.047      0.963   -1.21e-06    1.15e-06\n",
       "지하철저녁승차     -2.26e-08    4.8e-07     -0.047      0.963   -9.92e-07    9.47e-07\n",
       "지하철저녁하차    -3.863e-09   8.21e-08     -0.047      0.963    -1.7e-07    1.62e-07\n",
       "==============================================================================\n",
       "Omnibus:                        6.719   Durbin-Watson:                   1.388\n",
       "Prob(Omnibus):                  0.035   Jarque-Bera (JB):                8.518\n",
       "Skew:                           0.357   Prob(JB):                       0.0141\n",
       "Kurtosis:                       4.686   Cond. No.                     3.30e+19\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 smallest eigenvalue is 1.05e-27. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = new_final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
    "       '지하철저녁하차']] \n",
    "target = new_final_data[[\"acci_cnt\"]]\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>      <td>   0.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   7.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 May 2021</td> <th>  Prob (F-statistic):</th>          <td>2.15e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:36:15</td>     <th>  Log-Likelihood:    </th>          <td> -43.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    61</td>      <th>  AIC:               </th>          <td>   125.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th>          <td>   165.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    19</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>2030val</th> <td>    1.4215</td> <td>    0.406</td> <td>    3.498</td> <td> 0.001</td> <td>    0.601</td> <td>    2.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>과속방지턱</th>   <td>-2.791e-16</td> <td> 2.73e-15</td> <td>   -0.102</td> <td> 0.919</td> <td>-5.78e-15</td> <td> 5.22e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>   -0.2718</td> <td>    1.133</td> <td>   -0.240</td> <td> 0.812</td> <td>   -2.559</td> <td>    2.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.8006</td> <td>    0.475</td> <td>    1.685</td> <td> 0.099</td> <td>   -0.158</td> <td>    1.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침승차</th>  <td>    0.6086</td> <td>    2.629</td> <td>    0.232</td> <td> 0.818</td> <td>   -4.697</td> <td>    5.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침하차</th>  <td>   -0.3305</td> <td>    1.275</td> <td>   -0.259</td> <td> 0.797</td> <td>   -2.904</td> <td>    2.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁승차</th>  <td>   -0.4762</td> <td>    1.312</td> <td>   -0.363</td> <td> 0.718</td> <td>   -3.124</td> <td>    2.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td>    1.2093</td> <td>    3.322</td> <td>    0.364</td> <td> 0.718</td> <td>   -5.496</td> <td>    7.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>   -0.5420</td> <td>    0.456</td> <td>   -1.188</td> <td> 0.241</td> <td>   -1.463</td> <td>    0.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>    0.5578</td> <td>    1.055</td> <td>    0.529</td> <td> 0.600</td> <td>   -1.571</td> <td>    2.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>   -0.2470</td> <td>    0.900</td> <td>   -0.274</td> <td> 0.785</td> <td>   -2.064</td> <td>    1.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>   -0.3899</td> <td>    1.487</td> <td>   -0.262</td> <td> 0.794</td> <td>   -3.390</td> <td>    2.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>    0.6636</td> <td>    0.486</td> <td>    1.364</td> <td> 0.180</td> <td>   -0.318</td> <td>    1.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>    0.9568</td> <td>    0.755</td> <td>    1.267</td> <td> 0.212</td> <td>   -0.568</td> <td>    2.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>음식점</th>     <td>    0.7783</td> <td>    0.717</td> <td>    1.086</td> <td> 0.284</td> <td>   -0.668</td> <td>    2.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>   -0.1288</td> <td>    0.660</td> <td>   -0.195</td> <td> 0.846</td> <td>   -1.461</td> <td>    1.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>    1.1258</td> <td>    0.522</td> <td>    2.155</td> <td> 0.037</td> <td>    0.071</td> <td>    2.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>출근시간자전거</th> <td>    0.3203</td> <td>    0.871</td> <td>    0.368</td> <td> 0.715</td> <td>   -1.437</td> <td>    2.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td>    1.1533</td> <td>    0.747</td> <td>    1.543</td> <td> 0.130</td> <td>   -0.355</td> <td>    2.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침승차</th> <td>   -0.0127</td> <td>    0.206</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.428</td> <td>    0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침하차</th> <td>   -0.0127</td> <td>    0.206</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.428</td> <td>    0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁승차</th> <td>   -0.0127</td> <td>    0.206</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.428</td> <td>    0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁하차</th> <td>   -0.0127</td> <td>    0.206</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.428</td> <td>    0.403</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.569</td> <th>  Durbin-Watson:     </th> <td>   1.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.037</td> <th>  Jarque-Bera (JB):  </th> <td>   8.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.367</td> <th>  Prob(JB):          </th> <td>  0.0183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.615</td> <th>  Cond. No.          </th> <td>4.25e+32</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 smallest eigenvalue is 1.37e-64. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:               acci_cnt   R-squared (uncentered):                   0.777\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.676\n",
       "Method:                 Least Squares   F-statistic:                              7.701\n",
       "Date:                Thu, 20 May 2021   Prob (F-statistic):                    2.15e-08\n",
       "Time:                        13:36:15   Log-Likelihood:                         -43.653\n",
       "No. Observations:                  61   AIC:                                      125.3\n",
       "Df Residuals:                      42   BIC:                                      165.4\n",
       "Df Model:                          19                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val        1.4215      0.406      3.498      0.001       0.601       2.242\n",
       "과속방지턱      -2.791e-16   2.73e-15     -0.102      0.919   -5.78e-15    5.22e-15\n",
       "무인카메라         -0.2718      1.133     -0.240      0.812      -2.559       2.015\n",
       "신호등            0.8006      0.475      1.685      0.099      -0.158       1.760\n",
       "버스아침승차         0.6086      2.629      0.232      0.818      -4.697       5.914\n",
       "버스아침하차        -0.3305      1.275     -0.259      0.797      -2.904       2.243\n",
       "버스저녁승차        -0.4762      1.312     -0.363      0.718      -3.124       2.172\n",
       "버스저녁하차         1.2093      3.322      0.364      0.718      -5.496       7.914\n",
       "관광여가오락        -0.5420      0.456     -1.188      0.241      -1.463       0.379\n",
       "부동산            0.5578      1.055      0.529      0.600      -1.571       2.686\n",
       "생활서비스         -0.2470      0.900     -0.274      0.785      -2.064       1.570\n",
       "소매점           -0.3899      1.487     -0.262      0.794      -3.390       2.611\n",
       "숙박             0.6636      0.486      1.364      0.180      -0.318       1.645\n",
       "스포츠            0.9568      0.755      1.267      0.212      -0.568       2.481\n",
       "음식점            0.7783      0.717      1.086      0.284      -0.668       2.225\n",
       "학교            -0.1288      0.660     -0.195      0.846      -1.461       1.204\n",
       "교육             1.1258      0.522      2.155      0.037       0.071       2.180\n",
       "출근시간자전거        0.3203      0.871      0.368      0.715      -1.437       2.078\n",
       "퇴근시간자전거        1.1533      0.747      1.543      0.130      -0.355       2.661\n",
       "지하철아침승차       -0.0127      0.206     -0.062      0.951      -0.428       0.403\n",
       "지하철아침하차       -0.0127      0.206     -0.062      0.951      -0.428       0.403\n",
       "지하철저녁승차       -0.0127      0.206     -0.062      0.951      -0.428       0.403\n",
       "지하철저녁하차       -0.0127      0.206     -0.062      0.951      -0.428       0.403\n",
       "==============================================================================\n",
       "Omnibus:                        6.569   Durbin-Watson:                   1.375\n",
       "Prob(Omnibus):                  0.037   Jarque-Bera (JB):                8.004\n",
       "Skew:                           0.367   Prob(JB):                       0.0183\n",
       "Kurtosis:                       4.615   Cond. No.                     4.25e+32\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 smallest eigenvalue is 1.37e-64. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = norm_new_final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
    "       '지하철저녁하차']] \n",
    "target = new_final_result\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 변수 선택법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "norm_new_final_data , new_final_result 결합한 데이터 프레임 만들고 적용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
       "      <th>무인카메라</th>\n",
       "      <th>신호등</th>\n",
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       "      <th>부동산</th>\n",
       "      <th>...</th>\n",
       "      <th>스포츠</th>\n",
       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
       "      <th>지하철아침승차</th>\n",
       "      <th>지하철아침하차</th>\n",
       "      <th>지하철저녁승차</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>다사612444</th>\n",
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       "  </tbody>\n",
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       "<p>61 rows × 23 columns</p>\n",
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      "text/plain": [
       "           2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차    버스저녁승차  \\\n",
       "gid                                                                        \n",
       "다사542434  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
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       "다사549419  0.326829    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사551417  0.126829    0.0    0.0  0.000000  0.217674  0.642015  0.247114   \n",
       "다사564435  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "...            ...    ...    ...       ...       ...       ...       ...   \n",
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       "다사613445  0.000000    0.0    0.0  0.181818  0.072777  0.291838  0.416527   \n",
       "다사613446  0.204878    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사619457  0.000000    0.0    0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "다사648432  0.117073    0.0    0.0  0.227273  0.129848  0.452690  0.423809   \n",
       "\n",
       "            버스저녁하차    관광여가오락       부동산  ...  스포츠       음식점   학교        교육  \\\n",
       "gid                                     ...                                 \n",
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       "다사549419  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사551417  0.430773  0.000000  0.230769  ...  0.0  0.014925  0.0  0.000000   \n",
       "다사564435  0.000000  0.000000  0.000000  ...  0.0  0.044776  0.0  0.000000   \n",
       "...            ...       ...       ...  ...  ...       ...  ...       ...   \n",
       "다사612444  0.000000  0.000000  0.076923  ...  0.0  0.134328  0.0  0.333333   \n",
       "다사613445  0.226660  0.333333  0.076923  ...  0.0  0.223881  0.0  0.333333   \n",
       "다사613446  0.000000  0.000000  0.000000  ...  0.0  0.089552  0.0  0.277778   \n",
       "다사619457  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사648432  0.210064  0.333333  1.000000  ...  0.0  1.000000  0.0  0.222222   \n",
       "\n",
       "           출근시간자전거   퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                               \n",
       "다사542434  0.146667  0.183732      0.0      0.0      0.0      0.0  \n",
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       "...            ...       ...      ...      ...      ...      ...  \n",
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       "다사648432  0.546667  0.350239      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 23 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_new_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "          acci_cnt\n",
       "gid               \n",
       "다사542434       1.0\n",
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       "다사549419       1.0\n",
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       "\n",
       "[61 rows x 1 columns]"
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     "execution_count": 39,
     "metadata": {},
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   "source": [
    "new_final_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.concat([new_final_result, norm_new_final_data], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          acci_cnt   2030val  과속방지턱  무인카메라       신호등    버스아침승차    버스아침하차  \\\n",
       "gid                                                                        \n",
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       "다사549419       1.0  0.326829    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사551417       1.0  0.126829    0.0    0.0  0.000000  0.217674  0.642015   \n",
       "다사564435       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "...            ...       ...    ...    ...       ...       ...       ...   \n",
       "다사612444       1.0  0.000000    0.0    0.0  0.181818  0.000000  0.000000   \n",
       "다사613445       1.0  0.000000    0.0    0.0  0.181818  0.072777  0.291838   \n",
       "다사613446       1.0  0.204878    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사619457       1.0  0.000000    0.0    0.0  0.000000  0.000000  0.000000   \n",
       "다사648432       1.0  0.117073    0.0    0.0  0.227273  0.129848  0.452690   \n",
       "\n",
       "            버스저녁승차    버스저녁하차    관광여가오락  ...  스포츠       음식점   학교        교육  \\\n",
       "gid                                     ...                                 \n",
       "다사542434  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사547430  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사549419  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사551417  0.247114  0.430773  0.000000  ...  0.0  0.014925  0.0  0.000000   \n",
       "다사564435  0.000000  0.000000  0.000000  ...  0.0  0.044776  0.0  0.000000   \n",
       "...            ...       ...       ...  ...  ...       ...  ...       ...   \n",
       "다사612444  0.000000  0.000000  0.000000  ...  0.0  0.134328  0.0  0.333333   \n",
       "다사613445  0.416527  0.226660  0.333333  ...  0.0  0.223881  0.0  0.333333   \n",
       "다사613446  0.000000  0.000000  0.000000  ...  0.0  0.089552  0.0  0.277778   \n",
       "다사619457  0.000000  0.000000  0.000000  ...  0.0  0.000000  0.0  0.000000   \n",
       "다사648432  0.423809  0.210064  0.333333  ...  0.0  1.000000  0.0  0.222222   \n",
       "\n",
       "           출근시간자전거   퇴근시간자전거  지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                                               \n",
       "다사542434  0.146667  0.183732      0.0      0.0      0.0      0.0  \n",
       "다사547430  0.170000  0.122488      0.0      0.0      0.0      0.0  \n",
       "다사549419  0.000000  0.000957      0.0      0.0      0.0      0.0  \n",
       "다사551417  0.003333  0.000000      0.0      0.0      0.0      0.0  \n",
       "다사564435  0.046667  0.043062      0.0      0.0      0.0      0.0  \n",
       "...            ...       ...      ...      ...      ...      ...  \n",
       "다사612444  0.110000  0.100478      0.0      0.0      0.0      0.0  \n",
       "다사613445  0.293333  0.303349      0.0      0.0      0.0      0.0  \n",
       "다사613446  0.210000  0.214354      0.0      0.0      0.0      0.0  \n",
       "다사619457  1.000000  0.800000      0.0      0.0      0.0      0.0  \n",
       "다사648432  0.546667  0.350239      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[61 rows x 24 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv(\"output/그리드 전처리 완료/final3.csv\", encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acci_cnt', '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차', '버스아침하차',\n",
       "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
       "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차', '지하철저녁승차',\n",
       "       '지하철저녁하차'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acci_cnt    float64\n",
       "2030val     float64\n",
       "과속방지턱       float64\n",
       "무인카메라       float64\n",
       "신호등         float64\n",
       "버스아침승차      float64\n",
       "버스아침하차      float64\n",
       "버스저녁승차      float64\n",
       "버스저녁하차      float64\n",
       "관광여가오락      float64\n",
       "부동산         float64\n",
       "생활서비스       float64\n",
       "소매점         float64\n",
       "숙박          float64\n",
       "스포츠         float64\n",
       "음식점         float64\n",
       "학교          float64\n",
       "교육          float64\n",
       "출근시간자전거     float64\n",
       "퇴근시간자전거     float64\n",
       "지하철아침승차     float64\n",
       "지하철아침하차     float64\n",
       "지하철저녁승차     float64\n",
       "지하철저녁하차     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-7508284c3728>:20: 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 = final[[ '2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스아침하차', '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박',\n",
    "       '스포츠', '음식점', '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철아침하차',\n",
    "       '지하철저녁승차', '지하철저녁하차']]\n",
    "y = final[['acci_cnt']]\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 = final[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 = final[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(final[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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다중공선성 문제 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cLnzSRW4mr30HDuvl79ypl6zr0MEjZwq/X9hR2zU8oJGxpY87PnZJv/fYUT181626NDOn9V1ZDfV21PztB/l8oJGxKY1O5NTfXd9zAFjq785M6IEvHtXbXr1ZzklBID3wxaN6WX+ntm9aG/fwUoV1gbQJe7ueyTjtGh7QDXt36uxkru5+Iw4LB6/veeSpQl917+6t2r1tQyo/NfbI6fHCspDme9B7HnlKW9Z3attG6iFQjyTtSy2uEafHc3ro8eO66zWbddOGHr20r9P0awsL25WVyi2TRrY71cwtyz0KGldv/V3Izfq3/ai+fPSc5vLSx75+XKfHc5H2RdQTe5KyzpJSO9m3qV4tyyopOUfzxVlL6u0BfMs3dSx6PuR2/NK0njk7pd/49A9y+NF/voMsoKwosst5NFhEbpEEpXrbMHpF3/pfNA+9AlBapVpYTb1t9FxwUo7Hwrbp6Tl9+snnlhyPSHpf0My5x3G9+FXT6/pQv8mK/+hzYRXZhUXkFlaRXfgqsRfdH39hqrATtSA3k9eJF6bknIpekH/D3p0anciteNzxsUu6NDOnWzevq2ss+XxQ8iJ/Du4B9Tv14iUdH7ukB77wzIrb2bhGi3WBNGnWdj2Tcdrc16nNfZ0hjrb5OHi91Onxlb1kbiavM+M5bdsY06AAw5K2L7W8Rpwez+m+R5/RH/z8K8zV/2Zhu7JSuWVS73anlrlltUdBYxqtv5mM0/MXLuu+R5fuI0bZF1FP7DnyXIl11tepbcaOLSShdrJvU71Sy+p0kWVFbUIt4qgljfQAvuWbOhaPuHP722/cVniDuzS/zk+ev0QWUFGzs8t5NFhEbmFdud42jF7Rt/4XzUWvABRXqRZWqrdhnQtOwvFY2JXPB/rG98+vOB6Rhr6gWXOP43rxq1TffanfZMV/9LmwiuzCInILq8gufJXMj9CT1LG6RdnWpS8v25pR++qWohfW52byOjs5/2lrxR63vitb91hGxqaKXuQ/MjZV93MCkK7uWF10vl7dsTqmEaUX6wJpwnZ9qXIHr9NosGdN0Xo40FN/LwmkWdJqLjWiMrYrK5VbJvVmKmlzC+ELIyNx1zzqiT2nxotfEHdq/FJMI0q3uOewJX2dbUWX1brOthX3pTbBd430AL7lmzqWHotze+ny7Ioctpc4Z0wWECXOo8EicgvryvW2YfSKvvW/sI2aC6sq1cJK9ZbzVUiCkbEpnXjhIn1BiDiuF79K9d2X+k1W/EefC6vILiwit7CK7MJXif2m+/7uNv36P75Bz09NKx9Iq5zU27Fa/d1tygfzE3DxDuHChfVDvR3av2f7ik9fG+rtqHss5S7y59M1gfq1r16l33rDTfre81OFeT60rkPtraviHlrqsC6QJo1s1/P5QCNjUxqdmP+gn6HeDpPf1LzYwsHr5X1VWg9eDw92697dWwuf9pttzeje3Vs1PNgT99AAk+Lelwq7blMjKmO7slK5ZVJvpqqdW0nsXVCdMOrvQj5/77GjuuPmDVqVkW7ZeJVu7O9uxpBXoJ7Ys+7KhcvL19m6jpUXLvsuCfWTvqV6mYz03juG9ZufPVJYVu+9Y1gtRT7yeLBnja7rXaM7bt4gdyUSn3niFLUJ3mikB/At39Sx9Fic2/a2lhX9xB997Zje//qthW+cK5eFJGzD4SfOo8EicgvryvW2OzZd3XCvWOnYE30FakHNhVWVamGlffO4zwUvRt1GvUYncoUP/GvWOam05ZPjevGrVN9rqd/NzC9Z8R99Lqwiu7CI3MIqsgtfJfai+2uvald7W4se/J/fWbIjde1V7cpkXMkL6zMZp13DA7ph706dncwVLsRvZAevvztb8iJ/APVb29GqubOBHvzysSXzfG1na9xDSx3WBdKk3u16Ph/o4JEzK/qPXcMDpk+EcPB6qZaWjHZv26At6zt1Zjx35YLIHrUUu9oCQEVx7ks1o25TIypju7JSuWVSb6aqmVtJ7V1QnTDqb0tLRq+76Rqtck6/9qlvR54j6ok9/d1teu/PDOs3P7PowuWfGVZ/j62L7pNSP+lbqtezZrU++c0T+u03btOl6VmtWd2ih752TL/9xu0r7ntjf5fe/o+26D2LLvx83+u3RvaBJEAljfQAvuWbOpYei3P7kS8/u+KDUN7yyut0x/CgXtZfPgtJ2YbDT5xHg0XkFtaV623D6BXLHXuir0CtqLmwqtJx+Er11pf31VK30Yj+7qw+9PnvrDge8f7Xh3NOKo355Lhe/CrV92rrd7PzS1b8R58Lq8guLCK3sIrswleJvej+xPmLhZ09af4T1O555Cm9YtNabe7rLHthfSbjtLmvM7RPyxzq7Sh5kT+A+s3Mqug8/8t37ox5ZOnDukCa1LtdHxmbKjxGmp8n+w4c1g17d0b+Cd1h4uD1Si0tGW3buFbbNsY9EsC+OPelmlW3qRHlsV1ZqdIyqSdT1cytpPYuqE5Y9ffk+KXCBfdStDmintizcW2HrrlqSh984zZNTc+qY3WLutas0sa1to6hJql+0rdUZ6i3Q//y1S+tqmaeHL9UuCBZms/Hez79lHZct9ZcPpBMjfQAPuabOpYOi3P75KkJrf7mCf3hv/gRjV+a0eCiHrBSFpK0DYd/OI8Gi8gtrKvU2zbaK5Y79nTs3AX6CtSEmgurqjkOX67e+vK+WvYH0YiF48P/7avP6rffuE256VltvLpdP7xxbSjnpNKaT47rxatSfa+2fkeRX7LiN/pcWEV2YRG5hVVkF75K7EX3oxO5JZ+gJs1PvLOTOW3u6wz9wvpyMhlX9iJ/APU5O1l8np+7kNNL1yf3gKKPWBewLJ8PNDI2pdGJnPq7K2+j692uV+pNLOPgNYBmqVRza63htUhy3fZdkrYrYWU07GVSTT/DHEi+cvkM61hW3DlKUj1Jg0zG6R9s7tOR0+M6PZ4rXCRn7Rhq3LlH9DIZp9fe2K+H77q1YnbJB3xXSw+wvJcg34hLqTpc6xvbyTCaifNosIjcwroo3qu1+NjT4v740swcfQVqQs2FZZmMU1e2VRen59SVba2pzvryvlr2B9GIQo4Huq7kuLvq42lpf78Z/FbuPGu19btUfr87OilJXEuRAvS5sIrswiJyC6vILnyV2Ivu13dllW3NLJl42daM+jqzsYwnyov8gbTwbZ6nGesCVuXzgQ4eObPiU1d3DQ9UvPC+1u16f3fxebK+i3kCAOWUqrn11vDlz1HqpDZ1G41qJKPN/ECJBZX6GeZAsuXzgR77+1E9eXJc+UBa5aSbru3RbS/vX3LhfaPHssgRapHPB/ri0bOFXD59ekLnLlxekksLyH361JJd8gGflOo5q+kBivW6H/mFHeQbscjnA33+6dGa972Wz4FS5znIMMLAeTRYRG6RBMt723w+0LFzF0I/7rq8P7779uvpK1ATai6squZcQyU+vK+WY3YISxCU/l29547JJ2oRxfsMFlRTv0vl99unJvTvHj5c83t8YA99Lqwiu7CI3MIqsgtf1fYR/4a0rJLe+zPDyrbOv8Rsa0bv/ZlhtayKeWAAQsM89wfrAlaNjE0VTmZI85+Kte/AYY2MTYX+tzatbde9u7cumSf37t6qTWvbQ/9bSKf83JyeffZZPf3003r66ac1Oztb9H6zs7OF+5S7H+C7Rmv4wkntn77vK/pnH/kb/fR9X9HBI2eUz8+fCR/q7dD+PduX1O39e7ZrqLejOS8IiVNvRitlMyrMgWQ78cKUjo5e0INfPqb7H3tGf/DlYzo6ekEnXgi3DyZHqEVUuWw2cp8+tWSXfMAXjfacxXrdez79bX3gZ28m34hcPftexebA98YuUKPRNJxHg0XkFknTzOOuy/uRA4dO6u7bt9BXoGrUXFjFMV2g+h6j3nPH5BPV8uV9BosVy+/e27boz795sqnv04Q/6HNhFdmFReQWVpFd+Cqx33R/Zvyyfv9Lz+htr94s5+Y/QfD3v/SMNq/bput6+bZ5IAmY5/5gXcCq0Ynckk/FkuZPapydzIX+Kdonzl/U7z12dMk8+b3HjuoVm9bG+ondSI6LL5zR+z99XL3XTurCuVP6g7f/E914440r7nf06FH90gN/qc6+DWXvB/iu0Rpe6qT2DXt3anNfpzIZp13DA7ph706dncxpfVdzPwUcyVNvRitlMyrMgWQbnbisDz96dEnOPvzofG86tC68nJEj1CKqXDYbuU+fWrJLPuCLRnvOYr3u8bFL2nBVVp8j34hYPftexebAOz7+LR28eycZRlNwHg0WkVskTTOPuy7vR06P5/TQ48f1R7/4SgUK6CtQETUXVnFMF6i+x6j33DH5RLV8eZ/BYovz+93RSX371IQ+9vXjOj2eK4yxGe/ThD/oc2EV2YVF5BZWkV34KrEX3U9Nz+r42CU98IVnltx+cZpv8gSSgnnuD9YFrOrvzuq63jW64+YNclfORXzmiVNa35Ut+7h8PtDI2JRGJ3Lq767uZMboRK7oPOHAce3qWf5p0b5uUN2DQxXv19m3oar7AT5ZPvcHe7LKtmaWnJjOtmYq1vAFpU5qj07kNJmb0enxnAZ71mh4sJs6jbqs71qa0cGerN6041pdnJ7TsXMXlmy/Fuf70sxcZB8KVEkm47S5r5M5kEBT07NFczY2dVnHzl3QprXtOnH+YtX9Vrn+jByhWqVyafnYQhDfl4cgQvVml3wgTuXe5LtpbbuOnB5fsk/U0pJZct/+7uL7Y1d3tLHdR9Mt7z2X73vdvKFbd73mpXphalpPfP/FohkuNQfOTOR06+Z1ZBih4zwaLCK3SILZ2Xyht12VcUW3/98dnZSkhs43FuuPz1+cVl9XG30FqkLNhVVJOqbLuQzUq5qL6fP5QO2rV2nv7dcrH0if/MZJnR7PVf3+hlrz6cP7qnwYQ9pUm8Wo18tCfiXp3z18uO73+JRC1hrT7OVHnwuryC4sIrewiuzCV4m96H7jVe1F3/Bz7VXtMY4KQJiY5/5gXcCqTWvb9c7btuieR55SbiavbGtG9+7eqk1rS2c3nw908MiZwifDZlsz2r9nu3YND5Q94FjqzciNHjhOm3qXPwDbSs39+++8Re/4+LeW3DbU21HVc5aqy7mZOf3if/8/S7YLu7dtWPEGfaCcfD7Q98Yu6O7bt+jDjx7V2vbVeuuPXVf4to/F2y9JS/J99+3X0zOg6a67uqNozo48N6l9B57Qvbu36vceO6rjY5cq9lv0ZwjLxrUlji2U2T/zEXMifWrJLvmAL5ZfpCzN57a3o02PPHFqxbGy5ftEQ70d2r9n+4osV7s/BtSrWB29/85bCnl82fpO/bMfvU6//GdPlM0wx2oRNc6jwSJyC+tmZ/NLettSx12/fWpC/+7hww3tm9Efo1HUXFhFdoHKxxiKHcvYe9sWPXzohN6968bQ+wUfjkH7MIY0qieLUa6XZvTMcb8m66JYfvQKsIrswiJyC6vILnyV2Ivup6Zn9ev/+AY9PzWtfCCtclJvx2pN8UkXQGIwz/0xNT1buKBo4eDL3bdvYV2gYc3+JM0T5y8W3mghzX+66z2PPKVXbFpb8tOBR8amCgcaFx6z78Bh3bB3Z9lPFB7q7dD9d96iJ0+OF2rWTdf28GaLGtW7/AHYVmru/+U7d+pze3fq7GRO67tq204UO6H3W//0Jv3HzxxZsV3Ysr5T2zaubdrrawSf2u2nkbEpvePj39La9tV626s364aBLv2HKxd9DPZk9YZXXKvvnJnQhqvWqGdNy5J8Hzh0ckVvzRs0EZaFmnF2MqeP/MIO3fPpbxcurN972xZ97OvHC7Xvba/erAe+8EzFfov+DGG5PDtX9DjP5dm5uIdWE+ZE+lyenSt6XKxYduvJB/0emmFVRsWP516eLXqsbPk+USbjtGt4QDfUuT9WCnlHJcXq6Ds+/i0dvHv++MDzk5f11j/824oZ5lgtosY5TVjE+V80S1Q935HT40t622LHXRcfDyu3b1ZpzM3qj5Ee9Aqwqpp+gX19JF2lC4mLHcu477GjeviuW3XThquUybhQ54kP5yh8GEMa1ZrFte2r9Z0zE8q2ZjTU29H0+tyMnpmsNSaK5UefC6vILiwit7CK7MJXsV9075y7RtLvSuqW9GtBEHzryu09kj4o6XpJ9wZB8Ggtz3v2wmVdmsnrwS8fK+w8/vufeJnOXbgc8isAEBfmuT9OvnhJDz1+XG979WY5JwWB9NDjx7Xp6nZt3+TnBWrwXxSfpDk6kVvyqVjS/MHDs5O5kgcO63nMgunZYEnN2r9ne0PjT6NGlj8Au0rN/XMXcrp187q65n+xE3ojYxd0fOzSir9zZjynbRsbeglNwad2+2shs6fHc3rgC8/oHbddX7jg/hduvU73PTb/5qMHv3xM//mf3qS17at1ejwnSTo9ntNDjx/XH/3iKxUo4A2aCE2xmvGBn71ZXdlV+sbxcX3s68cLOczN5OUWRa5cv0V/hrCcncwVP84zmYt7aDVhTqTPyfPFj4ttXtehrRuuWnLfWvNBv4dmWeg5l+d249XtRTNabJ8ok3Ha3NcZWm0j76hGqTp6ZmL++MB3RyerzjDHahElzmnCIs7/ohmi7PlOjy/tGxZ64I+8dYemLs/q26cmVhwPK7ZvVu2Yw+6PkS70CrCqUr/Avj7SoNKFxKWOZVyamStccB/mPPHhHIUPY0ijWrK4/H0LUdXnsHtmstaYKJYffS6sIruwiNzCKrILX8V60b1zzkl6RNIxSS9IekjSTVd+/VFJnZK+I+lh59w1QRBMV/vca9e06sG/O63ffuM2Xbo8q/a2Fv3R147p3btulCRNT8/pyefG9cLFaa1d06qZ2UD9PVmtysyfaFn4tEBJdX2C4OJPHmxf3aLpuTn1drSVfc5in1YoSSdemNLoxGVNTc/qurXtWrXK6fR4ToM9Wc3OBTpx/qI6Vreov7tN117VrhPnL1b13Auvo97fpRXLxB9r17Tq6cuX9Ue/+EqdnZxfH48fHdVVa66Ke2ipM9id1QN3btXsXItGr6yL22+4SvmgNe6hwbAoPkmzvzur/W/8IV17dXchuyfHJrS+KyvpB/3CmYmcBruzuumaHvV3Z5VtzSw54JhtzRQeMzub15HT41e21Ws0PNitlpaMRsam9IGDTxdONkrSBw4+rRsGuup+PaX+ViN83871d2d1Xe8a3XHzhsJy/MwTpwrLH4DfXryU03fPzO/f9He36WUDHbpqTeX5W6n21mv5Cb3J3GzRvzPQ42eN4VO7o1FPbktl9g2vuLZw4lqaX2e//qlv667XbNZ9jz5TuO/5i9Pq62pjPaIhy7M70NOyoma8+5NP6uG7btVHv3psRV6DQEv+XarmNqtGI326s6v17Nnn9N/+xY/o+cnL6utq06e+eUI/fJ2tizn6u7Pa88OD2v2KTUteh8U5UWyfePXqVXEPq2bN2HdebLBnjd7549fp+v61hWMLG7pXFe0ha81Hrf2e7/v05Vgeu0X93VndtKFTr3zJWp2fmtHVHa06OvqiBruzeu0PrdPP3fqSwu1//PXvaePVa/S33xsr9BXDA51ycnrqzGThtq0DXXJy+vaZicJtNw10q62tpei5r+W31ZL3aue15VxZHnsz9Xdn9a/+wUb9xPAGnZvMaX13Vgrymsvn9bffG9PMXKA//b9eqUzG6cWpGV3VvlrPvXhJaztW69mzL+r4Czl1Z1drNh8UzduGK98yJ9V3fhgohXOa1UtKDxqFZm8rBruz+n/ecIPWtGYLfe4rhzrV0WZv3yYKZLc6UR3TnryU09Udq/WBn71J7atb9Off+L5+aMNVWpWR2loyujzrtG1jj9a2t+jFS/PfmHR2fEoZ5/SZJ55Tf3ebsq0Zdba1KghUOMfb1pLRS9Z16OQLU/rG8Re0daBb333+QtG+tNR7wBbfdm3PGj09OlG2r61mrtdSDxqtHWHVHvrdpegV0CxR9Atve9W1unnjukK/sDY7v+8mqar3yVSzj9/o8b2wa2mz+DAGH0SxHJb/jU1r23XyxYsauzCtTEa6PJvX8xemta5ztXqyLbowPafcdF4z+Tl1Z1dr4tKMeta0qnWV09T0XOE55vKBnjl7QSfPX1RPtlUvX9+ljraWFe9z+vqz57SmdZUef/Z5ta9u0QcOPr2iP+p/24/q6o7VOlNiORR7j3pfZ5vaW1dp7+3XKx9In/zGSZ0ezynbmtHsXKCjo5N6aV9nyeepZnlXc/+0njuMOruDPVnl84FOT+Q0dXlW67vadHF6TjNzeWVbW/TsuQu6kJvRpZm8xqYua0PPGr3njhv1wsUZvby/Sx/8/HdW5O7l79ypl67vLPl6pB/0s+s72zSdn9PzF2aUm5nT9es6lJcr9DKL7z82dVltqzJ6/sK0Otrmr3fYdHXl6xsqLeP1XdVlrdzfWLwv19/VprYr+wFhzIVaxLENiGKu0ufCKrILi8gtrCK78FXc33T/akmTQRC85coF+Medc92SrpK0VdLWIAhmnXNbJd0g6clqn9i5QD/7ik36lT97ovBJF++9Y1jOBZqentMjTz6nB75wVG/esWnJJ7XdffsWPfT4cZ2/OK3777xF07NBzZ8gWOyTB/fetkUPHzqhd++6UatbnN7x8W8tec7X3tivzz89uuJvdbRl9HfPTerDjy4d4//89mn945sGV9x+7do1+uDn/17Hxy5VfO5dwwOSVPJTEsv9Lo0H8yS+acU33WtWaXBtp/75H/5tYX2873XD6l7DyeuovbS/XZ9/6pze8xffXLQutuq1W3viHhoMi+KTNK/ubNFs0KK3LqkjW3V1Z0uhX3jPp5/6we9ev1X/ZHi93ve6rXrPXzy15DHXdGU1O5vXI0+c0j2P/OB39+7eqt3b5t8Eurzv2HvbFj1f5+sp97fqvXjAwnZu09p2vfO2LSte96a17XEPDUAFL17KXekXnlrWL/RVvIB5qLdD+/dsX1GfFk7ShWV4sFv37t66osYMD/rZ0/Cp3c1Xb26XZ/YzT5zSvbu36sQLF4uus5f1dxVOZjYr30iXUtm969XX6b4vfK9wv9xMXhen51bU2Ht3b9XvPXZUkipmMqoajeRra5F2DK3Tv/zv/2fJcZ62uI9g16ivs0U7hvqWvY6t6uu09UJK7RPvvvkaUxeONGPfebnr1mX192faVhxbuG7dmhX3vbpEPq4ukY/TL14q2jucfvFS3d++6CPLY7dqXWeLfuLGa/RLH/vGkiy+tL+96O2jkzn9mz/+wXmt337jzZqezS+ZW+9//Va1tWb0K3/25JLH9nev1i/98TeXrNti58muyrYWzfvoxNL9m2rnteVcWR57s/V2tuhlA2v1LxbV3B9k7we53feTL1Pbqozufvjwkjy2tUj/9k++pX+1c3PRvH3l6PM69WKuaEZZ/mgE5zSrk5QeNApRbCvmz/9O6T1/sbTPfe1WzscsR3arF8Ux7clLOf3PZcfFfvN1w/rEoRM6dHxcD375mH5t1w26PJfX/v/1XeVm8nrtD63TT9w4qLf+t79d8phHnz6tN/7wdUXP8f7ynz2ht//4Fv3108/p83/3/JK+NJNxK+Zosfegve/1W/XAF44W3uO1vK+tZq7XUg8arR1h1R763ZXoFdAMUfULz55rX3Fc7Pr++X6h1Ptkzl2p+9Xs4zd6fC/sWtosPozBB1Esh+V/47reNfrl175c56em1d62SjNz0m9+5kjh73/gZ2/S8xem9dDjI7rzldfp//3rb2lt+2q99ceuW/L+7fe/fqvuX7Rdv/v2LTp+/qKcAv3rf3h94Tmv612jt/+jLXrzg19fMi8+9vXjOj2ekzTfH33p6PyF+QvvZ1+8HIotp+X9zeL3rb/lRzbpP/zZkzp/cVofetN2/eOtpZ+n3PKu9v5pPHcYdXbXtq/W2//RSzWZm9WHHz2qte2r9Yv/YEh/8jfH9eYdm/TwoRN6+z98qZ6/MK3f/MyRopktlrvvjE7oJevm11M1/ex7f2ZYv/+lZzQ9G6x4/oXjwO//7N9dmTs/yObdt2/Rlv5O/fiW9SWvb6iml73/zlsqZq3cupmdza/Yl1vYD9h9y6aG50I96zbKbUAUc5U+F1aRXVhEbmEV2YWvwvs6l/q8RtKfSlIQBIGkE5LaJO2U9GdBEMxeud/xK7dXLR84/eZnjxRO1uRm8vrNzx5RPnB68rlxvefTT+mOmzes+Ia5Dz96VG94xbXKzeT15Mnxop+wPDI2VfZvF/tk5vseO6o7bt6gfQcO68mT4yue88jp4n9r8tJcYSd08Rj/1WteWvT2o2cv6I6bN1T13CNjUyU/RbrS79KKZeKX8Utzes9fLJ3n7/mLIxq/NBfzyNLnu2emCietpYV18ZS+e4a5gfotfJLmYmF/kma57C70C0t+9+mndOTMBT3wxaN626s36x23Xa+3vXqzHvjiUT11ZkJHTo8XTvQtPOaeR57SkdPjcs6t6Dvue+yoCh9jXKNyf6teFrZzJ85fLPq6T5y/GPPI/DU7O6unn3668PPss88qWPzVtZ5ZPt7Z2dnKD4IJjfQLmYzTruEBfW7vTv2Pu35Un9u7syknl1paMtq9bYMevutW/cHPv0IP33VrqBdkhS2KbWXa1Zvb5Zn9w3/xSu3etkG339BfdJ3dONDd9HwjXUpl99Vb+pfcL9uaUX93dkWN3b1tg/7wX7yyqkxGVaORfJdmgqLHeS7N+Nu7FvN0ifn3tLFjJKX2iZ98rv59zjg0Y995uVr6hVp7i9aWTNHeobVIf2phn74Uy2O36jtlsljs9lUus+S2Z85eWDG3fuPTT+mZsxdWPHYyN7di3RY7T9ba4ornfdXS26qd15ZzZXnszfb3RTJaLHv7/9d3NXZxemWWM6sKtxXL26bejpIZZfmjEZzTrE5SetAoRLGt4Pxv9chu9aI4pl1sv/y9f3FEb33V5sK/xy5OFy5Ik6Sfu/UlK+r0e//iiH7u1pdoejZf9BzvHTdv0Hv+4in93K0vKdy+0JcWm6PF3oO28N615Y9fUM1cr6UeNFo7wqo99Lsr0SugGXzoF0q9T8ZdeZ9MNfv4jR7fC7uWNosPY/BBFMth+d+44+YNOnr2gp6fmla2paVwcfzC33/23JR+56/+XnfcvKFw0fAbXnHtivdv/8ay7fqHHz2qZ85ekFNmyXPecfOGFb3jfY/Nv299QbY1o7m8lryfffFyKLaclvc3C8/7H157gx56fP7C6txMXu/6RPnnKbe8q71/Gs8dRp3dN7ziWp2dvFzI4Rteca32/6/vFq6LuOPmDWpvay1kr1hmi+UuyKvk9QPF+tnf/MwR3XHzhqLPv3CMbfHcWfjdhx89qidPjpe9vqGaZfyOj39LPzTYVTZr5dZNsX25hf2AMOZCteLaBkQxV+lzYRXZhUXkFlaRXfgq7isHNkp6atG/eyS9UOT2Xklnlz/YOXeXc+6Qc+7QuXPnlvxudOJyYcItyM3kdXbiss5c+eRk51T0PgvXvuWD4r8/O5kr+6JKfTLzwt/LL3ufaG4mXzigsfz2qcuzRW+/NF389nyw9Nq9cs99djJX9lOky/0urcJYJuVyi9qUmuejE5djGlGy1VNzWRdoxMInaS688aKeT9KsVHPLZfdMiZo/OnFZx8cu6YEvPKP7H3tGD3zhGR0fu6TRiVzJbe6Z8ZzOThb/W+cm65sn5f5WvSxs+y2MMQxh9gtHjx7VLz3wl3rXgcN614HD+o8f/5Jyl/1dXovH+0sP/KWOHj0a95BQpUZqbjUyGafNfZ26dfM6be7rbNoJ2ZaWjLZtXKuf2jqobRvXenvBvRTOthLN63OXZ7alJaObNvQUXWcvWdcRSb6RHHXX3Mlc0ZpRLK+1ZDKqGg370nBsISmvo/Q+sb/7EsWEse8cZp9baz7OX5zR3tu2LKnde2/bovMXZ4o8t939Zctj91m9NbfY7cszV+r8WbHzX1PTs1Xd7/yl4nmfzE0vuW+189pyriyPPQz1ZLdYpsrl8ZPfOFk0b8+9eLHkY9Oy/FGfZh8XS4uk9KBRCGtbkYZ9tCiQ3erFef730qK+dHk/e35qpuhjXrw4o2PPT5V9D9iLi3rlhb602Bwt1UMvf4/X4r62mrleSz1otHaEVXvS2u9ScxG1KN7nWCm7ld4nU80+fqPH98Kupc3iwxh8EE2fu/RvODe/nc4HKvp+7YVt+OL3m1d67/nCv4s9Z6nHLnzu5MIxij//5sklz7l4OdTSa3xndLLwTebVPE+55V3L/dN27jCamptbksHF63whV4v/uzh71ebu5IsXS14/UK6fLfX8C9c0lPpduesbyr3+xfc9M5Erm7Vy66bUvtyLF2dCmwvViHMbEMZcpc+FRRzPhVXUXFhEzYVVLTH//RclXZAk51ybpKkgCOacc4Xbr+iXdGL5g4MgeFDSg5K0Y8eOJW9/6O9uU7Y1s2TiZVszWt/dplXOLTmBs/w+C1/8ucoV/32lT1he+GTmYs+bbc1o+f5ItjWjwZ41RR/TkW0penv76uK3Z5w0l6/uuRdeR72/S6NS67aWZVIut6hNqXne390W46iSq56ay7pAIxY+SfOGvTt1djKn9V3ZwgVB1apUc8tld6FfKPa74rdn1boqU/R3Az1ZXZ7NF/1dX1d986TU9n2gp/7tdBjbuWazMMYwhN0vdPZtUPfgkCTpwrlTjT5d0y0eL+xopOaiPmFsKxFtn8s6Q1jqr7lZfY78IUZpOLaQlNcxWGLfq7/b1r5XGPvOYfa5teZjbXurHj50Qm979WY5JwWB9PChE/qdN24r8tx295ctj91n9dbcYrevbW9d8tylzp8VO//VsbplxW3F7tfbvlr/qUje73vLLUvuW+28tpwry2MPQz3ZLZapcnk8PZ7Tx75+XHe9ZrM29KzRifOX9PChE7rj5g0lH5uW5Y/6cFwsHEnpQaMQ1rYiDftoUSC71Yvz/O+aRX3p8n726o7Woo+5qr1Vx84VP8e78B6wqxb1ygt9aVd25fOV6qGDRa9geV9bzVyvpR40WjvCqj1p7XepuYhaFO9zrJTd9V3Ff7/wPplq9vEbPb4Xdi1tFh/G4INo+tyVf2PVlVak2Pu1F7bhC2NZ+F012/WMK/6cxf698/p1kubf//2xr89/M/3i51y8HEq9hmqPmZR7nnLLm5yWFk3NXfo3lq/zxTmVVmav2PiuX9+ld9x2feFY7Ou3byh5/UC5ftaVyV8+KP27Stc3lHv95e5b7eNaM8Xf63lVe2ukc8H63KLPhUUcz4VV1FxYRM2FVXF/Zd8XJW2/8v93SvrElf//0sLtzrl/JOl/B0FQ08VGLxvo0Ptet3XJTuT7XrdVLxvo0E3X9Oh9r9+qzzxxasW3GNx9+/wnBGZbM7rp2uLfQFfpE5aLfTLz3tu26LNPntL+Pdt187U9K55zeLC76N/qyq7S3bevHONHvvxs0du3rO/UZ588VdVzD/V2lP0Uab41cSWWiV/KzXNEi3WBZmn2p95W0y8s+d3rt2rrQFfR22++pkfDg926d/fS3927e6uGB3t000B30b9100B3XWMv97fqZWE7Z2GMAIqjX2iOtH1CfNSakVvWGaJQLrvkD75KSq/wQyVexw8Zex2l9olvvqb+fc44NGPfeblasltrzm8a6Nbbf3yLPvrVY7r/sWf00a8e09t/fEvRYwmW95ctj92qG8tksdjtc/n8ktteur5zxdx6/+u36vr1nSse25VdtWLdFjtPdtM1PXrnbUvz/s7btqyYr9XOa8u5sjz2ZruhSEaLZW/fT75Mve2ri2R5rnDb+YvTWtO6Sr/76FF99KvH9OYdm8qey2X5oxFJ6XWbLSk9aBSi2FaQ2+qR3do0+/hosV73N183rIe+dqzw76vbV2vfT76scJ8//vr39L7XDa94zJ98/XtF3zu28B6w971uq/7k698r3L7Qlxabo8We532v37rkPV7L+9pq5not9aDR2hFW7aHfXYmai2bwoV/YOtBV9PdbB7okVbeP3+jxvbBrabP4MAYfRLEclv+NzzxxStev71Rvx2rlZmb13p9Z2hNs7uvQf/ipl+szT5zSv/+J+f7hk984ueL92+9ftl2/+/Ytun59p3Izs0vu+5knTq3I9P4927Vtw1XadHW7PvrVY4UL7he/n33xcii2nJb3NwtjWtex9PjIh95U/nnKLW9yWlrU2f3kN06qr6utkK1PfuOk9v3kywrXRXzmiVO6mJsp5LlYZt/7M8P60Oe/UzgW+5Yf2aSbr+0pef1AsX72vT8zrM8+earo8y8cY1s8dxZ+d/ftW3TztT1lr28IaxmXe1yxfbmF/YAo50KS5xZ9Lqwiu7CI3MIqsgtfuRqvZQ/3jzvnJP2WpBZJlyR9WtLrgyD4Defcf5B0jaS8pPcEQTBV7rl27NgRHDp0aMltL17K6btnpjQ6cVn93W162UCHrloz/6lf09NzevK5cZ2/OK2r1rRqZi5Qf3dWqzLSmYkffJqyJI2MTdX8Ccv5fKCRsSmNTuTUvnqVZubyurqjrexzLjxm8e2SdOKF+ddwcXpWG9e2q2WV05mJnAa6s5qdC/T98xfVvrpF/d1tuvaqdp04f7Gq5154HfX+Lq2qXCZVLaRiuUVtys1z1KXu7LIuEKOGam41/cLoRE793VndfE2PVq9eVfJ2SZqdzevI6XGdGc9poCer4cEetbTM7wRcujSjb5+ZKPytmwa6tWZN64oxVavc36qXhW2/hTFWqaHsPv3003rXgcOFb4R/7sn/rVVdV6v/JTfqxZPP6h2vvEovfelL9eyzz+qBQxPqueYlZe83OzsrSWppmf+mj+X/3rJli1paWjQ7O6ujR48uGcvC78Kw+HVNnB7Rh/Zs14033hjKc9dj+esN87X6osbX2LSaCzQZfS4soubCqtTX3AuXcvq7Ra/jhwY61GnwdZTb97Wkhn3nitkNo+bWmvNajiVY3l+2PPaY1V1zJy/l9PSiLN440KGuNVldvDStp85MFm7fOtClQIGOnLlQuG14oFNObsX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wyTNxcQP6buUyt2VNd3zlkYPLVgdq3Ua0er2px/LXOrfVlHnmc9b1dsUPj5yM6z78zVm5LJXSqjPV6nlpNeWyu/fI6ThvTddkP2Fdb1esKERcuWX1suRhdLQUf/nw/njy2Olpd7n50GuuiJ/Zmd1+rrrSOAcGy+8zXXHB6ti2Pj9jC4VCEoWkEB/7xhPTcp+3DNVj36ZZlEpp9K1siw/euCuGhkejp6Mt2tuSihMZOtqSeONLt0UpjSgk47/Ppdrn57n9OjhYeWxx27reBpWq+S00i1Mt5W5ac2W1mrG4Sp/9sks3TBsPOb9/ZXzhwadz2Y6pE9B8DgzMcUwzR33d5Zbn/kwzcvyX5VCPej7X/uzEZx8cLMbwWCke3T8Yr37h+VFIIkppqWw/bO+R03HhOSvjL37t6jh8sr5t01K/L3fBzQ/nP5FXhwcrHEc7cSa2b1gVpVIapbQ0bfyhlJbcBbkMfeHGmWt7WWmdzHzNhWtXxsd+/qoYK5Wiu6MtejpXxOmRsXjPqy6Lt3/+wdh75HTF7fBi1n2zjx05PlGdctn9yOuujC/d/JL40bFT0d3RFhv6OmPLOc/m6bodG+N5v3Z1PHJgMJ4+dipu/qmLY3isFFddeE785La1s3I3V/1oX5E0PIeykn36ueSV7JJHckteyS5Z1bST7h/ePxC/8if3Ttuh62ovxGfe+KJY1dU+6yp/jx06EV3thVnPn7iK8SW3XD1tJ7DSlQJnPm/iysXNMJABWdPeVohjp4bjo197fPKxrvZCtBssqbv2tkLZNtS6YKmWezs63/a8ra0Quy5YE7suWPr7bejrKttmrV/VVXW/gux7eP/A5EB+xPi6fNtdD8X29b2x64I1NfmMzilt7v6BYnz0a49HV3sh/vlz100e/Jir3sxVxnL95HpmUd+5uXVW6i+saFx/YWbmnjh8clnrwHK0Ea1eb5Z7+Zcjt9WUeepznjh8Mm7+1L1lc3miOLKgTLV6XlpJpewmMTsDW89dnjw8vH8gHjt0cnLiccR4Rn/9s/fFpZuy3c9VVxqjs9I4TwP7Coux58hQ/Ppn78td7meqx75Ns3jg6YG49U/vm9Xm/skbfiKuOnthtAl7jgxN265PPPfLFfqbC31+XtuvDX1dZbdb61d1NbBUzW2h2Sr3+qXsO1XKajVjcZU+e6LsE+95/4+O5bYdUyeg+TimWb289meakeO/LJflrufz7c9uW9cbJ4oj8Qsf+8dZ+f7/3vATZXN/3uru2LauN56zvv5t01K+L8eg80Nfgbxqb0sqHEcbP2/h4f0D8Zufe2DW37ec0535ffNG0BdujPm2l+XWyczX7D1yOt74yd3TxtaeOHwy/uXHv1nVdnih677Zx44cn6hOueze/Kl748u3XB0/dcmGsq8pFJJIkojf+Oz9VY0Nz3cuZKNzKCvZp59LXskueSS35JXsklVNO+l+/0Ax1nR3xA0vOD+Ssxdeu/M7T8aBgWKcGh6b/NuqrhWxeXV3PHXsdPw///qF8c4vPDR5VcFbrtken/z7vWWvvNaoKwWWSmnsOTIUBweLsaHP1SxpbcdOjcS7X/H86O5sj6Ezo9HT1RaniiNx7NRIo4vWcqwL8urgYDFefNE58W9eclEcGxqJc3ra44+/9cNFb8/n6h/8+Na18ZHXXRkPPDkQpTRiRRJx2fn9sXVtT/zDD480/Mqv1Mb+gfIZODBQrPriDfMZKA7H+2/YGSsKKybb3LGxsThRHF5yGU8Nj8kiy2ap2a2H5d7Pq0cbQW1lIbdz5fL4qRGZoqwsZHf/QDFKaehbULWB4nD8x395aRw+eWZyn+nc3s5M9RWqcXCw/Lh03nKv31K9AxXW+cHB4qznLjQfzX7Hoglb1/bEbTddMetuOVvX9jS6aE1rrmxtXdsz6zhUREx77MhQ+TvZHR06M/n+y3UMq9p6ked2TJ2A5uM4WvVGR0vx8P6B2D9QjE39K2PHpj53p2sQuSWvqukHVnrOYHG4bD9sy5rueOLwyTg4WIzujrYYHhuLtT2dcX7/ynjk4OCi26zlPgesVfYpm4E2l7w6dnokbrlme9z+1ccm281brtkex0+PZzdP++bOy82Oeq+L+baXU8uzqb8rxkoRjx06Ef/+p7fH6FgaxdHx184c513O7XC1Y0d53b/KU9vRSNVkrFx+f3D4RHzgxl3x1PFTcfLMWNz5nSdj/0CxbDbnOxey0WOYtchKXutJXujnkleySx7JLXklu2RV0066v+CclfH6F18YH77n2QG9W6/dHuevWRk9nW3x+hdfGH/6j/vitVdtid/83P2Tz3nfz14Wh08UY6A4Fp/8+72xf6BY9sprjbhCW6mUxt0PH5i1g3rdjo0G+GhJ5/V3xtGh4fiNKXX4na/YEef1dTa6aC3HuiCvNq/piusu2xS//MnvTGb3Xa/cEeetXtz2fK7+QamUxvFTI5N3+uxqL8R7r98ZpVKaiSu/Uhub+leWXZcb+2u3Li88pyu+s3ck3vGFZ9vcd79yZ2w5p7rPmKuMq7raZZFls9Ts1sNyt8f1aCOorSzkdq5crmxvkynKykJ2N/WvjEf2D+pbULVN/V1x+MTwtH2md71yR+7atE39XWXHpTf25W05yvdbNuRsOerhgtXlj0VsXr1y1nMXmo9zezvLroe1Pc015lYoJHHdjo1xyS1Xx6ETxVi/yonNy61SH3NjX1fZ41AdbUnc/Kl7Jx97/6svjwvXroy9R05Pvv7CtSvjqePF+Ncf/1/LegxrfYV6ce6MepHn/S91ApqP42jVGR0txV33PzV5l7qJ4yjX79rshPcGkFvyqpp+YKXnnNPTGZdtXj2tH7ZlTXd85ZGD0/rIt1yzPT6ze1/86k9tj49+7bHJm7sspM2qxzlgjkHnhzaXvFrf2xG/8xf74g0v2RZJEpGmEZ/ZvS/+4LVXRER+9s2dl5sdjVgX853rNVGeNd0dk2O7E///yNcerzjOu5zb4WrGjvK8f5WXtqPR5stYpfxO7dN+8f6n4udfdGF8Zve+stmc6zOyMIa51KzkuZ7khX4ueSW75JHckleyS1Y17R7B6Fg6uXMYMX7lsg/f81iMltIYK0V8+J7H4uWXb568yubEc37rzx+MSzb1x8e/9cTkhPtyV16buFJgV/v4V1iPK7TtOTI0OZgUEbGmuyO+d2Aw7vnewfiHJ47EnmdOxuhoKZ44fDK+/YNn4onDJ6NUSiNifOe53OPz/Y3ZfF/ZURwpxbu++PC0OvyuLz48efVQ6se6IK8ODQ7Hf/n64/GGl2yLm6+5OH7p6m3xX77+eBwanPtOiqOjpbj/R8fi7of2x/0/Oh6jZ7M+V//g4f0DkwO0EeP15G13PRQP7x9oSL9iMSptAyt9H61ox6a+eO/1O6ety/devzN2bOqv2WccGRqLd3xhepbe8YWH4sjQ2LzrYHS0FB1tEe9+Vfky5iWL9aLfV1tzZbca9Vgfy10H6tFG5NnEOv67x5+J7+w5Gl/93oGGb1eWmtupFrtfvnVtT3zoNdNz+aHXjOeyGTKlrV0eC83uzPVQaXxpIXZs6ouL1/fGrdduL5vfemhEP1WmF+/MSBrv/ML0sYV3fuHhODOSr+9wYux55rj0WM52kzraIt75ih3T6u87X7EjOtvzd5LpctfL7s62+NN/3DdtbOFP/3Ff9HTOvubxQvMxdGZ0Vjt667XbY+jMaE2XIQsKhSS2reuNF207N7at63VC8zKrtO8zVoppx6GKI6V40x33xWMHT07L+G1//Wj8/o2Xx0f+jyvj/TdcFh953ZXxvhsui7fc+cCs1+45MlR1uaqpr6dHx8rWi+Lo9H5O3vvK6gQ0lzOjadnjaGfG8tXXXW5zHUeh/hz/Ja+m9gMv39wXH/k/rowP/9yVcWa0FH/54NPxnT1H47nn9lTsK87sh+07dmpWH/n2r46fc/aOzz8U737Vznj/DZfFB27cFX/6v/bOarOm9nH3PHMyfnBo/P8PPjUQ77/7kSX1n+ezHMc8jH0tD20u+ZXE/3X1tvj4t56Ij3z18fj4t56I/+vqbZHE+D5sNfvm1bQry932zDwvdznaZKrTiHUxc3v5suefG//jF348vrt/ML6z92j892/9IIojpbjhBedPju1O/f9EOT98z2Nx5ORwfPnBp+N//fBIFJKIj7zuyuhqL8Sm/q645dqL44M37oo0jZpkeK6xo1IpjfuePB77jp6KX7vm4njLdc+LX7p6W/zo6Kn43sHBae+TxW173sf16mXLmu742M9fFbdce3HcfM3FceHalfGR110ZkabxPx8/HN9+4pnJ+lQus7d/9bF4zQsviNu/+li851WXzeojjo6W4tTw6Kx1MbU/We0YZq1yNvN9Lt2wKj5w4+WT38Gt114cH7jx8qqzYhxi+ennslyWe/sluywHuSWvZJc8qkVum/ZO98+cPDPtymUR4xXvmZNn4tTwWBRHSpEkUfY57SuS+PI8V15rxBXaDg4WJ8u7qb8rfv5FF05eNGDipKLz16yMD37l0cmrKN920xXxsks3zLrq8sTVHyPCVToXwFVNs+XwHPWc+rIuyKvB4nC89qot07ant1yzPU4UK0+6n+8Kp5X6B/sHimXryf6BYuy6oPFXfp1PpW3gTz9vfXzhwadd8fWstrZCXL9rc2xf3xsHBoqxsb8rdmzqr+l3cfhE+Tb3qeOn4z985v6K62Bqdp+7vnf8YF6kccGa7th53rNlzHoW60W/r/YqZbea/kI910dHWxJvfOm2KKURhWT891qpRxuRV+XW8fjdgr4bv3bN9oZtV5aS26nmynDE3PvlpVIapbQ0LZeltBSlUpr7TGlrl89CsltuPbz3+p3xn7/62LTxpYWul7a2QvzMjk3xvYODsXNzfxRHxuKitT11mzTWiDsTyPTSVMrt4RP5Gls4WGHf7+BgMZ6zvrdBpVq4vUdOx6f/YW/8/o274vTwaKzsaIs/+sYP4pzui+PSTasbXbyq1aNeHhsqP7Zw7NTssYX9A6cqjA2cKpuPJ4+fjk98e++0u4V94tt7Y8s53XHFljU1KT+tqdIxrr//wZGyGd3U3xVv/bMHJzP+W9ddEj86enradvb9r7687GsPHD8d29bN3/5VW1+fPFa+Xmw7tyd2bl49+by895WB5nL4RPk+4uETxQaVKJvmPo7SoEK1MMd/yauJfuCO81bFg0+diA985Xuz9tne/aqd8YodG6vqK049V2vC1HPOHnxyID74le+PX6zu5TumHWeu5u6in/z7vbF/oDj5vgcHi1X1n6tR63PbjH0tH20ueZXGWPR1tU07htXX1RZpjF8Yr1BIYt2qjvjgjbtiaHg0ejraYtXKFZNtRjXtSj3ankpt/aETtWuTqU4j1sXU7eXQmZF4ZP/J+D//+H9N5u2dL98Rw6N7o7Pt2btpVzr3/G8fOxy33/P4s3dmXN0Zf/Xvr47v7D0ev/3nD9Zl+1muztx67fa48ztPxrFTw3HBOd3x/LMXGsrqtt243vxKpXTWvIA/eO0VkaYRf/Xdg/Gn/7gvbv6p7fNmdmNfV6zp7oj2Fcm0dT71OOua7o5440u3xXPW9caOTX0LPtZbq5yVe5+PvO7KKCSF+Ng3nph87EOvuaLq9600DnHAOETNVOrnHtbPZQnqsf2SXWpNbskr2SWPapXbpt0DXdXVPnlltQld7YVY1dUe3R1t0666NvM5G/q6qrryWr3vMrGhr2uyvDe84PzJA0IRz14p8bFDJ+Pll2+efOxNd9wXD+8fqHj1R1fpXBjfV7ac29tZtg6v7e1sUIlal3VBXvV2ts/ant7+1ceip7O94mvmu8Jppf7Bugr15Nyz9STrd6+qtA184GlXfJ2pra0Quy5YE//bzk2x64I1NT/os25V+Sxt6l8ZEZXXwdTsPvDUYNz86XvjNz/3QCRJTCtj1rNYL/p9tVcpu+dW0V+o1/rYc2Qobv7UvXH7PY/HR776eNx+z+Nx86furennLHcbkVfl1vHE3YIauV1ZSm6nmivD8+X74f0D8Zufe2BaLn/zcw9Mfid5zpS2dvksJLvl1sPb7npo1vjSYtZLW1shdm5eHVdvXxf/4vkb4+INq+rWt2jEnQlkemn6u8uP5/Z3V94/y6L2tqTscrSvyFe/elP/yvj+oZNxy6fvjbfc+WDc8ul74/uHTsbG/q5GF21B6lIvkyg7tlBOR9uKsvnoaFtR9vmb+rri2Knh+OjXxvsBH/3a43Hs1HBs6MvXeiCbyu3/V2rD2gqFaRk/cmp41nb2icMny7+2yv5ptfV1U//KsvWiXPuU574y0FzWOo5WlfmOo1Bfjv+SZ21thRgeTeMdnx8f45q5z/aOzz8UDx88UVVfceq5WhO62guRpuP/bjl7l8/iSCne9aWHo7Pt2fvfTO3jVrq76A0vOH/a+3Z3lN8/XKxaHvcz9rV8tLnk1chYEm++88Fpx7DefOeDMTI23tbsOTIUv/zJf4qbz44x3vzpe+OXP/lPk+1GNe1KPdqeSm39+lXG4OqtUetiYnt5ZjSNd3xh+pjXu770cPzSS58TF53bM61s5co5dnaeSHFk/M6MJ06PxfFTI5MT7if+tpzbz3J15sP3jPc5Jo6VLaQONopxvbmVW3ff3T8YDz41EB++Z/w8iyePnZo3s/uOnYrXXHX+rGMOU4+z7h8oxu33PB5vufOBGBoeXXB/slY5K/c+Dzw5EL/+2emP/fpnq3/vTf0ry34veTsWlmWV+rnGeliKemy/ZJdak1vySnbJo1rltmnvdH/s1Ej8h59+bvynv/n+5FUJ/sNPPzeOnRqJc3ri7B3z9sUt12yfdjXj2266IraePSASMX51gz1HhuLgYDG6O9pieGws1vZ0znvl36mv29DXFVvWdMe+Y6cmf1/MlYO3ru2J2266It50x30VrzpXSsevSDf1sUpXYjt0ohhpWv59XKWzPFc1zZbjp0fit667JI6cGo5SGrEiiTinuyMGTo80umgtx7ogrw6dOBNrujvihhecP7n9vPM7T855J8XFXuG0UIh458t3xLu+9PC0qyHn5ZhApW3ggUqPu+Lrsqnc5j5754ziSCmeOn46Tg2PTfY9a3113pn93aXcGSOL9Ptqb7H9hYmslVsfBwfH7/5Sqxw2+3rPcr2d725BjdquLLWfO/Gdf//giUXvl1dqv2e28wtdl1nIQ7PXuUaqJrvz5XPm+FIt77JVD424M4FML82xU+Vze+xUvsYWjp0uPy59PGdjJDs29cUHbrw8Hjt0cnJ9XLy+N3Zs6m900RakHvXy8ALGFuY6blHOZef1x3tetTPe/vln7yb+nlftjMvPK78esrB9Jz/K5eXY6ZFZx81uuWZ7HBwsxq/+1MWTGe/uWBHPXd8bv/TS58TpM6PR3dkWf/adH8W7X7lz8sTkyfavyna82vq6Y1NfvPf6nZMnfXa1F+K91+/MXftEdpTGRuPRRx+d9tjzn//8aG/P14V/yDbH0aqT9+Mo9bbcfT+5XZjR0VI8vH8g9g8UY1P/ytixqc/EoAYqldJ46vjpaWPMU009vjBRlwZOD8dYKeKZk2emrcOp52pN7SNPnHP29PFT09730Ikz8cThk7F1bc+0Pm6lcqw4G5OJ9x0Zm/6cape3HvuCtdzHtv86nTaXvDo4WH5c7ODgmbN/L5b9+0S7UU27Uou2Z742p1xbP/M8YuqjnuuiXC4qnQNVHB6No6eGJ8fN7vzOk3HrtdsnL6gzsR3/5N/vnfa6oeHRsvXgG48eiv0Dp5dlOzjXcfeJ/y+kDpItcx3fLaXj/06s7zt2Pzl5PGKuzP7H//3SWXWs0nHWo0Pjfd2Dg8VYv6orVhTGn1spx3Od77P3yNDk51bTLyyX19IS50A0y7GwLNPPZTnUY/slu9Sa3JJXskse1Sq3TTvpfl1vRxwcOB1vfOm2KKURhSRiZXsh1vV2xOrujvjM7n3x8ss3R6EQ8fs37op9R4bi6u3nxmWbV0/urJVKadz98IGyB07ect2lcd2OjWV37Mq97r3X74z//NXHYu+R05MDQZVeX0mhkMR1OzbGJbdcHYdPnok/+uYT00LQ1V6IQhIx9fjLxF1Pu9oLs547cfXHuf7GdBNX0vR9ZcOG3s7Y88xQfOwbT0zWtTf9i+fGOle0qTvrgry6YPXKeP2LL5w2oHzrtdvj/NUrK76m0nZ14zx3mevr6og7/2lf/P6Nu+L08Gis7GiLT/zdE/H+V19Rq8VZVpW2gRsrPe6Kr8umUpu7esodQLvaC1EqRfwf/+0fJvuem1cvLrvllOvvLqZ/m2X6fbW3mP7CRNYePTBYdn2MjKXxL2//Zs1y2MzrPev1ttJ3P3G3oEZtV5bSz536nf/S1dsWvV9eqe8xs51fyLrMSh6auc412nzZrSafaRrTfh8ZS6NUSjPRZsynVEqjf2V73fupMr00G1Z1xN4js3O7vrej0UVbkA29nXHgeLlx6XyNkRQKSRSSwrT18aHXXJGLNmCqetTLzRXGFjaXGVuY67hFOW1thVjd3T7t+au728tO3snK9p18qJSX9as64zO798UbXrItkiQiTSM+s3tfvPW6S+MDX3l08rnvu+Gy+FcvujDe/Ln7n50U+Yod8bwNPVXne6Zq62tbWyGu37U5tq/vjQMDxdjY3xU7NvWb1MaiDT3zdPzel87EugfHJ96dOLgv/uuvRuzatavBJaOZOI5Wnf6V5Y+j/P6NVzS6aJlTj77fuT0dZXO7tidf+2j1MDpairvuf2rWRYGu37VZH6UBJupH54rC5B2SyvUzN/R1TT73v3/rB/HqF2yZdtGPqevwuh0bY/MbXxTffOyZ2LK2J54+fipefvnmyXPPpr7vWBrxL2//Ztx20xXxvA2rpn12uXJcvH5V3HzNxZN97+t2blzU8tZjX7BW+9j2X2fTVyCvNq/uqjAuNnGMq/zfJ85RWH/2bnYz25Vze57N/lLbnmranKnn5R46MT6RtNUvBtIo9VoXlXKxqULeLlzbE92dbfH//s8n4rabrojvHRiMiBg/x2zNytjQ1xW/+bn7Y/9Acdrr1nR3RG/nimn14MK1K+Pf/rOL4w3/Y/eybAfnOu4+8f+J+uO4Vr7Md3x3xdn4TPSBO9qS6GorTI7Xrkgi/uu/fkE8+ORgnBktxSf/fm8cOzUcl27sm5W9cucoXLh2ZRw/NRq/8v99c1qb/olvj7/PzBzPd77PvT86HmNpGsOjaVX9wnJ5XZEsbQ5EsxwLyzL9XJZDPbZfskutyS15JbvkUa1y27RHd5KI+N2//F7cfs/j8ZGvPh633/N4/O5ffi+SGL8a4luuuzQ+/q0n4vf+8tF48+fuj23reqdNuI8Yv3LaxI5cxPhVDW7/6mPx8ss3x5vuuC/2HBkq+9nlXve2ux6aPNBSHCnN+fq5FApJbFvXGz924Tlx201XTDtAdOu122P7+t740gNPTT52201XxI5NfbOeO3H1x4krQ5b7G7P5vrJlpFSK2/76+9Pq2m1//f0YLS38yt8sjXVBXo2MlSYPakSMZ/fD9zw25x0EVne3xTtfsWPatuCdr9gRq3vmvvPRikLEtZdujDd/7v54y50Pxps/d39ce+nGybsXZF2lbeDl5/XHe6/fOe1xdxdbXiNjFdrcsfEjZBOZ/Ng3fjD59zfdcV90tMWisltOuf7uYvu3WaXfV3uVs1u5zZ3I2h27n4xbrtk+bX28/9WXx9s//2BNc9jM6z3r9bbcd3/LNdvjSw881dDtymJyO2Hqd37nd2ZnuNr98ok7ec5sv2e28wtZl1nJQzPXuUabL7vz5fM9r9o5bXzplmu2x9s//2Bm2oz57DkyFB/6yvfinS/fMWu5lrM9kemlGS1F2dyOpfO8MGOSQvlx6UJO9v0m7DkyFL/+2enbil//bHb6DtWqR70cLaVlxxZGS7PD21ZIyuajrcIJXHuODMWvffreac//tU/fW3Y9ZGX7Tj5Uykt3RyH+3T+/OD7+rSfiI199PD7+rSfilmueG7939yPTnvvDZ4binV94eNpj7/riw3HizNisfPd2VjfusJD62tZWiF0XrIn/beem2HXBGpPZWLLe9efHmgu2x5oLtseqDVsaXRyakONo1dm6tid+8SXPmXYc5Rdf8hz7VGXUo++3Iim/j7bC3INZHt4/MDnhPuLZc4Qe3j/Q4JK1pon6sersMd0v3v/UrLGvd79qZ1x+Xv/kc1//k9smJ9xHzF6HhUISl21eHdvW9cabP3d//N5fPhof/9YT8as/tX3aGNo7X74j/ugbP5iskysKMdnHnbi76Mxjqh/6yvcm+95vuW72XUarXd567AvWah/b/uts+grkVSGJsuNiE0NdY6Xyf584zHZ6dGxW23jrtdujODo2+RlLbXuqbXMmzst90bZzY9u6XhMuG6ge66JSLtb1dcS7XzX9uOy7X7kzPv6tx+N3/uKR+MWXPCfedMd9cfs9j8f773403n/3o/Gbn3sgHnpqIP7tP7t41vHctkIS9z85MK0evPzyzfGuLz68bNvBcnXm1mu3x5/905OT51lM1B/HtfJlvuO7l53fH5du6otbr90eX7z/qXjLdZfG++5+9njE7/7lo/Erf/JPMVoqxUe/9vjkRPmLzp29vsudH/mOl++I3/rzB2e16Te84PyyOZ7rfJ9brtken939ZDzw5EDV/cJyeb3s/P4lbyOa4VhYlunnshzqsf2SXWpNbskr2SWPapXbpr3T/ZPHi9OuSBAxXvGePF6MKy+s7mqIBwfLv0eSjP976EQxtq3rnfXZc71u6u+VXj9VqZTGniNDcXCwGBv6ni3nxBUdn/drV8e+o0PR3dEWG/o64/zV3bHjvP5ZyzXX8rpKZ/Vc1TRb9g+cKVvX9g+caVCJWpd1QV7tH6yQ3cHK2X36eDE+/Q97p91p5Y++8YO46NzuuHBt5e36/oFifOLbe6fdKewT394bV25ZHVvPnbs/UEmlfsJSzNf3KLcNrPfdxZZjufOkUm6PDo3EH/7rF8Sa7o547198Nx54anDa3/ceOb2o7JZTqb9bTf82L/T7am8xbe5E1vYPFOOTf/9sG3r1xedGoRCx98jpWe+3lBw283rPer2d+t0fHCxG+4pCnCgOx+0/d2VD71q5mNxOmPqdT83w5Zv7YvuGVVXvl8+8k2eldn4h6zIreWjmOtdo82V3vnwOnB6Ol1++ebLf+sm/3xv7B4qZaTPmc3CwGLv3DsTw6PS+z5Y1Xcvansj00uwfKN82Tb0zTB48XWFcev/xYlx+foMKtQhZ2VYsVaGQxMsu3RCfeeOLYv9AMTb1r4wdm2bftWUpFpLdp+d47uUXzH7vg4Pj2/4bXnD+5PGFO7/zZNn10CzrjPqolK0nnjkVdz+4P/7w518Yx0+NxOru9tj7zKlZ+16lNMrm7cjQcPzqT1087T2PnDoT22PVvGWyHQWameNo1bEtqF49+n77jpX/jH3HirHL9UmmqbRPcGCgGLvK9PNZXhN93QPHz8Sn/2Fv/PrLLok0LcUf/vwL4/GDJ+P55/XFiiSJJwdOx5Gh8fb59JnReddhuf3L563rjedt6I0fHTsdpTTij77xg8lx2+JIKQ4MFqe1axv7uuJlz98Yh0+Ot3Fb1nTHC7asWVKbV899wVq10/ZfZ9NXIK9+VKG/8KNjxbhiS8ShE+XHHw6fLMZz1vfGk8dOlz2PZtu5PbFz8+qIKNf+jp+TUm3bo82hnHK5WNPdEfuPn4lLNvbGJ3/xx+PgiTOxvrczCoU0vnLnMxER8fihk+XP1zk1Et+496m47TW7YmQsjdU97VFK03jwycEYHitNe83E+ecz3+PQiWJsXduz5POhJrbXa3/hx+Objz8TXWePi736hedHmkZsXt01+Z6L2ba3+jlbjTTX8d2L16+KFYWIJ545GRERv/GyS2J0RvYixrN26aa++G+vf2FcdPYGAeXWZ7nzIx87WD7/E+37zLZ15vk+N//UxbGutzPWreqM7x86ERGVx5nLtdGV8hoRi+6f2kYsP/1clkM9xhBll1qTW/JKdsmjWuW24ZPukyQ5LyL+ICL6IuK30jS99+zj/RHxwYi4OCLem6bpPQt53/P6u6KrvTCt4nW1F2JTf1dEPHs1xLl2ijb0lX+PNB3/d/2qrgW/burvlV4/oVRK4+6HD0xexW3iygrX7dg4OfntOet74znrpy9DueWaa3mr+S54lu8rOzb0dZataxv6OhtYqtZkXZBXi8nuhr6u+P6hk3HLp++d9pr5tusb+rri2Knh+OjXHl/Q6yqZr5+wHO9ZaRs4cXexepw4tBzLnTcb58jtj1+0Np44fDK+f+jktNeM94NXLiq75VTq7y42z1ml31dbc2W3kqlZ2z9QjI9+7fHoai/EDVdunnx9rXPYrOs9D/U2i9/9YnI7YeZ3vn+gGB//1hPx5VuuXtA+e8T0bW2ldn4h6zJLecjiem8G82V3vnw+cfhkfPxbT2QiI4sxsXwPPDU42ffpai/El2+5etk/W6YXbyltbpZs6l9Zdjk29uWj/kzI0rZiKUqlNL7yyMFl3YdcyNjCeavL52PiuMVMm/q74vUvvnDyTkgTdyUql6dmWWfUx8a+8tm6YPXK+PYPj8bXvv/M5HNvvfbiWdlakZTfH1vb2xEfv/OJefNaie0o0KwcR6uebUF16tH329RfPrcb5XaWZtkPbBYT+1Gru9unHRebePwX/t9/nOyvvv/Vl8eFa1dGd2fbvOtwrv3LVV3t8S9v/2bZOlmuXZt6ntdS27x67wvWop22/zqbvgJ5NV9/Yb6xrU39K8ueR7Oxv7r2t5rxPW0OM5VKaYyOpdNyMZHV//OP/9e0rP7OXzwSN111/uRzh8dKZfNUSCIeeGow/t2nnj0m9YaXbIu2QuVxtJm/r+vtqtn5UIVCEutWdcYffXP2sb5Xv2DzrOdWu213zlZjrV9V/vjuX95ydTxy4ES86Y774peu3ja53m++Zva4bld7IR56ajA+/q0n4iOvuzK+f+hk2fVZ7vzIcuPEU+dGzGxbZ7a/hSSJd37x4cnPuuWa7VGoUD8qtdGV8rrY/qltxPLTz2W5LPcYouyyHOSWvJJd8qgWuW3opPskSZKIuCsinoiIoxHxiYi47OyfPx4RvRHxvYj4TJIk56VpOlzte68oRLz7lc+P7o72GDozGj1dbXHqzEhM3MyqmqvtbV3bE7fddMW0Hcpbrtken9m9L2676YrJK6TNVO51771+Z/znrz4WETF54ObI0JnJ55cbcNhzZCjedMd90672+eiBwXj+plWLviPuQrkqIVnW27kiPnDj5fHYoZNRSscHKC9e3xu9nSsaXbSWY12QV+t6V8Tv33h5PD4lu89Z3xvreitnt9x2fmq/oNK2c7Gvq2TPkaF4/92PTF7xOyLi/Xc/EpdsXLXozuFE32Nip6U4Uoo33XFfXFJmcuByq/R9ZKmMjdLdUT63PR0r4ts/eCY29XeVzdqOTX1zZrCcxeZ5KfQ/G2e5v/u5slvJfFn7yOuujAeeHJh8v8vO719yDmv5PWQpz8tZbxcjS9/NXBaT2wlb1nTH+199ebzlzgfm/c5nfh9b1nTHvmOnyn4/tViXWctDK2p0mztXBkqlNNI04oM37orHDp2IO3Y/GcdODc+ZkazV6cVmPGvLkUXL+R11d66I97/6svjB4aHJ3G5b1xM9ORtbWN3dFu+74bL44TPPLsfWc3tidU97o4u2IM2yrajHPuSa7vJt7pru2dld1dlW9rjFqq7y+RgrxeRJyRPl//A9j8XLnr9x1nO3ru1Zlv4xzelEcSTu3Xck/vDnXxjHhkbinJ72+JO//2H8+NZz4m3/+6Vx6MSZyRxdcM7K+L0bLou3/tmDk+3B8zauive8ame8/fMPTT727lftjD/460eryitAq3EcjVqrR3+9s60Q737l8+PJ48XJ3G5ePT4hgelWd7fFO1+xI941ZQLJO1+xI3f7gc1irBQxPDwcXW3jbe+5vZ1x+OTpWL2yM3bvPRa/dPW2uPM7T8b+gWK85c4H4mM//8LYd2Qo3vXKHfHOLzy7Dt/1yunrcK79y0buQ+dx/z2PZV5u+gosl+Ue8+5oK8T7fnZHtK9omxzrGhkdjc6z/YWxUsSf/uO+aee1/Ok/7pscK9ixqS/ee/3OeNtdz44vvPf6nbFjU//kZyx1fE+bw0x7jgzF2z7/YNxyzfa4/avjY6+vuer8+PA9j007V7s4Mhavf/GF8ZcP7o//8q+ujFNnSjFWKsU7X7Ej/p+/fTxefvnmWFGI+LELz4lSPDsZf+p55m9/+fMjIuLWa7dPjvN+8f6nZvU7brvpilhRiLJZX/sLPx7rVnVWrL9T6/mm/q4YK8X4nfx6O+OP/82PxbefOBKlNOKL9z8Vb7nu0iVlfyH1sRWPuS33Mq8oTM9SV3shfvtnLomjQ8PxxOGT8YEbd8XpkZH4yOteEA88eTy6O1bEm/7Fc+O2v/7+tGx+8u/3RnGkFE8cOhknh8fil67eFhER33j0UHzvwGC0r0hi29njDTd/6t7J1152fv+s9vTWa7fHJ769N7raC/GR110ZaRrx7R88M3nOw203XRHvv/uR+PWXXRKPHzoxrS9++1cfiz/+Nz9WlzZ6Iee//e7PXhaFZPw1c62/Wq/vRtWZ5f5c/VzySnbJI7klr2SXrGr0ne5fEhEn0jT9ubMT8PcmSdIXEasjYmdE7EzTdDRJkp0RcUlEPFDtGw8UR2IsTeI3Pnf/tINag8WRqq+2Vygkcd2OjXHJLVfHwcFidHesiJGxUly3c+OcOxVTX3foRDHWrxrfeXzBljVxcLAYI2NpvP3zD8beI6fnvNLfwcFirOnuiJ9/0YWTgztd7YW4cG1PbDln+XemXJWQrDs5PBLFkVJ87BvP3jnnPa/aGUPDI40uWsuxLsir4mgpzpTJbnE0nfN1HW1JvPGl26KURhSS8d8j5t92zuwfTPQnFrPNPTJ0Jl571ZZpfYRbrtkeR4fOLHriwMHB4rSrhEWMHyA5dKJY1wntc30flcp4cLC+ZWykgdPDZXN7+GQx/s0ff2fyQMZf/NrVcfjk9KxVymA5i83zUuh/Nk49vvtK2R04XfnaavO1ncOj6bT3u+2mK5ZUxlp+D1nL83LV28XI2nczl8XkNuLZu27c9tePxhtesi1WFCKuuvCc+Mlta2ctY7nvY+LCfeXGDWqxLrOUh1aUhTa3UgYiYlbZfvdnL4sXbFldcSwqi3V6MRnP4nJkzXJ/R8Ojo7O27e951c4YHh2tQenr59CJM3Hk5PC05fiNlz0vDp84Exeuzc8+S7NsK+qxnzuapjE8Or3Nfe/1O2M0nT22cPz0cNnjFscr9C0OnShf/sMni9PujDih1v1jmteZsdG49tJN8cuf/M60SUVJUoq2QmFWW7yyY8W0sbDRUhr/csf62Lr2x+Pg4JnY0NcZKwpJ7N47MO1z5sorQCtxHI1aq0d/vZSWIklm9wtKUZr/xS3m6ePF+PQ/7I3fv3FXnB4ejZUdbfFH3/hBXHRud672A5vFWDoam9b0xuun3Kn23a/cEW//ykOT460Tk432DxTjvh8NxJ3/9KP49Zc9b1qfd3i0NG1ffr79y0btQ+dx/z2PZV5u+gosh3qMeY+OjcXIWBK/9ef3T2lzd8bo2FhERBw9VeG8llNn4jnRG21thbh+1+bYvr43DgwUY2N/V+zY1B9tbc9e5Gep43vaHGY6OFiMvUdOxyf/fu/kBSE2968se6727/zszvi5H78gHj1wcnKi81UX9sevXfPceNtdD06rW3ffenXsH5h9nnlExMXreuMFW9bEqeHRKCRJ/Oevfn/WMeR/3Hu0bNa/+fgz8UfffKJs/Z1az9d0d8TrX3zhtAnZt167PT579uLa73/15fGySzcsKfvV1sdWPOZWj2XeP1CMT3z72dz2dKyI0yOl+Fcf/4cojpTiwrUr49++9OK4+VP/NFmGP3jtFfGlm18Sjx8+GQ8+NTjZB97U3xU9Xe1x2988Nu217/rSsxeD+NBrxnN9YHD6seSJ9nRdb1esKERcuWV1bOzriu/uPxH/+3/+5rTl/+nnrY+RsVK8ecoxkal98YhY/n3bKs5/e96vXR2PHBiM7x88ER/4q0cnL0hfaf3Ven03qs7U43P1c8kr2SWP5Ja8kl2yqtGXoH5pRHw6IiJN0zQi9kVEZ0RcHRGfS9N04ozKvWcfr9rK9rbJq0hHjO/Yv+uLD0dXe1vFq+3tOTI0630KhSS2reuNFz/n3Nh1wZq4auva2Laud96diYnXvWjbubFt3fgA4bZ1vbGhryve+MndsffI6Xk/e0NfV7zmqvMnB3Emnv/bf/5g2efX2kK+J2iItDB5J52I8Yy+/fMPRZo2umlrQdYFOXWimJbN7oli5ROG9hwZips/dW/cfs/j8ZGvPh633/N43Pype2PPkaF5t50z+wcT/YnFbHM7VhRm9RFu/+pj0b5i8fVuQ9/sO5R0tRdi/aquRb/nYsz1fXR3tJUtY3cVdxxuFm0rVpTNbXd7++TvN3/q3kiSmJW1ShksZ7F5Xgr9z8apx3dfKbttK+auv7VsO+dTy/fMYp6Xo94uRha/m0oWm9uJZdx75HR89Gvj/YU3fnJ37Dt2quJzp37G2+56KF5++ebJ32d+P7VYl1nJQyvKSptbLgPlyvbbf/7g+MnGFTKS1Tq90IxndTmyZLm/o5GxpGxuR8by1T51rCjEB78y/U7PH/zKo0vaV2uUZthW1GM/98Tp0uTduCKe3ZafOD17bGF0LC173GJ0rPzF/xZSfu0YC5Gmhcm7aUWM5+WdX3g4RktJ/N8zMvr2zz8UDz89OG0s7M2feyAeOTgUP3bR2nj5rvPixy5aG6u7OzIxrgRLVRobjUcffTTuv//+yZ+RESd1NKORkZFp63lZ17XjaCyD5e6vnxmNsrk9o0mcZUNfV3z/0Mm45dP3xlvufDBu+fS98f1DJ/WDGuT4qbF4x4y+7ju+8PC08dbbv/pY3PCC86OrvRBbz+2Jl1++Od78uQem9Xl/58uPTNuXn2//rJH70Hncf89jmZeVvgLLoB5jRaOlJN7xhYdmtLkPxWhpvE4nkZQ9ryWJZ+t8W1shdl2wJv63nZti1wVrpk24j6jN+J42h6kmMrV/oBgf/dr4tv/pgdNlz9Xee+RUHBg8MzmRPSLiJ7atm5xwP/G8N91xX5TSKHueeaGQxNZze+Mntq2NC9f2xC//yXdi996BWceQK50PlaaV6+/Uen7DC86fVs7iSCk+fM94n6c4Uoq33PlA2WPVi/nuZpZxZn1sxbHqeizzhr6uOHZqeDK3J8+MxX/6m+9PfubLL988OWl+ogz//jP3RaGQxHM3rIqPf+uJyYnuN7zg/HjPl74752t//bPjuZ7adk5tT5+zvje2njv+/1IaZZf/kYOD8ZY7H5i1HZjoi2/o61r2Nrqa89+SJOI3Pnt/3H7P47F/oDjv+qv1+m5UnanL5+rnkleySx7JLXklu2RUoxN4QUQ8NOX3/og4WubxtRFxaOaLkyR5Y5Iku5Mk2X348OFpfzs4eGaywk0ojpTi0OCZOa+2t9wW8tlb1/bEc9evykVZqd5cuWVhKtXzg4NnGlSi5raYNte6oNHma3MXk925to+L3XYu5nWnhsfKvubU8NicnzWXrWt74rabrpg8QDJx9dKJK9XWy1zfx/DYWNxyzfZpZbzlmu0xMtZcd1ZZVJt74sy035faZ2tEX1D/s3Fq8d0vR5u73GVezveU58qy9t0sRz93IctY6blJMv132WkeWW5zF1O2rNXpxWqW5VhOy53dZhlbWI59NRavFvu5tWxzD52ocNziRPmcL6T82jFmWmybW+7x0ozrQow/d3q2sjKuRP41+jja0DNPx+996cF48+fujzd/7v74lY9+Mb773e/WvRwsv+9+97vxKx/9Yk3Wdb3HxaBWWmEfrR70g+prsW3uzPHWFYWIW67ZHk8fPxVJEvPuy1vPLJU2l3rLwrGIo0Pl/350aLjqMmh/WYy5slsuU5ed31/2XO1SOv4z9fFK/YZq6tZizof6s396suJnTH2/SuWa6APVYqy42vrYimPV9WhzZ37/KwrVZ3Mpr61GpeWfmMA+8/EVhahbW17Nulno+qt1xhtVZ2r1ufq55JHxXPJKm0seaXPJq7YGf/7xiDgZEZEkSWdEDKVpOpYkyeTjZ22IiH0zX5ym6cci4mMREVddddW0U3429HVGV3thWsXrai/E+r7OOLe3wt/qcJXpiSv9VfPZhUISl27qy0VZqd5cuWVhKtXzDX2dDSxV81pMm2td0GjztbmLye5828fFbDsXs82t9JoNfYvfThcKSVy3Y2NccsvVcehEMdav6oqta3vqfpXv+b6Pz+zeF294ybZIkog0Hf/9up0b61rG5baYNnfdqs5pvy+1z9aIvqD+Z+PU4rtfjjZ3ucu8nO8pz5Vl7btZjn7uQpax0nPTKSWRneaS5Ta3lv3SvGW2WZZjOS13dptlbGE59tVYvFrs59ayzd24wHwspPzaMWZabJtb7vGZkSuX26yMK5F/WTiO1rv+/FhzwfZGfDR1tmrDlpqs63qPi0GttMI+Wj3oB9XXYtvcmeOtF69fFR/6yvfi5ZdvjiQpf4x3ap/XemaptLnUWxaORWzoW7nkbGt/WYy5slspUz98ZmhWXlecjVm5HC+mblV7PtSWc1bGU8dPxyf/fu/kncnLfcbM95urD1SLseJq62MrjlXXo82d+f2vbG+Lj33jiaqyuZTXLmX5N/WXf/zaS9bHZZtX16Utr2bdLHT91Trjjaoztfpc/VzyyHgueaXNJY+0ueRVo+90//WIuOLs/18XEZ89+/+/nXg8SZKfioj/mabpgk6quGRjT7z7lTujq/3ZK+q9+5U745KNPQ29+uVCP/uic/NTVqi351ao58/dKKP1Zl2QV8+vkN3nz5HdubaPi912LuZ1y7WdLhSS2LauN1607dzYtq63IQcq5/uO33LdpfHxbz0RH/nq4/Hxbz0Rb7nu0pbqn1Tq5/7Nw09N/l6LLDSiL6j/2Tj1+O7n2kfLSplr+Z7yXFmevpvF5nYhy1juue+9fmd86YHatutkR5bb3Cz1S+utWZZjOS33d9QsYwuylD3LvZ+7kDb3svP6492vmvHcV+2My8/rX3L5ZY+FuLRCbp+3sSfee/30x3/nZy+Li9f3VpXbLIwrAWRRs/R1aS1yuzD6Qdmxc+OqMtndMW289ZZrtseHvvK9+Lkf2xJ/9k9Pxhfvf2pWP7jc/pT1zHLR5rIc6jFWNF92d2zqm9W+vvf6nbFjU+WxsHK0v9RauUyVO1d7bU9HbN+wKm699tk70H/x/qdmjfFWW7eqPR/qD/7msVjZviKOnRqe8zOmvt+d33lyWjm72gtx67Xb48/+6cma1v9q6mMrjlXXa5mnfv+Xbe6f9plfvP+peOfLd1Qsw7yvfUXl1y52+Xds6i/7eL0m3M9VtqnLttD1V+v13ag6k4W+AmSV7JJHckteyS5ZlSxwLnttPzxJkoh4X0S0RcTpiPh8RLwqTdO3J0nymxFxXkSUIuIdaZoOzfVeV111Vbp79+5pjw2eLsb3DgzFwcEzsaGvMy7Z2BN9K8evvlUqpbHnyFBDrn650M/OU1mZVNWXVC63LMzx08X4/pR6/tyNPbF6ZfNeEbMOFp1d64IGWlKbe/J0Mb47JbvP39gTvfNkd67t42K3nYt5XTNvp5fjO86gRWd3Zj/3eRt74sjJ0Zp/J434rpto/eZOld/9ktrcufbRlrHMDXtPea6sAd9NzdrcanO7kGWc+dwta7pj37FTstPEstzmtnK/tFmWYzktd3abZWxBlnJr3pVUizZ3eHgsHnh6IA4OFmNDX1dcfl5/dHSsWHrpQ/Za1KLb3BOni/HIlNxeurEnVq3sitHRUjy8fyAODBRjY39X7NjUH6VSumy5pWUtqa97//33x5s/d//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GJd0l6ZuS7p38ni/P8zxJn5zx7R0zfmdjJg2KRj1t33VE1297RpHRqMIVId26cZXWd7Wm3fHovICdolFPj/62W88d7FXUk8qMtGJxo9ae10IfzTO2BVwVZL0QRFuoN5CKXOSW/CEfbBpzbUHfsx+5JaeuKnR2yQ1KGXMkSKbQ4zPcYvP+lCwjkULkljwiCLnOLjUiXGVzPWIT9kUoJPKXH9QKQG4ENYZRs8Bmxbyvpu8BmAt1LlxFduEicgtXkV3YKqiL7r8qqUkTd7gvn/z6I0kRSb8b0N9Iy76egdgkhSRFRqO6ftszWrZ5jToW1KX8PMU84QG47sDxAe3uPqWtj++N9c8t6zp1zoI6LZ2fej9H9tgWcFVQ9UK2qDeQjqBzS/6QL7aMubag77mh1HNLTt1VyOySG5Q65kiQTKnXFkid7ftTsgw/hcoteUS28pFdakS4yPZ6xCbsi1BI5C/3qBWA3AliDKNmge2KdV9N3wOQCupcuIrswkXkFq4iu7BVKKDn+aCkt3me9xZJvydps6SLJf1w8m72edfdF4lNUkyJjEZ1tD+S1vMkmvDY1zMQWFsBZKa7b1i3PbI7rn/e9shudfcNF7hlpYdtAVcFVS9ki3oD6Qg6t+QP+WLLmGsL+p4bSj235NRdhcwuuUGpY44EyZR6bYHU2b4/JcvwU6jckkdkKx/ZpUaEi2yvR2zCvgiFRP5yj1oByJ0gxjBqFtiuWPfV9D0AqaDOhavILlxEbuEqsgtbBXXR/Zckvc0Yc7smLrb/C8/zoirQXe4lqaUhrHBF/MsLV4S0sD6c1vMU64QHUAwGRsZ8++fgyFiBWlS62BZwVVD1QraoN5COoHNL/pAvtoy5tqDvuaHUc0tO3VXI7JIblDrmSJBMqdcWSJ3t+1OyDD+Fyi15RLbykV1qRLjI9nrEJuyLUEjkL/eoFYDcCWIMo2aB7Yp1X03fA5AK6ly4iuzCReQWriK7sFVQF90PS2qUFJE0LmmDMebXkl4T0POnbWlzrW7duCo2WRGuCOnWjau0tLk2recp1gkPoBgsmVfr2z/b56XXz5E9tgVcFVS9kC3qDaQjldxGo572HjulJ198VXuPnVI06iV8PvKHfMnFmJtO1m1D33NDtrl1OaMSOXVZ0PVCOsgNsuH6uCkxR1KqUs2uLfMQsJ/t+1OyDD+Fyi15RLbykV1qRLjI9nrEJuyLIBVuToP85R61ApBcNuNfEGMYNQts55fzv3n3CoWMnFwDmULfA5AK6ly4iuzCReQWriK7sFV5QM9zStJ6SYsk/VbSM57nXWSMeSyg589IbVVIt1y5UgMjY6qtLFdtVfqfMTA14XH9tmcUGY2yOAFYZMm8Gv31u1for77761j//Ot3r9CSeTWFblrJYVvAZZXlRpsu7VDUk0Jm4v/zzaZ6Ixr1tK9nQN19EbU0hLW0uVahUP7fEyQWChm9/fwW3bvpYh3ujaitMayutsbYdopGPW3fdWRWntZ3tfpuS5vyh+I2V3bTlW7WbUPfc0M2uXU9oxI5dVnQ9UI6yA0yFY16evS33XruYK+inlRmpBWLG7X2vBZnxk1JOnu+fx84ez59oFilM6YGXROjeOVjf5rtHJANc2qwSyHqwGjU00uvDqg8ZHTLlSu199UBjUWj5BFpyUd2WUeDi9qbanTzhuW64YGdsdzevGG52pvI7UyhkNH6rlYt27xGR/sjWljP+pqtcrUOWsi5YPKXe9QKQGLZzukGMYaxJgHbTeX8vI+u0fNH+vRCd7/+zw9+qxODI86tHUun66megWF9/j0X6BP3P0ffA5AQdS5cRXbhInILV5Fd2Cqoi+6/Lukdkv5T0uWS7pv8/vaAnj9tB44P6D9f6ddtj+yOdbot6zq1ZF6tls6vS/l5WJwA7HXw5KAGIqNxJ/YNREZ18ORgWv0c2WNbwFX7egZ03T1PKzIajX0vXBHSw5vXqGNB/rJrS71RDBcHloJo1NMPn+9OuJ329QzEfiZJkdGort/2jJYlyLUt+UPxmyu76Uo367ah77khm9y6nlGJnLos6HohHeQGmTpwfEC7u09p6+N74+Zzz1lQ59TcAn2g9KQzpgZdE6N45XosyXYOyJY5Ndgl3/tAvxxvXtupb/3ioLY+vpc8ImX5yC7raHDRwZODGhwei8vt4PAYuU0gFDLqWFDHvsdiuVwHLfRcMPnLLWoFILEg5nSzHcOYj4ULQiEjY6SP3/ds3Hyaa2vHM+upJc3V2nrNalWUGW7sAsAXdS5cRXbhInILV5Fd2CqQi+49z9sladfk/35x2vc/H8TzZ6K7b1jf/o8DuvaSDpnJY/hv/8cBXdTelHans3VxgjvRotR19w3re88e0h++qUNDw2OqqSrXN362V+e1NrBzzTO2BVzV3RdRU02lrrhocaxeuP+XB3W0P5KT/X6yfbcN9UahTwhBavb1DOjz25+Pq3M/v/15LWutV8eCOnX3ReIW6doaw7riosV6obtfknxrRhvyh+I3V3bTNTPr0sS4FeQYnutjLvqe/bLJbT4ymmvMO7gr3XpBCjaf08c3coRUBTmfawvPK3QLkA/pzC0EXROjuOXyeCHZHNDS5to5992JaonjA8Oxn7PfL035PM71y/Htj+7WlnWdOjU8nnQuLBFq19KV6+yyjgYXkVsUm1yug/rVx001lTrWPxxXV0y1g1rDPdQKgL/uvuHYTbGkibH1tkd2pzWnG8RxWBB9lONB5Fo2a3O5zGc6zz2zntrfM6RNd+/ggw8BJESdC1eRXbiI3MJVZBe2CupO99YZGR/X+1+/RH/74xdin6L5p289V6Pj44VuWiC4Ey0geYrqPb/Trj//zrOxfnDj5V2SonM+FsFiW8BVbY1hffC/LNWtPzpdL1z/tnPV2hAO/G/Nte+2YQGvGC4OLAU9A8O6enW7bn90d9xdvI4PDKtjQZ1aGsIKV4QUGY2qrTGsay5eEve76dSMNuQSxWOu7KarpSGsJc3VuuyCRbELlh569pAW1gczhnPMBSm73E4fj6eEK0IpZdSG8Zc+4LZ06oUpU/kMMn/kCOkolvlccl960plbCLomBjKV6MMijg8M6zdH+uccw/yOx5568ZgOnYzoA1/7BeMfcmqqXn2hu18fWtOh+395UId7I5ImLm5rrq3UbY/sSjuH7MORS6yjwUXkFi6bPr9VU1mukfFx9UfGcrYOOnOura0xrD984xL90dfja+PKcqPr7nmaWgOzMObCVQMjY77zC4MjYyk93pbjMFvageI0VZcMjY5ry7pztG3H6XmMVNaOM81nKut96T4355UBSBd1LlxFduEicgtXkV3YKlToBuRKQ1VF7ARNaeLA/m9//ILqqyoK3LJgJPoE5n09AwVuGZA/5aEy3fTQrrh+cNNDu1QWKitwy0oP2wKuGhv3YifFSxPZvfVHL2hsPPjbESbbd08tYrzj9if0vq/+XO+4/Qlt33VE0Wh+b4s4dULIdKleHIj8qQyFYhdoSKfv4lURmth2S5trdevGVQpXhHTFRYtn/W6qNaMtuUTxmCu76WpvqtFH13bqa/+2V3c8ukf/8MRefXRtp9qbagJpL8dckLLL7fTxWFLsJIWpOxolYsv4Sx9wWzr1gnQ6n+1NNYHmjxwhHcUyn0vuS086cwtB18RApqYuApp+PPWHb1yiyrJQSmOY3/HYR97SqU/c/xzjH3Jq+vHSH3/zV/qHJ/bqmouXqK1xYv7yqtWL9env7cooh+zDkUuso8FF5Baumjm/evXWJ/UfL53Qnu7+nK2Dzpxru2r14ll3fr5+2zN67mAvtQZ8MebCVWfPq/GdX1gyL7X1WluOw2xpB4rP9Lrkv/3TDn3l8Yk+0tYYTnntOJN8prrenO5zc14ZkgqVyxgTyNeis9oL/WoQEOpcuIrswkXkFq4iu7BV0d7p/uipYd9P1Dt2arhALQoWnxgIJO4H3X2RArWodLEt4KoDJwZ9s/vyiUGd01If6N9Ktu+W5LuIsWzzmrzu16dOCJn5CcZzLfAgv44Pjvhm6fjgiCQpFDJa39WqZZvX6IXu/oxrxkSLa/nOJYrHXNlN14ETg7rhgZ1xGb3hgZ26qL0pkIxyzAUpu9xOH4+P9ke0sD61O4bbMv7SB9yWTr0wPZ9B548cIR3FMp9L7ktPOnMLQdfEQKbGo5p1EdBtj+zWeS31KY1hfsdjT798kvEPOedXr97+6G5de0mHvvZve3VWU03GOWQfjlxiHQ0uIrdwVaJ64bq3nKMt6zpjdXCQ66Az59oGR8Z9+8/Mz7Wk1sAUxly4qn94zHd+4b+8pjmlx9tyHGZLO1B8/OqS2x7ZrW988PVaUF+V0tpxJvlMdb0v3efmvDIkFR3T1V/5WSBPde+H3xTI86DwqHPhKrILF5FbuIrswlZFe9F9uKJM4YpQXMcLV4RUVVEcn3Qx9YmBM18fnxiIUjKvrtK3H8yrrSxgq0oT2wKuqq0s981uTWXwJVKyfbctC3iZXhyI/KpJIbehkIllJ9Oa0ZZconikkt105DqjHHNByj63U+NxOpm0ZfylD7gtnXohm5Nr5kKOkI5imc8l96UnnbmFoGtiIFNH+/33+bVh/4zOHMP8aoaol/kcBJCqRPVq+7xqXXtJh472RzLOIftw5BLraHARuYWrEtULkbGo7v/lQX3jg6+XJy/wddDpc217j53y7T8z/xS1BqYw5sJVr/T6j7mHeyO64Ky5H2/LcZgt7UDxSVSXePJSXnfLJJ+prvel+9ycVwYgXdS5cBXZhYvILVxFdmGrUKEbkCtNNRXasq5T4YqJlxiuCGnLuk411VQUuGXBmPrEwOmvj08MRKk5o7pcN17eFdcPbry8S2fUcJJqvrEt4KqWhirfeqGloSrwv5Vs3z21iDFdoRbwpk4IubhjvjoW1LEwYqF0cptNzWhTLlEcgh5zc51Rjrkg5bdWOP037Rh/6QNuyzS7QeePHCEdxTKfS+5LTzpjbiFqC8BPon1+S31VSmOY3+MfevaQPv+eCxj/kFOJsnvo5JDufGyPvvnUAW1e25lRDtmHI5dYR4OLyC1clahe8DzpxOCIFtRX5XwdNFFdccHiRmoN+GLMhavaGqt9x9zWxtTWFGw5DrOlHSg+Qay7ZZLPVP9uJs/NeWUA0kGdC1eRXbiI3MJVZBe2KtoELmtp0EuvDmjTpR2KelLISIubqrWspaHQTUtbNOppX8+Auvsiamk4/cmAfGIgSl3nggYd6Yto6zW/oxMDo2qqrdC4F1XnAvf6uevYFnBV+7xavfbMet1y5UoNDI+pNlyu+nCZ2ucFv3CWbN89tYhx/bZnFBmNsoCHpNLJbTY1I7lE0IIec3OdUY65IOW3Vphiy/hLH3BbptkNOn/kCOkolvncUMjo7ee36N5NF+twb0RtjWF1tTWS+yLWPq9WnS11cdntbKnzHXPT+d1MJFpLAGZKtM9vn1erxWfUTBvDqtXV1jArR36P/8T68/X281u0YlEj+33kzNLmWt3x/gv13MFeRT2pzEgrFjdKmjgB43BvRPfuOKCt16xWRZlJayykdkUusY4GF5FbuMqvVt28tlP37jiQdJ4ryOOpRHWFJD1MrQEfjLlwVVdbg27esFw3PLAzNubevGG5utoaY7+TbHy15TjMlnag+ASx7pZOPqf3t69es1o3PPhr7e8ZSvh3yT6AXKPOhavILlxEbuEqsgtbFe1F9+XlIf1+V5va5/XqSG9ErZMnN5aXh+Z+sEWiUU/bdx2ZNemyvqs19omBHQvqCt1MoCBCIaPBYU8fu++Xsf7xxatWMelXAGwLuGxgOKqPf+fZuP1sriTad7OIgXSlk9tMa0ZyiVwIcszNR0Y55oKU31pBsmv8pQ+4LZPs5iJ/5AipKqb53B8+351wPhfFJxQyWnteizrm1805dqbzu+maay0BmC7ZRUCpjGHJagb2+8i1kTFPWx/fG5fRt5/fEsgFbGQYucI6GlxEbuGq6bVqd19ENZVlGh2Pav3y1qQXqAV9PJWorqDWgB/GXLiqvDykDSsXqXNhne+cbirjqy3HYba0A8UlqHW3VPLp198+/54LtOiMsObVViWdsyb7AHKFOheuIrtwEbmFq8gubOXWGYtpKi8PaeVZTfq95W1aeVaTcydoStK+noHYJIgkRUajun7bM9rXM1DglgGFt69nQB+7L75/fOw++kchsC3gKpv2s1OLGBd3zFfHgjoOFJBQPnNLLhGkXGSXjCLXClUrkG1kK5vskj8UEvO5cFU6Y2euxlmyh3T5ZTGdHFEzoBASZfTAiUHyCKuxjgYXkVu4bKpWfeNr5mvlWU1avbQ5aY3A8RQKjTEXLks2p8v4CuRvDs2vv33i/uc0r7aKuRIABUOdC1eRXbiI3MJVZBe2cu+sxRLT3ReJDRxTIqNRHe2PFKhFgD3oH/ZgW8BVZBcuIrdwFdmFi8gtXEV2gcKh/6FQyB6CQI5gOzIKV5FduIjcopSQdxQaGUSxIttA/tDfANiIsQmuIrtwEbmFq8gubFVe6AbYJBr1tK9nQN19EbU0hLW0uTbrT/fL9jlbGsIKV4TiBpBwRUgL68NZtQsoBvQPe7At4Kq5sht0bZCLWgOlhzEXriK7cBG5havILlw1NhbVrsO9OtwbUVtjtbraGpy72z39rzTZcLzvevZseA9R+ByRg9N4L/xlm9Hp7+vC+rDKQtLhXt5j5F6hx1cgE+QWxW56XVBTWe6b9+qKMkWjHjUCco4xFy5LNqdLtlFK8j2XM/PvLaynvwGwD7UAXEV24SJyC1eRXdjKrTMWcyga9bR91xG94/Yn9L6v/lzvuP0Jbd91RNGoF+hzPvDMIe179ZTv80ajnvYeO6UnX3xVe49N/M7S5lrdunGVwhUTmypcEdKtG1dpaXNtxu0CisXixmp95p3L4/rHZ965XIsbqwvcstLDtoCrku1nk9UGfvvsueSi1kBpWtxYrc+8a8aY+67UxtxMsgsEJdPsklsUUjZjru3oW8UtiOySEeTb2FhU333mkK7e+pT++Ju/0tVbn9R3nzmksbHo3A+2CPO5pSca9fSvO+OP9/91Z/6P913OHnMm9ihkjnK1TudiPUOfSCxRRtubaubc1jPf1z/40sR4ff22Z+dcwwWyxToaXERuUQwS1YMz64LN3/6Vbt4Qn/fNazu1+dtPZ1yHuVqLojAYc+GqsbGoHng2fk73gWdPz+mmMs/AeIliMDYW1UPPvRI3l/PQc6/kbH3Db+7opZ5Tzs4PAyhe1LlwFdmFi8gtXEV2YSvudD9pX8+Art/2TOyTMSKjUV2/7Rkt27xGHQvqAnvOv/zur7Xp0g4ta23Q+q7W2CcZTk2CTP3+1ITH+q5Wre9q1bLNa3S0f+KuC9xpAZjwn0f6dOdPduvaSzpkjOR50p0/2a1zW+q0qr2p0M0rKWwLuCoUMgn3sy8ePeVbG5x73SV6YdrPpu+zk+2fc1FroDQ9f6RPdz42Y8x9bLfOW1inlUnG3GT1JrUl8iGT7JJbFFqmY67t6FvFL9vskhEUws5XevWpB3fGHTN96sGd6lzo1txCsuNMFKe9x07pY/fFH+9/7L5ndF7LJTqnpT5v7XA5e8yZ2KWy3GjTpR2KelLITPx/PgSdA5frGfpEYn5jXXtTjX74fPec29rvfb3tkYma+c7H9iRcwwWCwDoaXERu4bpk9eDMumB/z5C+9OhuffPaN+jx3cc0HpXufmq/DvdGMqrDXK5FURiMuXDVrsO9uuGB+DndGx6YmNNdedZEdpPNMzBeohhEo55+trdHn7j/ubi+8In7n1NTTaUuOWd+4Hn2m+O47p6ntX3LGj3s4PwwgOJFnQtXkV24iNzCVWQXtuKi+0ndfZHYBMSUyGhUR/sjGZ/Akug5o55mLcrMdQLN1BeA0w6dHNL+niHd+dieWd9n55pfbAu4LBQyvvvZ/ccHfPfjL2V40msuag2UpkO9Ccbc3iGtVOIxlxO2UWiZZJfcotAyHXNtR98qftlml4ygEA6dHPI9ZnJxbiHRcSaK00s9iecP8nnRveRu9pgzsce+ngFdd8/TcdsjXBHSw3moAYLOgcv1DH0iuZlj3d5j/h+eOnNbJ3pfjTn9b781XCAIrKPBReQWrktWD/rVBft7hvTqqWHd/kh85jOpw1yuRVEYjLlw1eFe/+OsI70RrTxr7nkGxksUg309A9qx/7hvX9ix/7gWN1UHnudEcxxH+iK6uGM+/QeANahz4SqyCxeRW7iK7MJWXHQ/qaUhrHBFaNYE38L6cEbPF416qqks0+Z15yjqSff/8qAO90YUrgjJ82YvynACDZC+eXWVWtJcrcsuWBQ7KeqhZw9pXm1lYRtWgtgWcFk06mlfz4C6+yJqaTj9Kb+1leW+tUG4oiyjfXbQtQZK1/y6Kt8xd35tVdLHUW+i0DLJLrlFoWU65tqOvlX8ss0uGUEhMLcAV4UrynyzG64oK2zDHMKciT3SqQESzallKugcuFzP0CfS090XUVNNpa64aHFsHL7/lwdnbetE76vnxf/blZzALdS6cBG5heuS1YN+dcGS5mrNr6vyPc8r3TrM5VoUhcGYC1e1NVb7Hme1Nk6Mm3ONh/kaL4OewwCm6+6LKOrJty+MR5WTeTXmjgC4gjoXriK7cBG5havILmxV8IvujTFnSvq/khok/YXneU8Xoh1Lm2t168ZVsU/ODFeEdOvGVVraXJv2c0WjnrbvOhL3XJvXdureHQd09ep23f3U/lkTHEyCAOmbV1OuP3nzObrxe7tife2md3ZpXm3Bh7aSw7aAq/z22bduXKX1Xa1qaajSlnWduu2R3bGfbVnXqaaaioz22UHWGihtrY1VvmNu6xnJL6Kj3kShZZJdcotCy3TMtR19q/hlm10ygkJgbgGuml9XoT/+3XN000Ons3vj5V2aX1dR6KY5gzkTe6RaAySbU8v0pPWgc+ByPUOfSE9bY1h/+MYls+ZxWxvit7Xf+7plXafuenJ/bC3Xbw0XCAK1LlxEbuG6ZPXgzLpgSXO1Prq2Ux/42s9nnef1ifXnp12HuVyLojAYc+GqrrYG3bxhuW54YGcsuzdvWK6utkZJc4+H+RgvczGHAUzX0hDWQ88e0qcue60++/3/nFVLvOeiRXG/H0QmmTsC4ArqXLiK7MJF5BauIruwVaiQf9wYYyQ9ICkq6bikuya/f7Yx5l+MMT8zxmwzxuS8p4RCRuu7WvXw5jX69qY36OHNazKeWNvXMxCbzJAmPn3z9kd368/evkx3P7VfJwZHZk1wTE2ChCsmNgmTIMDcykxZbMcqTfS1G7+3S2WGu0jlG9sCrvLbZ1+/7Rnt6xlQ+7xadbbUadOlHbpu7TnadGmHOlvqtKylIaN9dpC1Bkrb6Jh8x9zRseSPo95EoWWSXXKLQst0zLUdfav4ZZtdMoJCYG4BrqoIlcUuuJcmsnvTQ7tUESK7qWLOxB6p1gDJ5tQyFXQOXK5n6BPpGY8qdsG9NJHH2x7ZrfH4myXOel//5aMT7+tf/cH52nRpR8I1XCAI1LpwEbmF65LVgzPrgtvfe2HsglHp9Hlet7/3wozqMJdrURQGYy5cVV4e0oaVi3Tvpov1lQ9cpHs3XawNKxepvHxi/JtrPMzHeJmLOQxguqXNtfrE+vP13V+9rFs3rtLmdefo2ks6En54TxCZZO4IgCuoc+EqsgsXkVu4iuzCVoX+2IdLJPV7nvfeyQvw9xtjGiRdIemTnuf92hhzh6R1kn6Q68aEQkYdC+rUsaAupd+PRj3t6xlQd19ELQ3h2MJMd18k7tM3pYlOX1tVpr+9emXsU5OnT3BMTYIs27xGR/sjvr8DIN6xU8O+fe3VgWGdo/oCtao0sS3gqkT77KP9EXUsqNPa81rUMb9u1r450312slojUV0BzHS03z+3x05F9JqFievYVLNLFpErmWS3EMdJ9AFMl+mYG7Sgc8kcRPHLNrtBZoRxFakqprkFcl9acp3dUslTuuszyI1Ua4C55tSy+fup5CCVfuF6zUufSF0qte/MzLx+aXMsC0uaa/Xatga96TXNzuUE7iimWhelg9zCdXPVg9PrrSdffNU370Oj4xnVBTbVoqVyTOk6xly4LBQyqg9XaHBkXPXhirTOic3HeJmrOQxg+j72vJZ63XLVKr16aliLzliowZFxveeiRTmdV2PuCIALqHPhKrILF5FbuIrswlaFvuj+UknfkiTP8zxjzAFJVZ7nfXHa74xIqixE45KJRj1t33Uk9omDU5+yub6rVS0NYYUrQnGdPlwR0pLm2qQTHEyCAOlZWO/f1xbUhQvYqtLEtoCrEu2zF9ZPZDfRvjnofXayuoKTHjDTXLlNZq7skkXkUqbZzedxEn0AM2Uz5gYlV7lkDqK4BZHdIDLCuIp02DDmBoHcl55cZpc8oRBSqQEKOWan0y+oeUvDXHmcKzPkBPnAOhpcRG5RDFLdz+eivrWhxuCY0h2MuXBVKuPMXONhrsfLYpl3hl2y2ceSSQClhDoXriK7cBG5havILmwVKvDfP0vSzmn/3yjp+NT/GGPWSlov6Sd+DzbGbDLG7DDG7Dh27Nisn4+NRfXsyye0fedhPfvySY2Nne6A0ainvcdO6ckXX9XeY6cUjXppNXxfz0BswkSa+BSN67c9o309A1raXKtbN65SuGLi7Z2aUFnaXJvW30Bxmiu3SF1ZSNqyrjOur21Z16myQo9sRSpZdtkWsNVcY64t++xkdYULktVcyEyy7GaT27m2letZRGG5MuYmQx+wQ773K7kac4NCLuHHlTG30PmlTrXPXGPuF6+Kz+0Xr7KrVkhFoXOP4AU95qYzNpEnZCPItYiZa2rtTTW+uV/cWJ3zfS/9ovilm12/cfhz716hfT2n9OzLJ7X32Ckyg5ybK7eso8FWrP/CRanUCumeE5bpXJrtc0/UznZhzIWL5hpzXRhnbFkvQX7l+hzdRNn/1YETevjXr+gXL/Vof49/DUImkQjnlsNV1LlwEfO5cBVjLlzEmAtXFfpO9yclnZIkY0yVpAHP88aNMW+X9GlJL0h6m+d5/X4P9jxvq6StkrR69eq42YmxsageePaQbnhgZ+yTBG/esFwbVi5SKGSy/iTf7r5I3KdoSBMTJ0f7I+pYUKf1Xa1atnmNjvZHtLA+rKXNtXxKMCQlzy3S88qJiO56cr+uvaRDxkieJ9315H6tXHyGls7njiRBS5ZdtgVslcqYW1lutOnSDkU9KWQm/j/f5qorbJas5iov52gnU3NlN5PcprKtXM4iCs+VMTcZ+kDhFWK/kosxN0hHev1z2d1HLkuZK2NuIcdV6lQ7zTWfO+5F43I77kU1NhZVZWVZQdqbCeqJ4hPkmJvu2ESekI2g1iIS3bnr7ee36OFp62CLG6v1vV+/kvN9L/2i+GWS3alxuKayXJ0L6/SZ7+/S/p4hhStC+vx7LiAzyLm5css6GmzF+i9cNNeYm8mdZ0Mhk/Z5Xi7MPVE724UxFy6aa8w9ctJ/nDnSa884k8kYD/fl+hzdRPvYx3cf0+2P7FG4IqQbL+/SmWcMaM05C+PyRiaRCOeWw1XUuXAR87lwFWMuXMSYC1cV+qL7n0hapYm73b9f0n2T33+TpFs8z3vAGFNhjCn3PG8snSfedbg3trAhTUxo3PDATnUurFN9uML3UwaXbV6T8mRfS0NY4YpQ3MRJuCKkhfVhSRMTIx0L6qyZPASKUUW50YnBEd352J7Y98IVIVWUMQGZb2wLuGpfz4Cuu+fpWfvzh9OoCYIwV11hs2Q118qzmgrcuuKUaW5T2VYuZxH2s2XMTYY+UHi27VdsyG1Vecg3lxV8lCeSsCG7UmHHVdvGE8ztuVd69effeW5WXs68tlqrl84rYMvSQz1RetIZc9Mdm8gTbJDozl1TGZ/K+bMvn8jLvpd+gZmmj8O3v+9CfeSeX8XlcO+xU2QGBcc6GlxEbuGqRPXrXOeEpXuelwtzT9TO7mDMhasqyk2CNSy7ssu5vAhaon3s+OT/RkajuumhXbrlypXa1zMwK3tkEkCpoM6Fq8guXERu4SqyC1sV+gztH0haboy5RVKHpJ8aYz4r6bikzxljfiLpEUkXp/vEhxPcCe5IbyTpJ/mmamlzrW7duErhiom3cOqTkZc216bbVAAZOjE0qs1rO+P64ea1nTo5NFrglpUetgVcFURNEASX64pkNRdyI9PcprKtXM4i7GfLmJsMfaDwbNuv2JDb3siIb63dHxnJWxvgHhuyKxV2XLVtPMHcjiTIbXefW9uMeqL0pDPmpjs2kSfYINWM52vfS7/ATNMzOjQ8NiuH23Yc1M0blpMZFBTraHARuYWr8jUv5sLcE7WzOxhz4Sqyi1Llt4/dvLZT//yrg7HfiYxGNTAyZtX5EACQb9QKcBXZhYvILVxFdmGrgt7p3vM8T9InZ3x7x+R/b8/mudsaq7WkuVqXXbBIZvLDLR569pBaG8OqD1dk/Um+oZDR+q5WLdu8Rkf7I1pYH9bS5lqFQnySBpAvzTWV2vqbI/rClSs1NDymmqpyfeNne/X6s19b6KaVHLYFXGXLp/u7XFe0NVb7voetjdm9h9Gop309A+rui6ilwZ33Ix8yzW0q28qGLLLti5ctY24yQfcB8py+XO1XMpWv3CbLSnNtWPfu+E9de0mHjJE8T7p3xwHd/t4LA20DiostY24ha4tMxhPG7cJqawj7zue2NNhTK6TChpoa+ZXOmJts3cIPeUIupbrfSzXj+arl6ReYmd22xtMZrakqn5XDE4MjWtZar4fJDAqIdTS4iNzCVX7165LmalVXlOnJF18NbM7HtrlsP9TO7mDMhavILkrZeS31+rv/epFqq8pVVRbSlnuf1uFpH74TrgiptrLcqvMhACDfqBXgKrILF5FbuIrswlYFveg+l85vqdd1b+nUpx7cqchoVOGKkD77ruU6v6VB5eUTn9x7/bZnYj/L5JN8QyGjjgV16lhQl6NXASCZrtYGXf26Jfrz7zwb18+XtzYUumklh20BVy1trtUd779Qzx3sVdSTyoy0YnFjQT7d39W6oqutQTdvWK4bHjhdc928Ybm62hozfs5o1NP2XUdm1Wrru1o5CUSnPzE73Vo21W1VyCyy7Qsr1xcaZprdfAuqD5Dn5BLlLRf7lWzkI7dzZaWrrUEfXdtpzXuCYJTSmFuo2iLd8YRxOzW5zG5Xa4PvfK6LcwuuHt8hM+nMLZzfUq+PvKVTn56W889MrlskQp6QC+ns91KtK/JZy9MvSpdfdr941Srd8f4Ldd09T+urj7+oGy/v0k0P7YrL4bLJ9WEyg0JhHQ0uIrdw1fT6tammUh980xI11Vbp6q1PBTrnY9tcdiLUzm5gzIWryC5KUaK5iY+//Tz92Xeei33vxsu7VF9dNmsOjQ+ABlBKqBXgKrILF5FbuIrswlZFe9H9yyeHYidoSlJkNKpPPbhTF7U36TUL6/gkX6AIHOqLJO7nYRYM84ltAZeNjHna+vjeuJMskLry8pA2rFykzoV1OtIbUWtjWF1tjSovD2X8nPt6BmKLU9LEmHL9tme0bPMaTghR5nelyMW2ChrbvnDycaFhqd1RhTwnlixvto1V+cjtXFmx7T1B9hhz8yPdvsO4PbdcZ5e5Bbgs1bmFg71DsQvupYmcf/rBnVq9pImxBnn10qv++73zPrpGr1kYn8VU6wrqVuSDX3Y/dt8z+v51l8TuZN/WGNa9//1iHekjh7AHtS5cRG7hqqn69bVb1uhXB05qX8+Abv3xzsDnfKh/ESTGXLiK7KIU+a0nTc1N3PvfL9ah3iHNr61SS2OVzmqKn0PjA6ABlBpqBbiK7MJF5BauIruwVdHO9O8/PhDrcFMio1EdOD4g6fQn+V7cMV8dC+qYsAAcNFc/R/6wLeCqRBfW7Oshu+koLw9p5VlN+r3lbVp5VlPWJ5N090V8x5Sj/RFJE4tQe4+d0pMvvqq9x04pGvWy+nsu89J86UFvq6DNte2RO/keD9PNrovIc2Jz5c3WsSpXuU0lK7a+J8gMY27+pNN3GLfnluvsMreAXMn1MWQ6fSOTsYZjYORCumNuqmtqoZBRfbhCDdUVqg9XsPaGwCXK7gtH+yVJr1/arCXNdVrZzvET7EKtCxeR2/RQt9slFDKKetJffvfXinoKbM5n5nYOhQzztggEYy5clUp22Uei2CSa4311YFgr25v0jhVn6vUdE/MTM+fG5ppLpr8AwVt0VruMMVl/LTqrvdAvxUnUuXAV2YWLyC1cRXZhq6K9031dVbnCFaG4jheuCKmuauIlR6Oe9vUMqLsvopaG0rvbF1AM5urnyB+2BXIl1/vrZCe75+IOc0G/nmKtZ1oawr5jysL6MJ/6rInt/uhvu/XcwV7VVJZp8Rk1ev5wn5a1Nujs+W5nINm2R27lYzycnt2oJ5UZacXiRq09r8Xp3CaSrzy7uC/I9/43G7nI7cxttrCesa/UFHLMfXPnQh04MejUmJEv1CFzy3V2ayv95xZqKplbQObycQyZTt9Id6wptRoa+ZPumJvKcUcQ/c3F4xvkV6Ls7nqlX9dvezatzJE35BPraHARx2ipY+0qPfnYB0ejno71D+tDazp0Xkt91nM+0ainl14d0POH+7T7aL+27TioE4MjbGcEhloBrpqrXmBuC8Viev1SkyD3qdQWieaSjw8My/NErQHkwCsHX9bVX/lZ1s9z74ffFEBrSg91LlxFduEicgtXkV3Yqmg/Yrc+XKYbL+9SuGLiJYYrQrrx8i7VhctiC17vuP0Jve+rP9c7bn9C23cd4VMBAcc0VpfppnfG9/Ob3tmlhuqyAres9CQbc4FM5WN/PXWy+3S5urAm6NdTzPXM0uZa3bpxVdyYcuvGVVraXJv3O8Pa6MDxAe3uPqUHnzmkaFT6+Hee1UfueVp/8CX3M5Bs2yO38jEeTmV36+N7dceje/SVx/dqd/epov00wnzk2dV9QT73v9kKOrd+2+ylnlOMfSWmUGPuKyeG9PDOw86NGflCHTK3XGe37Ywq33metjOqAnl+lKZ8HEOm0zfam2p084blcTm/ecNytTfV+D53qdXQyJ+WhiptWdcZl8Ut6zrV0jB7zE103DE2Fo2789ZLr2bX31w9vkF+tTZWzV6LuKxLT7xwNGnmZt4pbmwsSt6QV6xpwkW+Y+7lXWpt5BhtJtauUpePmm/qb/zR13+hOx7do1t++BvdeFlXxnM+U8/3B196Qtd962l95fG9uubiJWqqqWQ7IzDUCnDVXPUCc1soBjPrl83f/tWsOd5EtcXM+YipD2OfbklztQ6djFBrAChK1LlwFdmFi8gtXEV2Yaui/diH3qFxffmne3TtJR0yRvI86cs/3aMvvGdlwgWvZZvXWHdXPQCJnRwc19/9JL6f/91P9uj/vGdloZtWcpKNuUCm8rG/njrZ/YYHdsbuPJHsZPdsBP16irmeCYWM1ne1atnmNTraH9HC+tN3uXDp7si50t03rNse2a1rL+nQ7Y/uLqoMJNv2yK2pCw1n3oknyAsNp7I7PbO3PbJbF7U3ael8NzObTD7y7Oq+IB95C0rQufXbZtfd87S2b1mjhxn7SkahxtxXB0b0N//6G+fGjHyhDplbrrN7/NSo7zzPeS0Xqn1eIH8CJSgfx5DpzC0cODGoLz26Oy7nX3p0orbwa0+p1dDIn/Z5tepsqdOmSzsU9aSQkTpb6tQ+b/aYnui4Y+s1q7Xp7h2x3N9y5cqs+purxzfIr7FxzV6LeHyPLrtgkZ471BeXuel3oRsb93TDg7/W/p4hhStC2nrNavKGvGJNEy4aHfN8139ft+R1hW6adVi7Sl0+ar6ZHwa1v2dIX358j265cqVkpPNbG3T2/NTnfPzafPvkcd2dj+3JajtPr1daGpiLKmXUCnDVXPUCc1soBn61xZce3a17N12sodHxhOtJUxfrT19TueP9F85aZ/nsu1bE5tikYGsNACg06ly4iuzCReQWriK7sFXRXnTfHxnV/p4h3fnYnrjv90VGFfU83wWv7j4mJwCXvHpq2LefHzs1XKAWla5EY25/ZLRALUIxyMcJKgdODOqHu17RV675HZ0YGNW82gp986mXEp7sno1EryfT+qPYT+AJhYw6FtTNei1TdxCc/tptvTtyrvRFRhUZjcoY+WZgf8+AsyflcHJR4eTjQsOp7E4XGY2mXC+4mI9EY1lQXN0XBJm3XOci29xON9VWv+c70hfRxR3zc77dXOxHxahQY27U868djvZHtLS5lmzo9Lg99X78/KWekn4/Zsp1dl/pHfKdW3ild0ir1BTI38iXsbGodh3u1eHeiNoaq9XV1qDy8tDcD0Tg8nEMeeDEoL79i/36wpUrNTQypprKcn3jZ3t95xa6+yK+OU9Uvw0Mj/mO3YMjY75tYV+PVIVCRmvPa1HH/LpZY/rMMWwg4p/D5w6ejDsZePfR/qz6W7bHN+S/NBzt9x9HzeSmDleEtKAurBePntLzh/u0+2i/tu04qBODI9q8tlN3P7Vfh3sj2rH/uJPH03AXa5qpYzy3x8snBn1z+/KJQZ3TUl+gVtmJtavU5WNOe//x2XOw+3uGVFke0trzFiY8Pp85/rQ31ejAiUG90N3v22ZjstvOfhfi3bpxldZ3tTLulSBqBbhqrnphYCS9ua1EqBGRb1OZ6xkY1uHe2fXL/p4hDY2O6+KO+Qkfu69nQL890qemmkpJ0hUXLdZzB3v11vNbtH3LGh3pm5iTS1QfZVtrAIANqHPhKrILF5FbuIrswlZFe9F9c12lljRX67ILFsVOtHjo2UNqrq1UWSjku+BVU1lWoNYWFpOScFVLQ5VvP19YX1XYhpWg5lr/MXdebWVhGwan5eMEld6hEb3lvDZ9+O5fxk5muPGyLvUNjQT2N6bUVpX79pPaDOuPUj2Bx6W7I+dKU02FljRX67yWem1ed46innT/Lw/qcG9E4YqQnn75pIZGo86dlMPJRYWX6wvEp7I7cxw8o7pizseSD38u7wuCyFs+cpFNbv3a+tsjfQXbZvQjuxRizG2oKvPN34K6MNmYhr6SXC6zO7/Of55nfp1b8zxjY1E98OyhWXc937ByERfeF0A+jiF7BoZ1+QWLtOdov6KeVGakyy9YpOMDw7P6ysL6sG/OF9T51wLz66t8x+5mnzk3xi+ky29MHxuL6l93Hdbuo6cU9aTnD/fp9WfP881h+4x+tG3HQf3Nu1foL7/764z6WzbHN+S/dCTKiecpts/d++opffRbT6upplJXrV6s6992ro72R3TvjgO64qLFuvOxPaoq918rTjQeA9laWM+aZioYz+3SEPafF2sIpzcvVgpYu0pdPua0ayvLff/Gzld6dWp4TBe1n6H2efHnRc0cf5Y0V+ujazt1wwM79aE1Hb59IWSU1Xbe1xN/19zIaFTXb3tGyzav4UOAShDnP8FVDeEK3zG3frJeWDKv1n9OYV7qY2e+akTOocWU6Zm79pIOlYfkm+PWhrD2HjsVl5lo1NO/7DysT9z/XCyvf/rWcxUuD+lz23+jyGhUWx/fOyvDfs+fba0BADagzoWryC5cRG7hKrILWxXtRfflxuiPf/cc3fTQrtMX0V3epfKQ0fD4uDav7dTtj+6O/Wzz2k6NjkfnfuIiw8I1XFZZHtKfvPkc3fi90/38pnd2qaqCE5jzzRj5jrmGYQRZyMcJKuNR6abv74o7meGm7+/SN699Q2B/Y4oX9Xz7iedl9nylegJPPu4Ma7uRsXH98e+eoz/7zrNxtey9Ow7o6tXtuvup/ToxOOLcSTmcXFT8prI7cxwcGR+f87Hkw1+p7gum5CMX2eTWr61NNZWz5iPytc3oR6XFL7s3vbNLt1y5Uh+fVkPcunGVykIiG9PQVwqnqtz4z/OUu1Xr7zrcG7vgXprI0A0P7FTnwjqtPKupwK0rPfk4hqyuKNPg6Li2Pr43lt0t6zoVrpj9IXvlZf5zaOUJPo9veGxcf/rWc/W3P34h7iTNEZ+1DMYvBOE33X06eGIoLs+1VeX663cv1199d2fcPMSRk4Nxj60sN6qpLNOmSzsU9aSQmfheqrI5viH/pcMvJ5+67LXqj4zq2ks6NDg8phse2Kmmmkpdc/GSWWvBocnlo4qQ0ZZ1nbrtkd1xY3cZy0vIkcoy/1q3ssytWjfXGM/tEgr5164hxspZWLtKXT7mtFsaqmbt5zev7Yytn226tEPLWhvizouaOf5cdsGi2LH94789qj++9JzYmnK4IqTPvGu53nB2k85qynw7J7qr7dH+CGNeCeL8J7jKk+d7bCVNnAhz9nz/cf/s+amP+/moETmHFtNNz5wxEx80OXN994tXrdR/Hu6flZmmmorYBffSRF7/9scvaNOlHQkz7Fcf/c27V/h+UBAAuIY6F64iu3ARuYWryC5sVbQX3Z8cGo0t/kmTF9E9tEtbr/kdLW6q0b07DujaSzpkjOR50r07Dmj98tYCtzr/WLiGy04OjsZ2rNJEfm/83kQ/R371R8YSjrlApvJxgsqrp4Z9T2Z49dRwYH9jSv+wfz/5hz9cndHzlfIJPLm+M6ztysvKZmXp9kd36wtXrtTnHn5eh3sjkuTcSTmcXFT8/LJ700O7dNcHXz/nY8mHv1LeF0j5yUU2ufVr6+HeiO5+an9sPmLNOfP1uqXz8rLN6EelxS+7N35vl+7+b6/XwzPGjJ+/1EM2pqGvFE5/ZNx3nifTY6ZCOdzrn6EjvRGtPKtAjSpxuT6GHBgej51YLE1s79se2a2L2md/yMKR3mHf2uIbH3y9ljTPbl9zbZXu+cX+uLWMe36xX297bcus32X8QhBODI7OyvOtP3pB/3fjqllrap9914rYXbjCFSF99l0rtOnuHbPuyvVwiutN2RzfkP/SMT0nL3T369eH+nTHo3ti82Gfv2KFIqNRXXHR4tjJ8FL8/Fm4IqQzz6jRXz/8fFyu73pyvy5sP0NL55MZBK9vuDhq3VxjPLcL67/pKfW1q1TlY067fV6tOlvqdMuVK/Wb7n55nnT3U/tj9ULU06zzomaOP8Yo9v9rzl0460PcP/3gTj28eU1W7W5pCPve1XZhfTjj54S7OP8JruruG9ZdT+6fdWzVPq9GUjDjfj5qRM6hxXQzM3dicCRufTdkpLOaqrVx61OzMnPLlSt98xqdcUOW6Rku9TV/wFeoXIY7fhUF6ly4iuzCReQWriK7sFXRXnQfGY36Tl5ERqNa2lyrT6w/P3aXuatWL9afvX2ZPG/iUyunT1ZEo5729Qyouy+ilobim8xg4RouGxwd983v0MjsOz0ht9gWyJVcn6DS1lit1Usa9Ydv6tDQ8Jhqqsr1jZ/tVVtj8CczDIyM+faTwZH07pQ7HSfwlKaeUyO+WXqhuz92wpCLJ+VwclHxS5Td44Mjcz6WfCRWyvuCfOQim9xON72th3sjuvOxPQpXhHTFhYvyNsdAPyotibLbMzCi153dHDdmkI14vB+Fk4tjpkJoa6zWkuZqXXbBIk2dD/TQs4fUmoPjTNhhYGRM5y6s04cufU1sbuGrj7+owZEx39/1z/ns35UUt5Yx/Y5JfneBZPxCECIJ5nllpLLQxAVK5SHpU5e9Vm/qaI77MJ/uvoiaaip1xUWLY+Pf/b88mNZ6U6bHN+S/tEzlRJL+570T42NbY1hXXLRYNVXl2rLuHJWFQr5ZDpdPfBCE502cNH/nY3tiPyczyKWBYf8a4NSwfw1QqhjP7cL6b3qK/dyiIOV6TjsUMlp7Xot+fahXH//Os7PGFM+bfV5US0M47lj+vJZ6LWmu1v6eobgL8KdERqPa3zOQ1Xb2u6ttouM9FD/GXLhqQX2VKstPj4PGSJXlRgvqq2Lfy3bcz0eNyDm0dil0XTU9c/f/8vRd7u98bI+WNFfrr9+9QgdPDPlmZmBkzDevM5s/M8OlvOYP+IqO6eqv/CyQp7r3w28K5HmQGepcuIrswkXkFq4iu7BV0V50f/b8Wt/Ji7Pn18Y+GfC1W9boVwdO6i+/++u4BYz1Xa0KhYyiUU/bdx2ZtcAx9XOXJJqIYuEaLlvUWO2b37YzqpI8CrnAtoCrzltQp42rl+jPJ0+4CFeE9Jl3Ltd5C+oD/1ttCfpJawP9BOlZ1OSfpXk1FbF/u3hSDicXFb9E2T0zhQvQyAf85CMX2eQ23211oQ3In3SySzbi8X4UzpmJjpka3TpmOr+lXh95S6c+/eDO08eZ71qu81saCt005EjH/Fq97w3xcws3Xt6ls+fPHjeWzPNft2if5z/GpHOXI8YvBKGj2T+jNZXl2vr43rhsTfEm79R15hlh/eEbl+i2R3bHfm/Luk61NuR+vYn8l6ap7f757c/r6tXtsTvbhytC+vIHLvLN8msmT2CPRj0yg7xadEaCY7QzWJOfjvHcLqz/pq6Yzi0qFqGQ0YpFjbExZeqGLGc11ehof0RLmqvjzotqb6rRR9d26oYH4o/l73xstyT59oWnXz6podFoxtuZu9piOsZcuKq1sUrXvaVTn5o2F/rZdy0PdE43HzUi59Daw4a6qr2pRluvWa0d+48r6kmP/uaItl6zWtWVIR0+GdGHvrFDH1rT4ZuZ4wMjsYv0p9r/hfdcoJqqstjvc5wDoJRQ58JVZBcuIrdwFdmFrYr2ovvXLKjTF69apY/dd3ry5YtXrdJrJj8JMBQyinqKXXAvTXwSxvXbntGyzWvUsaBO+3oGYpM3fj93RbKJKBau4bot6zpnncRnxCJkIbAt4KLfHu3Xp7+3M25f/+nv7dR5LXVa2d4U6N96bWuDbt6wPO5kjZs3LNdr2xoD/Tsofkb+Y+5F7U369qY3OHtSDicXFb9E2TVm7m1MPuAnH7nIJrf5bqsLbUD+pJNdshGP96NwaivLfHNbV+nWFPbB3qHYBffS5HHmgzu1ekmTU3PKSF3v4KhuemhX3Da/6aFduve/Xzzrd5fMq/GdG1gyrybh86d6lyPGLwThbJ+1tc9fcYE+9eDstbSt16zWprt3xH5v6zWrY2P41O/d9shuvf21rTlvN/kvTVPbfdEZYV299am47L1wpN+3rigLxT+WzCCfWEebG33TLsVyjJYPxXJuUbGZGlPO37xGO/afiLsg9OYNy9XedPo47MCJwdhxmnT6WP7eTRdrdDyqzoV1+sT9z8Uev3ltp+5+ar9ODI5ktZ25qy2mo1aAi0ZGvdj4Kk2Mn596cKe+f90lgf2NfNSInENrj0LXVdGopx8+3x2Xhc+/5wK9qaNZB04M6pqv/UKR0aju/+XBWRfX37pxlSrLjT77/f/UtZd0qCwkrV4yT2/qaFYoZPQwxzkAShR1LlxFduEicgtXkV3YqGhXw0Iho7ctW6hvXvsGHemLqLUhrAvObIybqOjui8R9EoY0MUlztD+ijgV1c/7cRn53tJ9rIoqF6/T4vce8X4Xx8olB3fXkfl17SYeMmbijzl1P7teS5hqtWHxGoZtXUtgWcNWh3iHfff2h3iGtVFOgY355eUjvXHGmljbXxtUm5eWhIF4KSkiyMfcdK84sdPOywslFxS3beoF8wE+ucxFknWtDhm1oA/Ij3eySjXi8H4Wx77h/bpe11qtjYX2hm5cyF+eUkZ3DCbb5kb6IVs743QMnBvWlR3fH5fxLj+7WRe3BfCgD4xfSkWje6/eXt+r8ttNrRj0Dw9rfMxT32MhoVDv2H49bd5r+/9N/79ipiF6zMD6Tif52NnNx5L80hUJGgyPjs7LXNzyu+395cFZdcWH7GVo6Pz4jnpfPFqNUsY6WPvrm3HJ93kKxHKPlA8eB9gqFjDxp1gWhNzywM3YcNtWX/Lbh0Oi4Lu6Yr4uinlobwnpiz6vyPOnup/brcG9EkrLazqn0Y85RKg3UCnDVgRODvuPnyycGdU7LRL0QxDiW62N+Wz78iTG/8HWV37nWn7j/ObU0hNU3NBr7/uHeiO5+amLcbp9XraXNtXrd0nmSpI75db45Yt4KQCmizoWryC5cRG7hKrILWxXtRfdjY1E9tvuoRsc8DQyP6Ygi6hkY1rrzWmIXt7U0hBWuCMVN0oQrQlpYH07p57ZJdEf7ppqKpBNRnIiUukTv8fqu1pKb4LRBc12VTgyO6M7H9sS+F64Iqbm2qoCtKk1sC7iqua7Kd18/v7Yq4zE/2UnDj794TM8d7FXUk3Z39+vk0IjWntfizD6EBT47zDXmsp2Cw3sZLBvrBbaxXWzcHoXKrY3vRa6U0mvNp1Syy3ufG7yvmautLPfNbY1jd1Gcn+A4kzmS4tXWWO27zVsaZq8jHOmNaH/PUFzOpYmTSZmfRz5Fo56e2HNU/UPjGhgeU8/AiF4+MaA15yyctWbkefLN+Hh0xnMm+L2Za2qJ5tzefn7LrDuJsf4CPzPrrYU++94yI9+6YiqP0ainR3/bHZurLTPSisWNTs3Vwi2JjtHmUyPGYS0+dfl4r2oqy3xzW11RFsjzFxPXzi0qBdPrhaHR0x/Q09YY1hUXLZYx0rFTw2pvqtEPn+/Wb4/0Jd2GoZDR/Loq/cMTe2f9zoK65Ns52drxXP2YcbF02LiGBqSiprLcd/ycmtN1aRwr9Dm0Lr1XuVTouirRRf+/eOm4xqPRuLYd7o3oa/+2V5su7dDqJfPi9vevX9pcUtsNABKhzkWu5Pq8DLKLXCC3cBXZhYsC+RDIHLWt4PYc69PxgVF9/DvP6hP//Gt9/L5ndXxgVHuO9cV+Z2lzrW7duErhiom3YWqiamlzbUo/t02iO9pPTW5OxwJfZhK9x/t6BgrcstJUV1mmm97ZFddHb3pnl+qqWOjPN7YFXFVbUaYbL4/P7o2Xd6m6sizpmB+Netp77JSefPFV7T12StHoxC1XphbB3nH7E3rfV3+ud9z+hLbvOqJo1NOB4wPa3X1KWx/fqzse3aOvPL5Xu7tP6cBxN/YhyV4b8itcHvLNbbgixHYKEO9l8JJltxDYxnaxdXsUIre2vhe5UEqvNd/myi7vfW7wvmanscb/+Kyx2q25hcGRMW1Z1xn3Oras69TgyFiBW4Zcqa6Q77xYTeXsxZpwRch3rr6yvGiXamCpl08M6JWTw3FraK+cHNbLJ2bPU5WXadb4fPOG5fr+c4fifu+hZw/p8++5YNaaWntTTdw82oHj/nNuuw73sv6COfnVW7uPndL1bzs3Lnvzair1p289d1Yep9Z455qrTTT/C2SqoSrBOlrYrVo311iLT10+3quaBGtoNZXkdibXzi0qdjPrhWdfPqlwRUhtjWFdc/ESfe3fJvb/f/SPv9C/7Dysz29/Xtt2HNTmtZ1Jt2FZSL7H+2VJDueSzRWl0o9z0depc+zEOTdwVV2Vf71QO1kvUN+ljvdqQqHrqkTnWp+zoFaP/eborLF6y7pOrVjUqJd6TrE2BAA+ahPUubXUuchCPs7LILsIGrmFq8guXBRUbt26TVAaTg6N6aaHdsVNQt300C790wdfF/udUMhofVerlm1eo6P9ES2sj//kgrl+bptEn7I4Oj6uWzeumvUpmCzwpS/Re3y0nzsRFcLJoVHdt+OAvnDlSg2NjKm6slx3/WyvFr/tvEI3reSwLeCqo6ciKpOnW65cqYGRMdVWlmtweFSvnoro1HC575h/fGBYvznS7/vp0okWwZZtXqNj/cO67ZHdcT+77ZHduqi9SUvnZ7YPyeddNJO9NvaB+XW0f9g3t8dODbOdAsR7Gbxk2S0EtrFdbN0ehcitre9FLpTSa823ubLLe58bvK/Z6R8a15d/ukfXXtIhYyburPzln+7RLVeuLHTT0vJKb0R3Pbk/7nXc9eR+nT2/VivPKnTrkAvd/f7zYmeecZ7ObY3/3dFoVFvWdcbmB6ZOzByLRv2fHMiR7t5h3zW0uz74ei1qrNGuw7063BtRW2O1hkZnj8/f/sV+bVl3rv7yu7+OZfkT68/X289v0YpFjbE1tak7h06fR/ubd69QU02lDvdGYu2JjEZ1uJf1F8zNr976n/c+oy3rOnXtJR1a1lqnslBIn9/+vEbGPG26tEPtTTVqOyOsN3bMj82ddvclnqttn1fL3QURuOODCdbRmlhHm461+NTl473qHx7zPUb7m3evCOT5i4lr5xYVu5n1wrYdB7VlXaeGRsd1+6Px+/9P3P+crr2kQ3c+tkd3P3X6WH7NOfP1uqXz4rbh4QTH+xe2n5FwrTfZXFEq/Tjovs5dlO3FOTdwVd/QqG+98LnJeoH6LnW8VxMKWVdFo576IiPavLYzVjOEK0LavLZT456nt3e16u9+skfXveUcLairUk1VuQ6fHFRbY1hXfvlJ1oYAwMfJBHNii6hzkYV8nJdBdhE0cgtXkV24KKjcFu1F96+eGvGdhOo5NRL3vVDIqGNBXcI3ba6f26SlIaxwRSjudYcrQppXW6WL2uexwBeARO/xwvpwAVtVuvqGxrRjf6927H86/vsR7hyWb2wLuKq5NqybHvpPXXbBotgC4PefO6Tbrr5QDdUVvmN+RVkoo5MjBkfGfX+W6d0O831SBAt89mgIV+jmf5md2y+8ZyXbKUC8l8FLlt1CYBvbxdbtUYjc2vpe5EIpvdZ8myu7vPe5wfuanWP9w9rfM6Q7H9sT//0CfUBPpuqrynVicCTudYQrQqqrKtqp+JI3MOw/LzYwPPt4v3dwzPcijdcwRiDPXh0Y9t1nvTowrAeePaQbHtg5bb5ppe/4/D/fWqmHfdadpq+p7T12atY82l9+99fadGmHbn8kfpxsa6xm/QVzOpLgwxmGRsd152N79KX3XajPb38+VgePR6UvPbZbf/PuFXFzpgMjYwnnavkgJeQC62ipYS0+dfl4r/qGxnxrAHLrz6Vzi4rdzPmZqYvl/+od5/vu/6fuVH+4N6I7H9ujcEVIV1y4aNZ6a0tD2Pd4P1m/SzZXlEo/DrqvU+fYi1oBrupNUC/0TmZ3Yb3/OLagjvpuJmrh0wpRV02dg/XbI3164JlDcfO39+44oHetWqRFjdXa3zOkW374Qtxjv/yBWtaGACCBU4nW0CLjBWoRikE+zssguwgauYWryC5cFFRuQ0E3zBbz6yoVroh/eRMXoFdKmpgk2XvslJ588VXtPXZK0ahXiGYGamlzrW7duCr2uqff0X5qIurijvnqWFCX8sV4xfg+ZSPZe4z8a07Qz5trKgvUotLFtoCrKsulP3nzOfrav+3VHY/u0df+ba/+5M3nqKrCJBzzE108P/3kiOmmFsGWzKv1/Vn7vMz2IYlOitjXM5DR880l2WtDfkVGx3T16va43F69ul2R0TG2U4B4L4OXLLuFwDa2i63boxC5tfW9yIVSeq35Nld2ee9zg/c1Owvqq3zfvwV1VQVqUWbCFWXasq4z7lhyy7pOVVeUFbhlyJX5df7Zne+T3YbqithFGnc8ukd3PrZHJwZH1Fhdka/mApKkRWfUJBxzpy64lybmmw70DPr+bkO4Ys51p0SLmee21M+ac+tqa2D9BXOqKg/55vGCxWcoXBHSoZODeu/r4uvg976uXW0N4bj1ziVN/n2gfV5t0kV4IFOso6WGtfjU5eO9Irdwld/8zInBEdVXl/tm+qL2ppT6Uib9LtlcUSrPF3Rfp86xF2MuXDVXdstC8p0rLQvNeqqSRy1cWC+9OnEO1mO/OaqPzDiP7OrV7bpvx0HVhv1riakPkpz5fdaGAEBqTHAcVh9m3RSZy8d5GWQXQSO3cBXZhYuCym3R3l6nImR04+VduumhXbG7cdx4eZcqy0ze7wybS9Gop309A+rum7jQ7+3nt/jeWSTT5y6W9ykooZDR+q5WLQvoPUZ2Ksqkm97ZpRu/d7qf3/TOLpWzb807tgVcdbg3ovFoVJsu7VDUk0JGGo9GdaQ3ovPbzvAd8/f1DCT8dOn2phrdvGF53N3Abt6wXO1NNQqFjG7duGrWfvXs+cGfFJGLT0qeWuCb2X4W+PKvsjyke3ccmPXJ2p979wq2U4B4L4OXLLuFwDa2i63boxC5tfW9yIVSeq35Nld2ee9zg/c1O6Pj475zC6Pjbn1y8fy6StVWlsUdZ9ZWlqm5jpOki9Wp4VHf7A4Mj8763Za6Sm1Z16nbHtkd+90t6zq1gHwgz85vqddn3rVcn37w9BzWZ961XJGx2Xf/fvjXh/WZdy7Xp7+3M269LerN/pDmmWtWie5od35rg+9aFusvmMvg6KhuvKxLN31/2vrvZV06PhDRtZd0SJKWzKuJ2w+fs7BOe3sGdN09T8cec8f7L9QXr1qlj903e67WGCWc/wUyxTpaatgXpC4f7xW5hav81mv/9K3nqiw0O9M3Xtal6gqldJ5VJv0u2VxRKs8XdF/nLsr2YsyFqyoTZLeyfGKcOtwb0V1P7o9bq7jryf26sP0MLZ3PHcCnoxYunGjU0/OH+9RUU6n1y9t0cnAkNq/gedLdT+3XicERVZSFdP3bztWtP3ohbr8+9UGSrA0BwGzlZWbW2Hn9285VeRn7N2QuH+dlkF0EjdzCVWQXLgoqt0V70X15KKSKMumWK1dqYGRMtZXlioyNqSwUSnhn2GWb1+TkIrVcSXZRfBCvo1jep6CFQkYdC+pK+j2whfGMykKz+3lI7FzzjW0BVzVWV+pP/t/Ts04s+H8feoMk/zE/WRG2r2dAX3p0d9yC4Zce3a2L2pvUsaDO6ZMiWOCzR3V5mf7rG5bMOrisqShjOwWI9zJ4ybJbCGxju9i6PQqRW1vfi1wopdeab3Nll/c+N3hfs1NZVqb7dhzQF65cqaGRMVVXluuun+3VJ9efX+impaV9Xq3ObDqlVwdGYt87s6la7fM4wa5Y1VdV6I4du2dn9/dfO+t3B0fHfT+UYWjUrQ+XgPsO9g5p23/sn5Xbv/j9186ab3rzsoW68yfx811f/uke3f7eC+Oe02/N6o73X5jwQyin5t2mY/0Fc6muqNCXH98Vn8fH9+hjb1+mOx/bqSXN1fqL34+vHcajnj73r8/HrXded8/T2r5lje8FdnyQEnKBdbTUsS9IXa7fK3ILVx04MRi3XtvV1qBj/RH94qUTevCZQ7PqiC9etSrlvpRuv5trriiV5wuyr1Pn2IsxF+4K+WZXk9ltaQjrxOCI7nxsT+wRfNhHYtTChbGvZ0C7j/brqtWLdfuju9VUU6lrLl6i2x89/cGpN17epZdePSXPk657yzkaGY9q3bKFWrHoDNaGACCJChNSzYx1sZrKMlWGQnM/GEggLx9GSXYRMHILV5FduCio3Bb8ontjzJmS/q+kBkl/4Xne05Pfb5R0i6RzJN3sed4j6Tzv+a0N2n9iUC8c7Y+7k8FrWxv0y5dP5PXOsLmS64vi830HXSBdR08N64s/3K0rLlocWxT9518d1E3v7Cp000oO2wKu6h0c9d3X9Q7OvhvdlGRFWHdfRPt7huIWDCXF9p2unxTBAp8djp4a1tf/fV/cSUFf//d9sW3PdgoO72Ww5spuIbCN7WLj9ihUbm18L3KllF5rPqWSXd773OB9zdzxwRHt2N+rHfufnvH9xMdnNgqFjNae16KO+XWcYFciVpzZqI2vW6I//86zcXcMv+DMxlm/+/KJIf39T/fG5tDGo9Lf/3Sv/tflXepaVIDGo2R190V8x9yykGbdFfQ1C+p857sGR+I/LMJvzSrZhc1AJkbGor553PfqgMIVIX32XSu06e4dsz6o9NpLOuIeExmN6khfRBd3zPf98AdOlkfQWEeDi8gtXDVzvfbjbz9Xdzy2Rx9a0+FbR4yOR/2eJjA2zRVR59iLMReu6u6PJM0uH/YBF3T3RbRtx0Fd/7ZzFRmN6nBvRHc/tT+2xraspV7NdZX64D/tisvx1AX3kl37ewCwydFTw7rzsRfj1sXufOxF6lxkLdf7XrKLXCC3cBXZhYuCyG1BL7o3xhhJD0jaK+m4pLskrZj88dck1Un6jaR7jTFnep434vc8fiory7T+/FY919ir7r6IWhrCuuDMRlVWlmV0Z9ho1NO+noHYc+Vr0SHZ3831RfH5voMukK66cLnvp+HWVhXmbqmljG0BV9VWlfvu62qrkpdIiYqwfO47OSmidNky5haqPoa7bMluKaPfps/W3LItMRdbs5tP9BP31IUTHZ+5l1tOsCstlZVleufyNp3dXKMjfcNqbajSiraJtYiZGqr9x+e6cDA5Z+xDqhLNYTVUV+qdKxq1tLlWR/oiam0Iq7G6wvd3Wxri57sSrVklurAZyERro392L2o/Q9+89g06PjDsm8OyGTdCmGvOln05gsYxGlxEbuGqmbVuZCwa+3cqdW2xo86xE2MuXFU/R3Y5rwW2ikY9HTg+oO6+YXnydGJwREf7I7Fa4XBvRHc+tif2QX6XnNPMh0oCQAaoc+EqsgsXkVu4iuzCVqG5fyWnLpHU73neeyX9V0mNxpgGY0y7pOWSLvM8739Iel7SsnSfvLKyTKuXztMfXHCmVi+dFzvJbeoTNMMVEy9/rk/QjEY9bd91RO+4/Qm976s/1ztuf0Lbdx1RNOpl8ppTNtffnVoomi7IC/vSfZ+AfGupD2vLus64jG5Z16nWElsUtQHbAq5aWFfpm90FdZUZPV++951TJ0VMnbjMgk5paEsw5rblccwtVH0Mt9mQ3VJGv82MjbllWyIVNmY3n+gnbmJuAa6KRj09uvuYrvnHX+ij33pa1/zjL/To7mO+Y05bQ7Vvzs9srA6kHYx9SFV7U41u3rA8Los3b1iuxY3V+vFvj+oDX/u5rrvnaX3gaz/X/uMDKc135XrNCpD851+vf9u5evHoKX3gaz/Xc4f6fHO4esk81jtRUNS6cBG5hatm1rplZiK/9//yoDav7aQmgJUYc+GqVLLLeS2wTTTq6dHfdutfdx7RH339F/rYtue0ZV2ntu14WZ+67LVxed68tlPff+6QWhrC5BgAMkCdC1eRXbiI3MJVZBe2Kuid7iVdKulbkuR5nmeMOSCpStIaSd/xPG9s8vf2T34/EOl+gua+ngFdv+2Z05+CPBrV9due0bLNa3L6yb9z/d2pE0umfifoBSE+aRS2GxodV324XJsu7VDUk0Jm4hN0h0bHC920ksO2gKsGRsdVW1kWl93ayjINZphd9p3IBxMyvmNuPnNWqPoYbrMhu6WMfpsZG3PLtkQqbMxuPtFP3MTcAlyVzpizpLlWnS11cTnvbKnTkgDm9Bn7kI4DJwb1pUd369pLOmSM5HnSlx7drc6FdbNydN09T2v7ljVz3s0r12tWgBQ//7q/Z0BPv3xS41FPn/vRbxQZjcYuprv90d1xOXxTB3ekQ2FR68JF5Baumqp1v3DlSu052q9wRZn+9K3n6m9//ILufmq/Nl3aoXNb6nV+a4POnk9NADsw5sJVZBcu2tczoOcO9mrr43tjd7W/68n9umr1Yp2zoEZf/sDv6FcHTmg8Kt2744A+sf585rcAIEPUCnAV2YWLyC1cRXZhq0Lf6f4sSTun/X+jpOM+32+WdHTmg40xm4wxO4wxO44dO5bWH07nEzS7+yKxE4ymREajOtofSetvpmuuvzt1YsnDm9fo25veoIc3r9H6rtZAF4T4pNHgZZNbxDt4Ykh3Pvaixie7yXhUuvOxF3XoxFBhG1akkmWXbQFbzTXmHjwxpL//6d647P79T/dmlV32nQhCsuwe7o34jrlH+nJbm05XqPoYdptrzLUhu6WMfpuY7WPuTGxLSIy5c6Gf2Iu5BbhorjE3nTEnFDJae16LNqxapEvOadaGVYu09ryWQOYOGPswU7LsdvdFtL9nSHc+tkd3PLpHdz62R/t7hnS41z9HR/oic8535WPNCqVhrnF3av41XFGm2x/Zo4GR8VhuD/dGdPdT+3XtJR36ygcuiuWwvDzEnC1yKpW1CGpd2IhjNLgolWO0/T1DeqG7X7c/skef3/5b/dPP9unaSzr0nt9ZrP/ymvl6x/I2vWYhNQHyizEXLqLOhavmmheLeoqbAzvcG9Htj+yRJ6NLOxdow6pFWtPZrK//f69nfgt5w7nlcBV1LlxEnQtXMebCRYy5cFWh73R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\n",
      "text/plain": [
       "<Figure size 4140x4140 with 552 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(norm_new_final_data)\n",
    "\n",
    "\n",
    "            \n",
    "plt.savefig('output/그리드 전처리 완료/변수끼리 산점도_다중공선성 pairplot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'statsmodels.stats.ouliers_influence'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m-------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-74e4b0c07048>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mouliers_influence\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mvariance_inflation_factor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mvif\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mvif\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"VIF Factor\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mvarinace_inflation_factor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnorm_new_final_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnorm_new_final_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels.stats.ouliers_influence'"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.ouliers_influence import variance_inflation_factor \n",
    "\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [varinace_inflation_factor(norm_new_final_data.values, i) for i in range(norm_new_final_data.shape[1])]\n",
    "vif[\"features\"] = norm_new_final_data.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "# 피처마다의 VIF 계수를 출력합니다.\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(picher_df_corr.values, i) for i in range(picher_df_corr.shape[1])]\n",
    "vif[\"features\"] = picher_df_corr.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 다중공선성 문제와 변수 선택법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>acci_cnt</th>\n",
       "      <th>2030val</th>\n",
       "      <th>과속방지턱</th>\n",
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       "      <th>음식점</th>\n",
       "      <th>학교</th>\n",
       "      <th>교육</th>\n",
       "      <th>출근시간자전거</th>\n",
       "      <th>퇴근시간자전거</th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>다사542434</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사547430</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사549419</th>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사551417</th>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2486.75</td>\n",
       "      <td>3392.25</td>\n",
       "      <td>1914.833333</td>\n",
       "      <td>3168.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사564435</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>다사559438</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사618387</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사559464</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사585417</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>219</td>\n",
       "      <td>303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다사548430</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8988 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          acci_cnt  2030val  과속방지턱  무인카메라  신호등   버스아침승차   버스아침하차       버스저녁승차  \\\n",
       "gid                                                                             \n",
       "다사542434       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사547430       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사549419       1.0     67.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사551417       1.0     26.0    0.0    0.0  0.0  2486.75  3392.25  1914.833333   \n",
       "다사564435       1.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "...            ...      ...    ...    ...  ...      ...      ...          ...   \n",
       "다사559438       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사618387       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사559464       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "다사585417       0.0      0.0    0.0    0.0  7.0     0.00     0.00     0.000000   \n",
       "다사548430       0.0      0.0    0.0    0.0  0.0     0.00     0.00     0.000000   \n",
       "\n",
       "               버스저녁하차  관광여가오락  ...  스포츠  음식점   학교   교육  출근시간자전거  퇴근시간자전거  \\\n",
       "gid                            ...                                         \n",
       "다사542434     0.000000     0.0  ...  0.0  0.0  0.0  0.0       46      201   \n",
       "다사547430     0.000000     0.0  ...  0.0  0.0  0.0  0.0       53      137   \n",
       "다사549419     0.000000     0.0  ...  0.0  0.0  0.0  0.0        2       10   \n",
       "다사551417  3168.333333     0.0  ...  0.0  1.0  0.0  0.0        3        9   \n",
       "다사564435     0.000000     0.0  ...  0.0  3.0  0.0  0.0       16       54   \n",
       "...               ...     ...  ...  ...  ...  ...  ...      ...      ...   \n",
       "다사559438     0.000000     0.0  ...  0.0  0.0  0.0  0.0       23      105   \n",
       "다사618387     0.000000     0.0  ...  0.0  0.0  0.0  0.0        0        0   \n",
       "다사559464     0.000000     0.0  ...  0.0  0.0  0.0  0.0      135      886   \n",
       "다사585417     0.000000     0.0  ...  0.0  0.0  0.0  1.0      219      303   \n",
       "다사548430     0.000000     0.0  ...  0.0  0.0  0.0  0.0       17       26   \n",
       "\n",
       "          지하철아침승차  지하철아침하차  지하철저녁승차  지하철저녁하차  \n",
       "gid                                           \n",
       "다사542434      0.0      0.0      0.0      0.0  \n",
       "다사547430      0.0      0.0      0.0      0.0  \n",
       "다사549419      0.0      0.0      0.0      0.0  \n",
       "다사551417      0.0      0.0      0.0      0.0  \n",
       "다사564435      0.0      0.0      0.0      0.0  \n",
       "...           ...      ...      ...      ...  \n",
       "다사559438      0.0      0.0      0.0      0.0  \n",
       "다사618387      0.0      0.0      0.0      0.0  \n",
       "다사559464      0.0      0.0      0.0      0.0  \n",
       "다사585417      0.0      0.0      0.0      0.0  \n",
       "다사548430      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[8988 rows x 24 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>acci_cnt</td>     <th>  R-squared (uncentered):</th>       <td>   0.038</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th>  <td>   0.036</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>           <td>   17.65</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 20 May 2021</td> <th>  Prob (F-statistic):</th>           <td>2.33e-61</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:39:49</td>     <th>  Log-Likelihood:    </th>           <td>  9435.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  8988</td>      <th>  AIC:               </th>          <td>-1.883e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8968</td>      <th>  BIC:               </th>          <td>-1.869e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    20</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>2030val</th> <td>-4.605e-05</td> <td> 1.59e-05</td> <td>   -2.896</td> <td> 0.004</td> <td>-7.72e-05</td> <td>-1.49e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>과속방지턱</th>   <td>   -0.0019</td> <td>    0.010</td> <td>   -0.181</td> <td> 0.856</td> <td>   -0.022</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>무인카메라</th>   <td>   -0.0234</td> <td>    0.009</td> <td>   -2.677</td> <td> 0.007</td> <td>   -0.041</td> <td>   -0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>신호등</th>     <td>    0.0046</td> <td>    0.000</td> <td>    9.376</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스아침승차</th>  <td> 2.995e-08</td> <td> 2.13e-06</td> <td>    0.014</td> <td> 0.989</td> <td>-4.14e-06</td> <td>  4.2e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁승차</th>  <td>-3.295e-06</td> <td> 2.83e-06</td> <td>   -1.166</td> <td> 0.243</td> <td>-8.83e-06</td> <td> 2.24e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>버스저녁하차</th>  <td> 1.562e-05</td> <td> 3.31e-06</td> <td>    4.714</td> <td> 0.000</td> <td> 9.12e-06</td> <td> 2.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>관광여가오락</th>  <td>   -0.0005</td> <td>    0.002</td> <td>   -0.242</td> <td> 0.809</td> <td>   -0.004</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>부동산</th>     <td>-1.107e-05</td> <td>    0.001</td> <td>   -0.010</td> <td> 0.992</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>생활서비스</th>   <td>    0.0015</td> <td>    0.001</td> <td>    2.746</td> <td> 0.006</td> <td>    0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>소매점</th>     <td>   -0.0004</td> <td>    0.000</td> <td>   -2.734</td> <td> 0.006</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>숙박</th>      <td>    0.0032</td> <td>    0.004</td> <td>    0.783</td> <td> 0.434</td> <td>   -0.005</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>스포츠</th>     <td>    0.0074</td> <td>    0.007</td> <td>    1.127</td> <td> 0.260</td> <td>   -0.005</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>음식점</th>     <td>    0.0008</td> <td>    0.000</td> <td>    4.028</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>학교</th>      <td>    0.0023</td> <td>    0.007</td> <td>    0.325</td> <td> 0.745</td> <td>   -0.012</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>교육</th>      <td>   -0.0008</td> <td>    0.001</td> <td>   -1.351</td> <td> 0.177</td> <td>   -0.002</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>출근시간자전거</th> <td> 2.023e-05</td> <td> 3.15e-05</td> <td>    0.642</td> <td> 0.521</td> <td>-4.15e-05</td> <td>  8.2e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>퇴근시간자전거</th> <td> 4.966e-06</td> <td>  7.1e-06</td> <td>    0.700</td> <td> 0.484</td> <td>-8.95e-06</td> <td> 1.89e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철아침승차</th> <td>-4.473e-07</td> <td>  1.6e-07</td> <td>   -2.795</td> <td> 0.005</td> <td>-7.61e-07</td> <td>-1.34e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>지하철저녁승차</th> <td> 1.542e-07</td> <td> 5.35e-08</td> <td>    2.881</td> <td> 0.004</td> <td> 4.93e-08</td> <td> 2.59e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15491.681</td> <th>  Durbin-Watson:     </th>   <td>   0.128</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>10759906.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>12.273</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>170.717</td>  <th>  Cond. No.          </th>   <td>2.83e+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, 2.83e+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:               acci_cnt   R-squared (uncentered):                   0.038\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.036\n",
       "Method:                 Least Squares   F-statistic:                              17.65\n",
       "Date:                Thu, 20 May 2021   Prob (F-statistic):                    2.33e-61\n",
       "Time:                        17:39:49   Log-Likelihood:                          9435.9\n",
       "No. Observations:                8988   AIC:                                 -1.883e+04\n",
       "Df Residuals:                    8968   BIC:                                 -1.869e+04\n",
       "Df Model:                          20                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "2030val    -4.605e-05   1.59e-05     -2.896      0.004   -7.72e-05   -1.49e-05\n",
       "과속방지턱         -0.0019      0.010     -0.181      0.856      -0.022       0.018\n",
       "무인카메라         -0.0234      0.009     -2.677      0.007      -0.041      -0.006\n",
       "신호등            0.0046      0.000      9.376      0.000       0.004       0.006\n",
       "버스아침승차      2.995e-08   2.13e-06      0.014      0.989   -4.14e-06     4.2e-06\n",
       "버스저녁승차     -3.295e-06   2.83e-06     -1.166      0.243   -8.83e-06    2.24e-06\n",
       "버스저녁하차      1.562e-05   3.31e-06      4.714      0.000    9.12e-06    2.21e-05\n",
       "관광여가오락        -0.0005      0.002     -0.242      0.809      -0.004       0.003\n",
       "부동산        -1.107e-05      0.001     -0.010      0.992      -0.002       0.002\n",
       "생활서비스          0.0015      0.001      2.746      0.006       0.000       0.003\n",
       "소매점           -0.0004      0.000     -2.734      0.006      -0.001      -0.000\n",
       "숙박             0.0032      0.004      0.783      0.434      -0.005       0.011\n",
       "스포츠            0.0074      0.007      1.127      0.260      -0.005       0.020\n",
       "음식점            0.0008      0.000      4.028      0.000       0.000       0.001\n",
       "학교             0.0023      0.007      0.325      0.745      -0.012       0.016\n",
       "교육            -0.0008      0.001     -1.351      0.177      -0.002       0.000\n",
       "출근시간자전거     2.023e-05   3.15e-05      0.642      0.521   -4.15e-05     8.2e-05\n",
       "퇴근시간자전거     4.966e-06    7.1e-06      0.700      0.484   -8.95e-06    1.89e-05\n",
       "지하철아침승차    -4.473e-07    1.6e-07     -2.795      0.005   -7.61e-07   -1.34e-07\n",
       "지하철저녁승차     1.542e-07   5.35e-08      2.881      0.004    4.93e-08    2.59e-07\n",
       "==============================================================================\n",
       "Omnibus:                    15491.681   Durbin-Watson:                   0.128\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         10759906.886\n",
       "Skew:                          12.273   Prob(JB):                         0.00\n",
       "Kurtosis:                     170.717   Cond. No.                     2.83e+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, 2.83e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = final_data[['2030val', '과속방지턱', '무인카메라', '신호등', '버스아침승차',\n",
    "       '버스저녁승차', '버스저녁하차', '관광여가오락', '부동산', '생활서비스', '소매점', '숙박', '스포츠', '음식점',\n",
    "       '학교', '교육', '출근시간자전거', '퇴근시간자전거', '지하철아침승차', '지하철저녁승차']] \n",
    "target = final_data[[\"acci_cnt\"]]\n",
    "\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(target, x_data)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  }
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