2조(20609_김형진, 20615_장인수, 20620_최재혁)_유가변동에 의한 교통량 분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be9f73c6",
   "metadata": {},
   "source": [
    "# 프로젝트 개요\n",
    "\n",
    "[ 파일명 수정 ]\n",
    "- 2조(20609_김형진, 20615_장인수, 20620_최재혁)_유가변동에 의한 교통량 분석.ipynb\n",
    "\n",
    "[ 팀원 ]\n",
    "- 팀원 1 학번 : 20609\n",
    "- 팀원 1 이름 : 김형진\n",
    "- 팀원 2 학번 : 20615\n",
    "- 팀원 2 이름 : 장인수\n",
    "- 팀원 3 학번 : 20620\n",
    "- 팀원 3 이름 : 최재혁\n",
    " \n",
    "[ 주제 ]\n",
    "- 프로젝트 주제 : 유류 가격이라는 외부 요인에 의한 교통량 분석\n",
    "\n",
    "[ 유의사항 ]\n",
    "- 각 단계마다 Markdown과 Code를 최대한 구체적으로 작성할 것\n",
    "- 아래의 양식은 가이드라인일 뿐, 머신러닝 과정의 6단계와 필수 조건이 모두 존재한다면 양식 및 순서 등을 자유롭게 수정하여 작성 가능함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73579cac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e4335200",
   "metadata": {},
   "source": [
    "# 1. 문제 정의 \n",
    "\n",
    "[ 내용 ]\n",
    "- 프로젝트 주제를 구체적으로 설명 (주제 / 선정 이유 / 계획 등)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9251051",
   "metadata": {},
   "source": [
    "[ 문제 정의 ]\n",
    "- 주제 (# 필수 조건) : 유가변동에 의한 교통량 분석\n",
    "- 선정 이유 (# 필수 조건) : 최근 유가 상승에 관한 뉴스로 많은 사람들에게 관심이 집중 된 가운데, 이러한 유가 변동이 교통량에는 어떤 영향을 끼치는 지 분석하고 싶어 프로젝트를 선정하게 되었음\n",
    "- 계획 : 최근 3년의 유가 변동 데이터와 교통량 데이터를 불러온 뒤, 그래프로 날짜에 따른 유가변동 추이를 확인하여 알고리즘 종류를 선택한다. 선택한 이후, 회귀 혹은 분류 알고리즘을 통해 데이터를 예측할 수 있도록 한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "7924cc66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 문제 정의"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32252f4",
   "metadata": {},
   "source": [
    "# 2. 데이터 수집\n",
    "\n",
    "[ 내용 ]\n",
    "- 프로젝트 진행을 위해 필요한 데이터 수집\n",
    "- 사이킷런에 내장되어 있는 학습용 데이터 사용 금지\n",
    "- 수집한 데이터의 소스(사이트 및 링크) 반드시 기재 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc354ccc",
   "metadata": {},
   "source": [
    "[ 데이터 수집 ]\n",
    "- 데이터 파일명 (# 필수 조건) : \n",
    " - 2조(20609_김형진, 20615_장인수, 20620_최재혁)_diesel.csv\n",
    " - 2조(20609_김형진, 20615_장인수, 20620_최재혁)_gasoline.csv\n",
    " \n",
    " - 2조(20609_김형진, 20615_장인수, 20620_최재혁)_daily_traffic.csv\n",
    " - 2조(20609_김형진, 20615_장인수, 20620_최재혁)_precipitation.csv\n",
    " \n",
    " \n",
    "- 데이터 소스 (# 필수 조건)\n",
    " - 3년의 기름값 데이터 : https://www.opinet.co.kr/user/dopospdrg/dopOsPdrgAreaView.do\n",
    " - 권역 별 이용 차량 : http://data.ex.co.kr/portal/traffic/trafficRegion#\n",
    " - 기상 자료 : https://data.kma.go.kr/climate/RankState/selectRankStatisticsDivisionList.do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "24460df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 수집\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "3e149796",
   "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>구분</th>\n",
       "      <th>서울</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년06월17일</td>\n",
       "      <td>1430.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년06월18일</td>\n",
       "      <td>1431.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020년06월19일</td>\n",
       "      <td>1433.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020년06월20일</td>\n",
       "      <td>1435.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년06월21일</td>\n",
       "      <td>1436.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2022년06월13일</td>\n",
       "      <td>2140.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2022년06월14일</td>\n",
       "      <td>2149.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2022년06월15일</td>\n",
       "      <td>2155.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2022년06월16일</td>\n",
       "      <td>2159.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2022년06월17일</td>\n",
       "      <td>2164.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              구분       서울\n",
       "0    2020년06월17일  1430.14\n",
       "1    2020년06월18일  1431.91\n",
       "2    2020년06월19일  1433.78\n",
       "3    2020년06월20일  1435.17\n",
       "4    2020년06월21일  1436.21\n",
       "..           ...      ...\n",
       "726  2022년06월13일  2140.80\n",
       "727  2022년06월14일  2149.69\n",
       "728  2022년06월15일  2155.02\n",
       "729  2022년06월16일  2159.03\n",
       "730  2022년06월17일  2164.53\n",
       "\n",
       "[731 rows x 2 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gasoline_price = pd.read_csv('2조(20609_김형진, 20615_장인수, 20620_최재혁)_gasoline.csv', encoding='cp949')\n",
    "gasoline_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "122264ae",
   "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>구분</th>\n",
       "      <th>서울</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년06월17일</td>\n",
       "      <td>1240.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년06월18일</td>\n",
       "      <td>1241.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020년06월19일</td>\n",
       "      <td>1243.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020년06월20일</td>\n",
       "      <td>1245.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년06월21일</td>\n",
       "      <td>1245.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2022년06월13일</td>\n",
       "      <td>2136.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2022년06월14일</td>\n",
       "      <td>2149.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2022년06월15일</td>\n",
       "      <td>2157.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2022년06월16일</td>\n",
       "      <td>2162.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2022년06월17일</td>\n",
       "      <td>2169.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              구분       서울\n",
       "0    2020년06월17일  1240.32\n",
       "1    2020년06월18일  1241.71\n",
       "2    2020년06월19일  1243.35\n",
       "3    2020년06월20일  1245.04\n",
       "4    2020년06월21일  1245.99\n",
       "..           ...      ...\n",
       "726  2022년06월13일  2136.35\n",
       "727  2022년06월14일  2149.23\n",
       "728  2022년06월15일  2157.28\n",
       "729  2022년06월16일  2162.10\n",
       "730  2022년06월17일  2169.68\n",
       "\n",
       "[731 rows x 2 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diesel_price = pd.read_csv('2조(20609_김형진, 20615_장인수, 20620_최재혁)_diesel.csv', encoding='cp949')\n",
    "diesel_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "521e5fa6",
   "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>권역</th>\n",
       "      <th>입출구</th>\n",
       "      <th>2020.12.06</th>\n",
       "      <th>2020.12.07</th>\n",
       "      <th>2020.12.08</th>\n",
       "      <th>2020.12.09</th>\n",
       "      <th>2020.12.10</th>\n",
       "      <th>2020.12.11</th>\n",
       "      <th>2020.12.12</th>\n",
       "      <th>2020.12.13</th>\n",
       "      <th>...</th>\n",
       "      <th>2022.06.07</th>\n",
       "      <th>2022.06.08</th>\n",
       "      <th>2022.06.09</th>\n",
       "      <th>2022.06.10</th>\n",
       "      <th>2022.06.11</th>\n",
       "      <th>2022.06.12</th>\n",
       "      <th>2022.06.13</th>\n",
       "      <th>2022.06.14</th>\n",
       "      <th>2022.06.15</th>\n",
       "      <th>2022.06.16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>수도권본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>1,078,145</td>\n",
       "      <td>1,423,350</td>\n",
       "      <td>1,405,607</td>\n",
       "      <td>1,418,693</td>\n",
       "      <td>1,431,632</td>\n",
       "      <td>1,495,446</td>\n",
       "      <td>1,276,019</td>\n",
       "      <td>878,245</td>\n",
       "      <td>...</td>\n",
       "      <td>1,527,320</td>\n",
       "      <td>1,511,366</td>\n",
       "      <td>1,497,771</td>\n",
       "      <td>1,582,400</td>\n",
       "      <td>1,417,363</td>\n",
       "      <td>1,236,052</td>\n",
       "      <td>1,494,344</td>\n",
       "      <td>1,494,851</td>\n",
       "      <td>1,458,354</td>\n",
       "      <td>1,520,533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강원본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>178,826</td>\n",
       "      <td>197,219</td>\n",
       "      <td>194,636</td>\n",
       "      <td>193,647</td>\n",
       "      <td>197,559</td>\n",
       "      <td>235,676</td>\n",
       "      <td>219,394</td>\n",
       "      <td>143,367</td>\n",
       "      <td>...</td>\n",
       "      <td>216,326</td>\n",
       "      <td>214,976</td>\n",
       "      <td>214,423</td>\n",
       "      <td>258,824</td>\n",
       "      <td>255,520</td>\n",
       "      <td>209,919</td>\n",
       "      <td>221,271</td>\n",
       "      <td>211,687</td>\n",
       "      <td>206,979</td>\n",
       "      <td>220,686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>대전충남본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>273,571</td>\n",
       "      <td>308,932</td>\n",
       "      <td>298,639</td>\n",
       "      <td>300,256</td>\n",
       "      <td>301,894</td>\n",
       "      <td>334,871</td>\n",
       "      <td>300,967</td>\n",
       "      <td>228,876</td>\n",
       "      <td>...</td>\n",
       "      <td>337,029</td>\n",
       "      <td>328,145</td>\n",
       "      <td>329,460</td>\n",
       "      <td>370,499</td>\n",
       "      <td>354,027</td>\n",
       "      <td>319,281</td>\n",
       "      <td>343,045</td>\n",
       "      <td>327,413</td>\n",
       "      <td>323,828</td>\n",
       "      <td>342,168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>전북본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>105,799</td>\n",
       "      <td>113,400</td>\n",
       "      <td>107,446</td>\n",
       "      <td>107,921</td>\n",
       "      <td>111,008</td>\n",
       "      <td>131,215</td>\n",
       "      <td>129,721</td>\n",
       "      <td>88,203</td>\n",
       "      <td>...</td>\n",
       "      <td>115,309</td>\n",
       "      <td>112,753</td>\n",
       "      <td>116,710</td>\n",
       "      <td>140,792</td>\n",
       "      <td>161,900</td>\n",
       "      <td>122,368</td>\n",
       "      <td>121,501</td>\n",
       "      <td>112,034</td>\n",
       "      <td>112,161</td>\n",
       "      <td>121,599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>광주전남본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>227,531</td>\n",
       "      <td>231,053</td>\n",
       "      <td>225,886</td>\n",
       "      <td>227,321</td>\n",
       "      <td>231,144</td>\n",
       "      <td>269,108</td>\n",
       "      <td>265,556</td>\n",
       "      <td>210,126</td>\n",
       "      <td>...</td>\n",
       "      <td>238,584</td>\n",
       "      <td>231,010</td>\n",
       "      <td>238,857</td>\n",
       "      <td>275,065</td>\n",
       "      <td>292,892</td>\n",
       "      <td>253,680</td>\n",
       "      <td>240,760</td>\n",
       "      <td>226,741</td>\n",
       "      <td>238,745</td>\n",
       "      <td>251,028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>대구경북본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>323,017</td>\n",
       "      <td>360,733</td>\n",
       "      <td>355,062</td>\n",
       "      <td>357,100</td>\n",
       "      <td>357,091</td>\n",
       "      <td>394,647</td>\n",
       "      <td>371,277</td>\n",
       "      <td>283,656</td>\n",
       "      <td>...</td>\n",
       "      <td>425,293</td>\n",
       "      <td>418,355</td>\n",
       "      <td>422,592</td>\n",
       "      <td>468,905</td>\n",
       "      <td>458,059</td>\n",
       "      <td>396,703</td>\n",
       "      <td>424,691</td>\n",
       "      <td>393,165</td>\n",
       "      <td>410,879</td>\n",
       "      <td>432,608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>부산경남본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>536,274</td>\n",
       "      <td>643,109</td>\n",
       "      <td>628,480</td>\n",
       "      <td>631,374</td>\n",
       "      <td>640,220</td>\n",
       "      <td>709,891</td>\n",
       "      <td>655,618</td>\n",
       "      <td>521,947</td>\n",
       "      <td>...</td>\n",
       "      <td>677,877</td>\n",
       "      <td>668,934</td>\n",
       "      <td>679,998</td>\n",
       "      <td>743,564</td>\n",
       "      <td>716,748</td>\n",
       "      <td>624,024</td>\n",
       "      <td>680,155</td>\n",
       "      <td>618,857</td>\n",
       "      <td>673,152</td>\n",
       "      <td>709,816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>충북본부</td>\n",
       "      <td>출구</td>\n",
       "      <td>129,682</td>\n",
       "      <td>154,886</td>\n",
       "      <td>148,861</td>\n",
       "      <td>148,537</td>\n",
       "      <td>150,830</td>\n",
       "      <td>172,385</td>\n",
       "      <td>152,029</td>\n",
       "      <td>103,695</td>\n",
       "      <td>...</td>\n",
       "      <td>174,318</td>\n",
       "      <td>165,956</td>\n",
       "      <td>167,431</td>\n",
       "      <td>195,984</td>\n",
       "      <td>195,881</td>\n",
       "      <td>162,065</td>\n",
       "      <td>174,276</td>\n",
       "      <td>165,773</td>\n",
       "      <td>165,167</td>\n",
       "      <td>174,040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>전국</td>\n",
       "      <td>출구</td>\n",
       "      <td>2,852,845</td>\n",
       "      <td>3,432,682</td>\n",
       "      <td>3,364,617</td>\n",
       "      <td>3,384,849</td>\n",
       "      <td>3,421,378</td>\n",
       "      <td>3,743,239</td>\n",
       "      <td>3,370,581</td>\n",
       "      <td>2,458,115</td>\n",
       "      <td>...</td>\n",
       "      <td>3,712,056</td>\n",
       "      <td>3,651,495</td>\n",
       "      <td>3,667,242</td>\n",
       "      <td>4,036,033</td>\n",
       "      <td>3,852,390</td>\n",
       "      <td>3,324,092</td>\n",
       "      <td>3,700,043</td>\n",
       "      <td>3,550,521</td>\n",
       "      <td>3,589,265</td>\n",
       "      <td>3,772,478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 560 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       권역 입출구 2020.12.06 2020.12.07 2020.12.08 2020.12.09 2020.12.10  \\\n",
       "0   수도권본부  출구  1,078,145  1,423,350  1,405,607  1,418,693  1,431,632   \n",
       "1    강원본부  출구    178,826    197,219    194,636    193,647    197,559   \n",
       "2  대전충남본부  출구    273,571    308,932    298,639    300,256    301,894   \n",
       "3    전북본부  출구    105,799    113,400    107,446    107,921    111,008   \n",
       "4  광주전남본부  출구    227,531    231,053    225,886    227,321    231,144   \n",
       "5  대구경북본부  출구    323,017    360,733    355,062    357,100    357,091   \n",
       "6  부산경남본부  출구    536,274    643,109    628,480    631,374    640,220   \n",
       "7    충북본부  출구    129,682    154,886    148,861    148,537    150,830   \n",
       "8      전국  출구  2,852,845  3,432,682  3,364,617  3,384,849  3,421,378   \n",
       "\n",
       "  2020.12.11 2020.12.12 2020.12.13  ... 2022.06.07 2022.06.08 2022.06.09  \\\n",
       "0  1,495,446  1,276,019    878,245  ...  1,527,320  1,511,366  1,497,771   \n",
       "1    235,676    219,394    143,367  ...    216,326    214,976    214,423   \n",
       "2    334,871    300,967    228,876  ...    337,029    328,145    329,460   \n",
       "3    131,215    129,721     88,203  ...    115,309    112,753    116,710   \n",
       "4    269,108    265,556    210,126  ...    238,584    231,010    238,857   \n",
       "5    394,647    371,277    283,656  ...    425,293    418,355    422,592   \n",
       "6    709,891    655,618    521,947  ...    677,877    668,934    679,998   \n",
       "7    172,385    152,029    103,695  ...    174,318    165,956    167,431   \n",
       "8  3,743,239  3,370,581  2,458,115  ...  3,712,056  3,651,495  3,667,242   \n",
       "\n",
       "  2022.06.10 2022.06.11 2022.06.12 2022.06.13 2022.06.14 2022.06.15 2022.06.16  \n",
       "0  1,582,400  1,417,363  1,236,052  1,494,344  1,494,851  1,458,354  1,520,533  \n",
       "1    258,824    255,520    209,919    221,271    211,687    206,979    220,686  \n",
       "2    370,499    354,027    319,281    343,045    327,413    323,828    342,168  \n",
       "3    140,792    161,900    122,368    121,501    112,034    112,161    121,599  \n",
       "4    275,065    292,892    253,680    240,760    226,741    238,745    251,028  \n",
       "5    468,905    458,059    396,703    424,691    393,165    410,879    432,608  \n",
       "6    743,564    716,748    624,024    680,155    618,857    673,152    709,816  \n",
       "7    195,984    195,881    162,065    174,276    165,773    165,167    174,040  \n",
       "8  4,036,033  3,852,390  3,324,092  3,700,043  3,550,521  3,589,265  3,772,478  \n",
       "\n",
       "[9 rows x 560 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_traffic = pd.read_csv('2조(20609_김형진, 20615_장인수, 20620_최재혁)_daily_traffic.csv', encoding='cp949')\n",
    "daily_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "20fff1c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지점번호</th>\n",
       "      <th>지점명</th>\n",
       "      <th>일시</th>\n",
       "      <th>강수량(mm)</th>\n",
       "      <th>1시간최다강수량(mm)</th>\n",
       "      <th>1시간최다강수량시각</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>4:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2022-06-15</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1:44</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0:35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>108</td>\n",
       "      <td>서울</td>\n",
       "      <td>2022-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     지점번호 지점명          일시  강수량(mm)  1시간최다강수량(mm) 1시간최다강수량시각 Unnamed: 6\n",
       "0     108  서울  2020-01-01      0.1           NaN        NaN        NaN\n",
       "1     108  서울  2020-01-02      NaN           NaN        NaN        NaN\n",
       "2     108  서울  2020-01-03      NaN           NaN        NaN        NaN\n",
       "3     108  서울  2020-01-04      NaN           NaN        NaN        NaN\n",
       "4     108  서울  2020-01-05      NaN           NaN        NaN        NaN\n",
       "..    ...  ..         ...      ...           ...        ...        ...\n",
       "894   108  서울  2022-06-13     18.0          15.5       4:00        NaN\n",
       "895   108  서울  2022-06-14      0.0           0.0        NaN        NaN\n",
       "896   108  서울  2022-06-15     18.5           5.8       1:44        NaN\n",
       "897   108  서울  2022-06-16      0.7           0.3       0:35        NaN\n",
       "898   108  서울  2022-06-17      NaN           NaN        NaN        NaN\n",
       "\n",
       "[899 rows x 7 columns]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precipitation = pd.read_csv('2조(20609_김형진, 20615_장인수, 20620_최재혁)_precipitation.csv', encoding='cp949')\n",
    "precipitation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9e3a03",
   "metadata": {},
   "source": [
    "# 3. 데이터 전처리\n",
    "\n",
    "[ 내용 ]\n",
    "- 데이터 분석 및 모델 학습을 위한 구조로 데이터를 가공하거나 변형\n",
    "- 기본적으로 코드(판다스 및 넘파이 라이브러리 등 활용)를 통해 전처리 작업을 수행해야 하지만, 힘들 경우 데이터 파일 자체를 수정하여 전처리하여도 됨\n",
    "- (# 필수 조건 : 위의 경우, 수행해야 하는 전처리 작업과 수행하지 못한 이유를 작성해야 함)\n",
    "- 모든 전처리 작업이 수행된 최종 데이터를 명시해야 함"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95be6fa",
   "metadata": {},
   "source": [
    "[ 데이터 전처리 ]\n",
    "- 필요한 전처리 작업 : 결측치 확인, 데이터를 result로 합치기, 강수량이 있을 때의 교통량을 제거\n",
    "- 전처리 결과 : result 값을 출력한 것을 보면, 잘 된 것을 볼 수 있다. (result 출력은 전처리 맨 끝에 있음)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "14aa1f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "2fba4e0d",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>권역</th>\n",
       "      <td>수도권본부</td>\n",
       "      <td>강원본부</td>\n",
       "      <td>대전충남본부</td>\n",
       "      <td>전북본부</td>\n",
       "      <td>광주전남본부</td>\n",
       "      <td>대구경북본부</td>\n",
       "      <td>부산경남본부</td>\n",
       "      <td>충북본부</td>\n",
       "      <td>전국</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>입출구</th>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "      <td>출구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.06</th>\n",
       "      <td>1,078,145</td>\n",
       "      <td>178,826</td>\n",
       "      <td>273,571</td>\n",
       "      <td>105,799</td>\n",
       "      <td>227,531</td>\n",
       "      <td>323,017</td>\n",
       "      <td>536,274</td>\n",
       "      <td>129,682</td>\n",
       "      <td>2,852,845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.07</th>\n",
       "      <td>1,423,350</td>\n",
       "      <td>197,219</td>\n",
       "      <td>308,932</td>\n",
       "      <td>113,400</td>\n",
       "      <td>231,053</td>\n",
       "      <td>360,733</td>\n",
       "      <td>643,109</td>\n",
       "      <td>154,886</td>\n",
       "      <td>3,432,682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.08</th>\n",
       "      <td>1,405,607</td>\n",
       "      <td>194,636</td>\n",
       "      <td>298,639</td>\n",
       "      <td>107,446</td>\n",
       "      <td>225,886</td>\n",
       "      <td>355,062</td>\n",
       "      <td>628,480</td>\n",
       "      <td>148,861</td>\n",
       "      <td>3,364,617</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.12</th>\n",
       "      <td>1,236,052</td>\n",
       "      <td>209,919</td>\n",
       "      <td>319,281</td>\n",
       "      <td>122,368</td>\n",
       "      <td>253,680</td>\n",
       "      <td>396,703</td>\n",
       "      <td>624,024</td>\n",
       "      <td>162,065</td>\n",
       "      <td>3,324,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.13</th>\n",
       "      <td>1,494,344</td>\n",
       "      <td>221,271</td>\n",
       "      <td>343,045</td>\n",
       "      <td>121,501</td>\n",
       "      <td>240,760</td>\n",
       "      <td>424,691</td>\n",
       "      <td>680,155</td>\n",
       "      <td>174,276</td>\n",
       "      <td>3,700,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.14</th>\n",
       "      <td>1,494,851</td>\n",
       "      <td>211,687</td>\n",
       "      <td>327,413</td>\n",
       "      <td>112,034</td>\n",
       "      <td>226,741</td>\n",
       "      <td>393,165</td>\n",
       "      <td>618,857</td>\n",
       "      <td>165,773</td>\n",
       "      <td>3,550,521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.15</th>\n",
       "      <td>1,458,354</td>\n",
       "      <td>206,979</td>\n",
       "      <td>323,828</td>\n",
       "      <td>112,161</td>\n",
       "      <td>238,745</td>\n",
       "      <td>410,879</td>\n",
       "      <td>673,152</td>\n",
       "      <td>165,167</td>\n",
       "      <td>3,589,265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.16</th>\n",
       "      <td>1,520,533</td>\n",
       "      <td>220,686</td>\n",
       "      <td>342,168</td>\n",
       "      <td>121,599</td>\n",
       "      <td>251,028</td>\n",
       "      <td>432,608</td>\n",
       "      <td>709,816</td>\n",
       "      <td>174,040</td>\n",
       "      <td>3,772,478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0        1        2        3        4        5        6  \\\n",
       "권역              수도권본부     강원본부   대전충남본부     전북본부   광주전남본부   대구경북본부   부산경남본부   \n",
       "입출구                출구       출구       출구       출구       출구       출구       출구   \n",
       "2020.12.06  1,078,145  178,826  273,571  105,799  227,531  323,017  536,274   \n",
       "2020.12.07  1,423,350  197,219  308,932  113,400  231,053  360,733  643,109   \n",
       "2020.12.08  1,405,607  194,636  298,639  107,446  225,886  355,062  628,480   \n",
       "...               ...      ...      ...      ...      ...      ...      ...   \n",
       "2022.06.12  1,236,052  209,919  319,281  122,368  253,680  396,703  624,024   \n",
       "2022.06.13  1,494,344  221,271  343,045  121,501  240,760  424,691  680,155   \n",
       "2022.06.14  1,494,851  211,687  327,413  112,034  226,741  393,165  618,857   \n",
       "2022.06.15  1,458,354  206,979  323,828  112,161  238,745  410,879  673,152   \n",
       "2022.06.16  1,520,533  220,686  342,168  121,599  251,028  432,608  709,816   \n",
       "\n",
       "                  7          8  \n",
       "권역             충북본부         전국  \n",
       "입출구              출구         출구  \n",
       "2020.12.06  129,682  2,852,845  \n",
       "2020.12.07  154,886  3,432,682  \n",
       "2020.12.08  148,861  3,364,617  \n",
       "...             ...        ...  \n",
       "2022.06.12  162,065  3,324,092  \n",
       "2022.06.13  174,276  3,700,043  \n",
       "2022.06.14  165,773  3,550,521  \n",
       "2022.06.15  165,167  3,589,265  \n",
       "2022.06.16  174,040  3,772,478  \n",
       "\n",
       "[560 rows x 9 columns]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_traffic = daily_traffic.transpose()\n",
    "daily_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "e88774aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "휘발유 데이터의 결측치\n",
      " 구분    0\n",
      "서울    0\n",
      "dtype: int64\n",
      "\n",
      "경유 데이터의 결측치\n",
      " 구분    0\n",
      "서울    0\n",
      "dtype: int64\n",
      "\n",
      "일일 교통량 데이터의 결측치\n",
      " 0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "5    0\n",
      "6    0\n",
      "7    0\n",
      "8    0\n",
      "dtype: int64\n",
      "\n",
      "강수량 데이터의 결측치\n",
      " 지점번호              0\n",
      "지점명               0\n",
      "일시                0\n",
      "강수량(mm)         531\n",
      "1시간최다강수량(mm)    669\n",
      "1시간최다강수량시각      744\n",
      "Unnamed: 6      750\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('휘발유 데이터의 결측치\\n',gasoline_price.isna().sum(), end='\\n\\n')\n",
    "print('경유 데이터의 결측치\\n', diesel_price.isna().sum(), end='\\n\\n')\n",
    "print('일일 교통량 데이터의 결측치\\n', daily_traffic.isna().sum(), end='\\n\\n')\n",
    "print('강수량 데이터의 결측치\\n', precipitation.isna().sum(), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "c2b401ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "강수량 데이터의 결측치\n",
      " 지점번호            0\n",
      "지점명             0\n",
      "일시              0\n",
      "강수량(mm)         0\n",
      "1시간최다강수량(mm)    0\n",
      "1시간최다강수량시각      0\n",
      "Unnamed: 6      0\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "precipitation = precipitation.fillna(0)\n",
    "print('강수량 데이터의 결측치\\n', precipitation.isna().sum(), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "53d405e0",
   "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>전국</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020.12.06</th>\n",
       "      <td>2,852,845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.07</th>\n",
       "      <td>3,432,682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.08</th>\n",
       "      <td>3,364,617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.09</th>\n",
       "      <td>3,384,849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.12.10</th>\n",
       "      <td>3,421,378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.12</th>\n",
       "      <td>3,324,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.13</th>\n",
       "      <td>3,700,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.14</th>\n",
       "      <td>3,550,521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.15</th>\n",
       "      <td>3,589,265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.06.16</th>\n",
       "      <td>3,772,478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   전국\n",
       "2020.12.06  2,852,845\n",
       "2020.12.07  3,432,682\n",
       "2020.12.08  3,364,617\n",
       "2020.12.09  3,384,849\n",
       "2020.12.10  3,421,378\n",
       "...               ...\n",
       "2022.06.12  3,324,092\n",
       "2022.06.13  3,700,043\n",
       "2022.06.14  3,550,521\n",
       "2022.06.15  3,589,265\n",
       "2022.06.16  3,772,478\n",
       "\n",
       "[558 rows x 1 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_traffic = daily_traffic.drop(labels=['권역', '입출구'], axis=0)\n",
    "daily_traffic = daily_traffic.drop(columns=list(range(0,8)), axis=1)\n",
    "daily_traffic = daily_traffic.rename(columns={8:'전국'})\n",
    "daily_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "1ea2c3e2",
   "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>날짜</th>\n",
       "      <th>전국</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020.12.06</td>\n",
       "      <td>2,852,845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020.12.07</td>\n",
       "      <td>3,432,682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020.12.08</td>\n",
       "      <td>3,364,617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020.12.09</td>\n",
       "      <td>3,384,849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020.12.10</td>\n",
       "      <td>3,421,378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2022.06.12</td>\n",
       "      <td>3,324,092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>2022.06.13</td>\n",
       "      <td>3,700,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>2022.06.14</td>\n",
       "      <td>3,550,521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2022.06.15</td>\n",
       "      <td>3,589,265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2022.06.16</td>\n",
       "      <td>3,772,478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             날짜         전국\n",
       "0    2020.12.06  2,852,845\n",
       "1    2020.12.07  3,432,682\n",
       "2    2020.12.08  3,364,617\n",
       "3    2020.12.09  3,384,849\n",
       "4    2020.12.10  3,421,378\n",
       "..          ...        ...\n",
       "553  2022.06.12  3,324,092\n",
       "554  2022.06.13  3,700,043\n",
       "555  2022.06.14  3,550,521\n",
       "556  2022.06.15  3,589,265\n",
       "557  2022.06.16  3,772,478\n",
       "\n",
       "[558 rows x 2 columns]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_traffic = daily_traffic.reset_index()\n",
    "daily_traffic = daily_traffic.rename(columns={'index':'날짜'})\n",
    "daily_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "ef81cb9d",
   "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>날짜</th>\n",
       "      <th>전국</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20201206</td>\n",
       "      <td>2852845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20201207</td>\n",
       "      <td>3432682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20201208</td>\n",
       "      <td>3364617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20201209</td>\n",
       "      <td>3384849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20201210</td>\n",
       "      <td>3421378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>20220612</td>\n",
       "      <td>3324092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>20220613</td>\n",
       "      <td>3700043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>20220614</td>\n",
       "      <td>3550521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>20220615</td>\n",
       "      <td>3589265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>20220616</td>\n",
       "      <td>3772478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜       전국\n",
       "0    20201206  2852845\n",
       "1    20201207  3432682\n",
       "2    20201208  3364617\n",
       "3    20201209  3384849\n",
       "4    20201210  3421378\n",
       "..        ...      ...\n",
       "553  20220612  3324092\n",
       "554  20220613  3700043\n",
       "555  20220614  3550521\n",
       "556  20220615  3589265\n",
       "557  20220616  3772478\n",
       "\n",
       "[558 rows x 2 columns]"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(daily_traffic)):\n",
    "  daily_traffic['날짜'][i] = daily_traffic['날짜'][i].replace('.', '')\n",
    "  daily_traffic['전국'][i] = daily_traffic['전국'][i].replace(',', '')\n",
    "daily_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "0f9f3cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_10256\\2749514706.py:2: 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",
      "  gasoline_price['구분'][i] = gasoline_price['구분'][i].replace('년', '').replace('월', '').replace('일', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>휘발유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200617</td>\n",
       "      <td>1430.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200618</td>\n",
       "      <td>1431.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200619</td>\n",
       "      <td>1433.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200620</td>\n",
       "      <td>1435.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200621</td>\n",
       "      <td>1436.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>20220613</td>\n",
       "      <td>2140.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>20220614</td>\n",
       "      <td>2149.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>20220615</td>\n",
       "      <td>2155.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>20220616</td>\n",
       "      <td>2159.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>20220617</td>\n",
       "      <td>2164.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜      휘발유\n",
       "0    20200617  1430.14\n",
       "1    20200618  1431.91\n",
       "2    20200619  1433.78\n",
       "3    20200620  1435.17\n",
       "4    20200621  1436.21\n",
       "..        ...      ...\n",
       "726  20220613  2140.80\n",
       "727  20220614  2149.69\n",
       "728  20220615  2155.02\n",
       "729  20220616  2159.03\n",
       "730  20220617  2164.53\n",
       "\n",
       "[731 rows x 2 columns]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(gasoline_price)):\n",
    "  gasoline_price['구분'][i] = gasoline_price['구분'][i].replace('년', '').replace('월', '').replace('일', '')\n",
    "\n",
    "gasoline_price = gasoline_price.rename(columns={'구분':'날짜', '서울':'휘발유'})\n",
    "gasoline_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "81c9d0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_10256\\2067969374.py:2: 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",
      "  diesel_price['구분'][i] = diesel_price['구분'][i].replace('년', '').replace('월', '').replace('일', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>경유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200617</td>\n",
       "      <td>1240.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200618</td>\n",
       "      <td>1241.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200619</td>\n",
       "      <td>1243.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200620</td>\n",
       "      <td>1245.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200621</td>\n",
       "      <td>1245.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>20220613</td>\n",
       "      <td>2136.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>20220614</td>\n",
       "      <td>2149.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>20220615</td>\n",
       "      <td>2157.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>20220616</td>\n",
       "      <td>2162.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>20220617</td>\n",
       "      <td>2169.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜       경유\n",
       "0    20200617  1240.32\n",
       "1    20200618  1241.71\n",
       "2    20200619  1243.35\n",
       "3    20200620  1245.04\n",
       "4    20200621  1245.99\n",
       "..        ...      ...\n",
       "726  20220613  2136.35\n",
       "727  20220614  2149.23\n",
       "728  20220615  2157.28\n",
       "729  20220616  2162.10\n",
       "730  20220617  2169.68\n",
       "\n",
       "[731 rows x 2 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(diesel_price)):\n",
    "  diesel_price['구분'][i] = diesel_price['구분'][i].replace('년', '').replace('월', '').replace('일', '')\n",
    "\n",
    "diesel_price = diesel_price.rename(columns={'구분':'날짜', '서울':'경유'})\n",
    "diesel_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "fae575cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_10256\\4233384256.py:5: 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",
      "  precipitation['날짜'][i] = precipitation['날짜'][i].replace('-', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>강수량(mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200101</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200102</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200103</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200104</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200105</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>20220613</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>20220614</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>20220615</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>20220616</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>20220617</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>899 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜  강수량(mm)\n",
       "0    20200101      0.1\n",
       "1    20200102      0.0\n",
       "2    20200103      0.0\n",
       "3    20200104      0.0\n",
       "4    20200105      0.0\n",
       "..        ...      ...\n",
       "894  20220613     18.0\n",
       "895  20220614      0.0\n",
       "896  20220615     18.5\n",
       "897  20220616      0.7\n",
       "898  20220617      0.0\n",
       "\n",
       "[899 rows x 2 columns]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precipitation = precipitation.rename(columns={'일시':'날짜'})\n",
    "precipitation = precipitation.drop(columns=['지점번호', '지점명', '1시간최다강수량(mm)', '1시간최다강수량시각', 'Unnamed: 6'])\n",
    "\n",
    "for i in range(len(precipitation)):\n",
    "  precipitation['날짜'][i] = precipitation['날짜'][i].replace('-', '')\n",
    "  \n",
    "precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "df2dc696",
   "metadata": {},
   "outputs": [],
   "source": [
    "diesel_price = diesel_price[diesel_price['날짜'].between('20201206','20220616')]\n",
    "gasoline_price = gasoline_price[gasoline_price['날짜'].between('20201206','20220616')]\n",
    "precipitation = precipitation[precipitation['날짜'].between('20201206','20220616')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "3bd21434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "날짜    object\n",
       "전국     int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_traffic = daily_traffic.astype({'전국':'int'})\n",
    "daily_traffic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "85148a76",
   "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>날짜</th>\n",
       "      <th>전국</th>\n",
       "      <th>휘발유</th>\n",
       "      <th>경유</th>\n",
       "      <th>강수량(mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20201206</td>\n",
       "      <td>2852845</td>\n",
       "      <td>1420.48</td>\n",
       "      <td>1227.59</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20201207</td>\n",
       "      <td>3432682</td>\n",
       "      <td>1421.92</td>\n",
       "      <td>1228.70</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20201208</td>\n",
       "      <td>3364617</td>\n",
       "      <td>1424.18</td>\n",
       "      <td>1230.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20201209</td>\n",
       "      <td>3384849</td>\n",
       "      <td>1425.68</td>\n",
       "      <td>1231.72</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20201210</td>\n",
       "      <td>3421378</td>\n",
       "      <td>1427.67</td>\n",
       "      <td>1233.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>20220612</td>\n",
       "      <td>3324092</td>\n",
       "      <td>2132.46</td>\n",
       "      <td>2126.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>20220613</td>\n",
       "      <td>3700043</td>\n",
       "      <td>2140.80</td>\n",
       "      <td>2136.35</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>20220614</td>\n",
       "      <td>3550521</td>\n",
       "      <td>2149.69</td>\n",
       "      <td>2149.23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>20220615</td>\n",
       "      <td>3589265</td>\n",
       "      <td>2155.02</td>\n",
       "      <td>2157.28</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>20220616</td>\n",
       "      <td>3772478</td>\n",
       "      <td>2159.03</td>\n",
       "      <td>2162.10</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜       전국      휘발유       경유  강수량(mm)\n",
       "0    20201206  2852845  1420.48  1227.59      0.0\n",
       "1    20201207  3432682  1421.92  1228.70      0.0\n",
       "2    20201208  3364617  1424.18  1230.83      0.0\n",
       "3    20201209  3384849  1425.68  1231.72      0.0\n",
       "4    20201210  3421378  1427.67  1233.75      0.0\n",
       "..        ...      ...      ...      ...      ...\n",
       "553  20220612  3324092  2132.46  2126.05      0.0\n",
       "554  20220613  3700043  2140.80  2136.35     18.0\n",
       "555  20220614  3550521  2149.69  2149.23      0.0\n",
       "556  20220615  3589265  2155.02  2157.28     18.5\n",
       "557  20220616  3772478  2159.03  2162.10      0.7\n",
       "\n",
       "[558 rows x 5 columns]"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.merge(daily_traffic, gasoline_price)\n",
    "result = pd.merge(result, diesel_price)\n",
    "result = pd.merge(result, precipitation)\n",
    "result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "e8283e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "rain = result['강수량(mm)']\n",
    "\n",
    "for i in range(len(rain)):\n",
    "    if(rain[i] != 0):\n",
    "        result = result.drop(labels=i, axis=0)\n",
    "\n",
    "result = result.reset_index()\n",
    "result = result.drop(columns='index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "c271f703",
   "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>전국</th>\n",
       "      <th>휘발유</th>\n",
       "      <th>경유</th>\n",
       "      <th>강수량(mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.990000e+02</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.536003e+06</td>\n",
       "      <td>1741.455238</td>\n",
       "      <td>1578.422857</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.680564e+05</td>\n",
       "      <td>178.356593</td>\n",
       "      <td>231.651761</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.054131e+06</td>\n",
       "      <td>1420.480000</td>\n",
       "      <td>1227.590000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.380876e+06</td>\n",
       "      <td>1615.050000</td>\n",
       "      <td>1414.925000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.604112e+06</td>\n",
       "      <td>1722.470000</td>\n",
       "      <td>1524.750000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.739918e+06</td>\n",
       "      <td>1820.495000</td>\n",
       "      <td>1637.910000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.368889e+06</td>\n",
       "      <td>2149.690000</td>\n",
       "      <td>2149.230000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 전국          휘발유           경유  강수량(mm)\n",
       "count  3.990000e+02   399.000000   399.000000    399.0\n",
       "mean   3.536003e+06  1741.455238  1578.422857      0.0\n",
       "std    3.680564e+05   178.356593   231.651761      0.0\n",
       "min    2.054131e+06  1420.480000  1227.590000      0.0\n",
       "25%    3.380876e+06  1615.050000  1414.925000      0.0\n",
       "50%    3.604112e+06  1722.470000  1524.750000      0.0\n",
       "75%    3.739918e+06  1820.495000  1637.910000      0.0\n",
       "max    4.368889e+06  2149.690000  2149.230000      0.0"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "a413dde4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 399 entries, 0 to 398\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   날짜       399 non-null    object \n",
      " 1   전국       399 non-null    int32  \n",
      " 2   휘발유      399 non-null    float64\n",
      " 3   경유       399 non-null    float64\n",
      " 4   강수량(mm)  399 non-null    float64\n",
      "dtypes: float64(3), int32(1), object(1)\n",
      "memory usage: 14.2+ KB\n"
     ]
    }
   ],
   "source": [
    "result.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "d3368f34",
   "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>날짜</th>\n",
       "      <th>교통량</th>\n",
       "      <th>휘발유</th>\n",
       "      <th>경유</th>\n",
       "      <th>강수량(mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20201206</td>\n",
       "      <td>2852845</td>\n",
       "      <td>1420.48</td>\n",
       "      <td>1227.59</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20201207</td>\n",
       "      <td>3432682</td>\n",
       "      <td>1421.92</td>\n",
       "      <td>1228.70</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20201208</td>\n",
       "      <td>3364617</td>\n",
       "      <td>1424.18</td>\n",
       "      <td>1230.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20201209</td>\n",
       "      <td>3384849</td>\n",
       "      <td>1425.68</td>\n",
       "      <td>1231.72</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20201210</td>\n",
       "      <td>3421378</td>\n",
       "      <td>1427.67</td>\n",
       "      <td>1233.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>20220607</td>\n",
       "      <td>3712056</td>\n",
       "      <td>2105.60</td>\n",
       "      <td>2086.53</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>20220608</td>\n",
       "      <td>3651495</td>\n",
       "      <td>2112.39</td>\n",
       "      <td>2096.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>20220611</td>\n",
       "      <td>3852390</td>\n",
       "      <td>2129.47</td>\n",
       "      <td>2121.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>20220612</td>\n",
       "      <td>3324092</td>\n",
       "      <td>2132.46</td>\n",
       "      <td>2126.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>20220614</td>\n",
       "      <td>3550521</td>\n",
       "      <td>2149.69</td>\n",
       "      <td>2149.23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜      교통량      휘발유       경유  강수량(mm)\n",
       "0    20201206  2852845  1420.48  1227.59      0.0\n",
       "1    20201207  3432682  1421.92  1228.70      0.0\n",
       "2    20201208  3364617  1424.18  1230.83      0.0\n",
       "3    20201209  3384849  1425.68  1231.72      0.0\n",
       "4    20201210  3421378  1427.67  1233.75      0.0\n",
       "..        ...      ...      ...      ...      ...\n",
       "394  20220607  3712056  2105.60  2086.53      0.0\n",
       "395  20220608  3651495  2112.39  2096.07      0.0\n",
       "396  20220611  3852390  2129.47  2121.67      0.0\n",
       "397  20220612  3324092  2132.46  2126.05      0.0\n",
       "398  20220614  3550521  2149.69  2149.23      0.0\n",
       "\n",
       "[399 rows x 5 columns]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = result.rename(columns={'전국':'교통량'})\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59689c79",
   "metadata": {},
   "source": [
    "# 4. 데이터 분석\n",
    "\n",
    "[ 내용 ]\n",
    "- 수집한 데이터의 탐색 및 분석을 진행\n",
    "- 반드시 하나 이상의 그래프(맷플롯립 라이브러리 등 활용)를 활용해야 함 (# 필수 조건)\n",
    "- 매 데이터 분석의 결과를 명시해야 함 (# 필수 조건)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "35d8359a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "03707806",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "result_day = result['날짜'][0]\n",
    "check_day = datetime.date(int(result_day[:4]), int(result_day[4:6]), int(result_day[6:]))\n",
    "graph_gas_price_xlabels = []\n",
    "\n",
    "for i in range(int((len(result)/ 30))+1):\n",
    "  graph_gas_price_xlabels.append(check_day + relativedelta(months=1)*i)\n",
    "\n",
    "for i in range(len(graph_gas_price_xlabels)):\n",
    "  graph_gas_price_xlabels[i] = str(graph_gas_price_xlabels[i])[:7].replace('-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "0e673a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day = result['날짜']\n",
    "daily_traffic_in = result['교통량']\n",
    "\n",
    "plt.rc('font', family='Malgun Gothic', size='12')\n",
    "plt.figure(figsize=(20, 12))\n",
    "\n",
    "plt.xlabel('날짜')\n",
    "plt.ylabel('교통량')\n",
    "\n",
    "plt.xticks(np.arange(0, len(result), 30), labels=graph_gas_price_xlabels)\n",
    "\n",
    "plt.plot(daily_traffic_in, c='red', label='교통량')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7dbc806",
   "metadata": {},
   "source": [
    "[ 데이터 분석 ]\n",
    "- 분석 내용 : 날짜에 대한 교통량 분석\n",
    "- 분석 결과 : 교통량을 분석했을 때, 날짜마다 변동 추세가 큰 교통량을 나타내는 것을 알 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "b926d62e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day = result['날짜']\n",
    "gasoline_price_in = result['휘발유']\n",
    "diesel_price_in = result['경유']\n",
    "\n",
    "plt.rc('font', family='Malgun Gothic', size='12')\n",
    "plt.figure(figsize=(20, 12))\n",
    "\n",
    "plt.xlabel('날짜')\n",
    "plt.ylabel('가격')\n",
    "\n",
    "plt.xticks(np.arange(0, len(result), 30), labels=graph_gas_price_xlabels)\n",
    "\n",
    "plt.plot(gasoline_price_in, c='green', label='휘발유 가격')\n",
    "plt.plot(diesel_price_in, c='skyblue', label='경유 가격')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71bb9fa0",
   "metadata": {},
   "source": [
    "[ 데이터 분석 ]\n",
    "- 분석 내용 : 날짜에 대한 유가변동 분석\n",
    "- 분석 결과 : 유가가 중간에 내린 적도 있지만, 대체적으로 날짜가 지날 수록 올라가는 경향을 볼 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f57dc4",
   "metadata": {},
   "source": [
    "[ 데이터 분석 ]\n",
    "- 분석 내용 : 종합적으로 봤을 때의 분석\n",
    "- 분석 결과 : 유가가 상승했을 때 바로 교통량이 줄어들지는 않지만, 어느 정도 시간이 지났을 때 교통량에 작용이 있는 것을 알 수 있다. 또한, 교통량의 변동률이 크긴 하지만 전체적인 유가의 추세와 비슷한 모습을 보이는 것을 알 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1478324",
   "metadata": {},
   "source": [
    "# 5. 모델 학습\n",
    "\n",
    "[ 내용 ] \n",
    "- 머신러닝 알고리즘(회귀 및 분류)을 사용하여 모델의 학습을 진행\n",
    "- 수업시간에 배운 것 이외의 알고리즘도 사용 가능함\n",
    "- (# 필수 조건 : 위의 경우, 해당 알고리즘의 원리 및 간단한 설명을 작성해야 함)\n",
    "- 반드시 '훈련 데이터'를 이용하여 모델의 학습을 진행해야 함 (# 필수 조건)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8561157c",
   "metadata": {},
   "source": [
    "[ 모델 학습 ]\n",
    "- 사용한 머신러닝 알고리즘 (# 필수 조건) : K-최근접 이웃 회귀 알고리즘, 선형 회귀 알고리즘\n",
    "- 해당 알고리즘의 원리\n",
    "  - K 최근접 이웃 회귀 알고리즘 : 지정한 K값 만큼 이웃한 데이터를 참고하여 추세선을 지정하고 분석하는 회귀 알고리즘\n",
    "  - 다항(선형) 회귀 알고리즘 : 1개의 독립변수가 존재하는 데이터를, 직선 형태의 추세선을 이용하여 분석하는 회귀 알고리즘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "79496527",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "9ff84853",
   "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>날짜</th>\n",
       "      <th>교통량</th>\n",
       "      <th>휘발유</th>\n",
       "      <th>경유</th>\n",
       "      <th>강수량(mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20201206</td>\n",
       "      <td>2852845</td>\n",
       "      <td>1420.48</td>\n",
       "      <td>1227.59</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20201207</td>\n",
       "      <td>3432682</td>\n",
       "      <td>1421.92</td>\n",
       "      <td>1228.70</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20201208</td>\n",
       "      <td>3364617</td>\n",
       "      <td>1424.18</td>\n",
       "      <td>1230.83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20201209</td>\n",
       "      <td>3384849</td>\n",
       "      <td>1425.68</td>\n",
       "      <td>1231.72</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20201210</td>\n",
       "      <td>3421378</td>\n",
       "      <td>1427.67</td>\n",
       "      <td>1233.75</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>20220607</td>\n",
       "      <td>3712056</td>\n",
       "      <td>2105.60</td>\n",
       "      <td>2086.53</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>20220608</td>\n",
       "      <td>3651495</td>\n",
       "      <td>2112.39</td>\n",
       "      <td>2096.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>20220611</td>\n",
       "      <td>3852390</td>\n",
       "      <td>2129.47</td>\n",
       "      <td>2121.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>20220612</td>\n",
       "      <td>3324092</td>\n",
       "      <td>2132.46</td>\n",
       "      <td>2126.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>20220614</td>\n",
       "      <td>3550521</td>\n",
       "      <td>2149.69</td>\n",
       "      <td>2149.23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜      교통량      휘발유       경유  강수량(mm)\n",
       "0    20201206  2852845  1420.48  1227.59      0.0\n",
       "1    20201207  3432682  1421.92  1228.70      0.0\n",
       "2    20201208  3364617  1424.18  1230.83      0.0\n",
       "3    20201209  3384849  1425.68  1231.72      0.0\n",
       "4    20201210  3421378  1427.67  1233.75      0.0\n",
       "..        ...      ...      ...      ...      ...\n",
       "394  20220607  3712056  2105.60  2086.53      0.0\n",
       "395  20220608  3651495  2112.39  2096.07      0.0\n",
       "396  20220611  3852390  2129.47  2121.67      0.0\n",
       "397  20220612  3324092  2132.46  2126.05      0.0\n",
       "398  20220614  3550521  2149.69  2149.23      0.0\n",
       "\n",
       "[399 rows x 5 columns]"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "d6b24dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(399,)\n",
      "(399, 1)\n",
      "(399,)\n",
      "(399, 1)\n",
      "(399,)\n",
      "(399, 1)\n"
     ]
    }
   ],
   "source": [
    "# 휘발유 데이터 구조 변환\n",
    "gasoline = result['휘발유']\n",
    "print(gasoline.shape)\n",
    "\n",
    "gasoline = gasoline.to_numpy()\n",
    "gasoline = gasoline.reshape(-1, 1)\n",
    "print(gasoline.shape)\n",
    "\n",
    "\n",
    "# 교통량 데이터 구조 변환\n",
    "traffic = result['교통량']\n",
    "print(traffic.shape)\n",
    "\n",
    "traffic = traffic.to_numpy()\n",
    "traffic = traffic.reshape(-1, 1)\n",
    "print(traffic.shape)\n",
    "\n",
    "\n",
    "# 경유 데이터 구조 변환\n",
    "diesel = result['경유']\n",
    "print(diesel.shape)\n",
    "\n",
    "diesel = diesel.to_numpy()\n",
    "diesel = diesel.reshape(-1, 1)\n",
    "print(diesel.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "65ba2360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gasoline, traffic)\n",
    "plt.rc('font', family='Malgun Gothic')\n",
    "\n",
    "plt.xlabel('휘발유')\n",
    "plt.ylabel('교통량')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "325163ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diesel, traffic)\n",
    "plt.rc('font', family='Malgun Gothic')\n",
    "\n",
    "plt.xlabel('경유')\n",
    "plt.ylabel('교통량')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "b6da71df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "21e3cc01",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "a963347b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_l_train, input_l_test, target_l_train, target_l_test = train_test_split(gasoline, traffic, \n",
    "                                                                      shuffle=True, random_state=42)\n",
    "model_lr_g = LinearRegression()\n",
    "model_lr_g.fit(input_l_train, target_l_train)\n",
    "\n",
    "poly_d2 = PolynomialFeatures(degree = 2, include_bias = False)\n",
    "\n",
    "train_poly = poly_d2.fit_transform(input_l_train)\n",
    "test_poly = poly_d2.fit_transform(input_l_test)\n",
    "\n",
    "model_lr_g_poly = LinearRegression()\n",
    "model_lr_g_poly.fit(train_poly, target_l_train)\n",
    "\n",
    "x = np.arange(np.min(gasoline), np.max(gasoline), 0.1).reshape(-1, 1)   \n",
    "\n",
    "x = np.arange(1400, 2200).reshape(-1, 1)\n",
    "x_poly = poly_d2.fit_transform(x)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.scatter(input_l_train, target_l_train)\n",
    "plt.scatter(input_l_test, target_l_test)\n",
    "\n",
    "plt.plot(x, model_lr_g_poly.predict(x_poly), \"r\")\n",
    "\n",
    "plt.rc('font', family = 'Malgun Gothic')\n",
    "\n",
    "plt.title('휘발유의 가격에 따른 교통량')\n",
    "plt.xlabel('휘발유의 가격')\n",
    "plt.ylabel('교통량')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "f524e52b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_ld_train, input_ld_test, target_ld_train, target_ld_test = train_test_split(diesel, traffic, \n",
    "                                                                      shuffle=True, random_state=42)\n",
    "model_lr = LinearRegression()\n",
    "model_lr.fit(input_ld_train, target_ld_train)\n",
    "\n",
    "poly_d2 = PolynomialFeatures(degree = 2, include_bias = False)\n",
    "\n",
    "train_d_poly = poly_d2.fit_transform(input_ld_train)\n",
    "test_d_poly = poly_d2.fit_transform(input_ld_test)\n",
    "\n",
    "model_lr_poly = LinearRegression()\n",
    "model_lr_poly.fit(train_d_poly, target_ld_train)\n",
    "\n",
    "x = np.arange(np.min(gasoline), np.max(gasoline), 0.1).reshape(-1, 1)   \n",
    "\n",
    "x = np.arange(1200, 2200).reshape(-1, 1)\n",
    "x_poly = poly_d2.fit_transform(x)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.scatter(input_ld_train, target_ld_train)\n",
    "plt.scatter(input_ld_test, target_ld_test)\n",
    "\n",
    "plt.plot(x, model_lr_poly.predict(x_poly), \"r\")\n",
    "\n",
    "plt.rc('font', family = 'Malgun Gothic')\n",
    "\n",
    "plt.title('경유의 가격에 따른 교통량')\n",
    "plt.xlabel('경유의 가격')\n",
    "plt.ylabel('교통량')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "3efa8ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 1) (299, 1) (100, 1) (100, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_g_in, test_g_in, train_g_tg, test_g_tg = train_test_split(gasoline, traffic, random_state = 20)\n",
    "print(train_g_in.shape, train_g_tg.shape, test_g_in.shape, test_g_tg.shape)\n",
    "\n",
    "model_g = KNeighborsRegressor()\n",
    "model_g.n_neighbors = 10\n",
    "model_g.fit(train_g_in, train_g_tg)\n",
    "\n",
    "x = np.arange(1400, 2200).reshape(-1, 1)\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.scatter(train_g_in, train_g_tg, alpha=0.5, color='green')\n",
    "plt.scatter(test_g_in, test_g_tg, alpha=0.6, color='blue')\n",
    "plt.plot(x, model_g.predict(x), \"r\")    \n",
    "plt.rc('font', family = 'Malgun Gothic')\n",
    "\n",
    "plt.title('휘발유의 가격에 따른 교통량')\n",
    "plt.xlabel('휘발유의 가격')\n",
    "plt.ylabel('교통량')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "6389e58b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 1) (299, 1) (100, 1) (100, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3oAAAH2CAYAAAAmvv7yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACoOklEQVR4nOzdeXzcVbn48c+ZPZlM9nRLmm7QUkrb0JYdZFdERLQoWDdU8KI/F4QregWUK3K9i4JeXBERAatXrYKiokV2la0ltGVroUuadM06k0lm//7+ODPJJJ1JZjL75Hm/Xnl9k+9sJ9t3znOec56jDMNACCGEEEIIIUT5MBW6AUIIIYQQQgghsksCPSGEEEIIIYQoMxLoCSGEEEIIIUSZkUBPCCGEEEIIIcqMBHpCCCGEEEIIUWYk0BNCCDFCKfUzpdS8PL/mt5VStiw8zwVKqYun+NjzlVJfmeJjP6yUOnkqjx33PDal1LczfZ5SpJT6V6XUsYVuhxBClBMJ9IQQYppQSr2ulBqM+/AqpQyllDPubmcCriSPP0Up9Wul1C6l1H6lVIdS6m9KqbUZNu1zQMaBHnAy8JbxJ5VSHxj3fQ8qpYJKqdvj7tYMrEr1hZRS85VS/dEvLwSOy6ThUTb0zyLR671bKdU9wYehlLJkoQ0TUkrdpZQaVkr1J/n4R4LHfDBBez1KqV/G3e0SoDXX7RdCiOlEAj0hhJgmDMNYYhhGVewDeAewxzAM72SPVUq9E/g98CvgGMMwZgPzgVuB/1RK3ZTkcXVKqf+NBocHlVJPK6XOTbftSqnWJIFFRCm1ZKLHGobx8/jvO/q9PwK8msLrnh8NkIeUUn9USjWl2/bo85wVDS5jgc5pSqndSqm+6Nf3TvI9/M4wjMZEH+jfQzpt+VSCwDf+w6eUemiCp/g3wzBqk3ycmqDt9ydo8+cTPO+K6M+pMZ3vRwghRGIS6AkhxPT1AeAPSqlvxTr5JM+qfAq4xTCMXxuG4QcwDCNiGMajwL8Anxn/AKWUGR1Q2YE2YBbwb8C90cAxZYZhdIwPKoA5QBDYm85zKaXmAKcDf48FXsAdCe43A/gFOsvWCOwCvp3Oa42zKS7Y+Xv03CnRrz+cwfOmxTCM748PfMcFwVdP8hRfnyCz+M0MmvYp4LvAigyeQwghRFTOp3kIIYQoPkqp5cA6YLlhGDuB66Lndyd5SAioSHJbZfT28U4DmoBPGoYRiZ57Sin1VXTw9IeptX7EmcBLhmEMpfm424AfG4bxMjqAQyl1BXr6YLz3AI8ahvFw9D5fBvYppaozafRUKKUuB+4F3Enu0gMYWXzJiZ7rRsMwvp3qEymlPoIOkP1xpy3A+Kzh1bGftRBCiMxJoCeEENOMUmom8Fvg36NBXipuB36rlBoCHgAOAnXAucC3gG8keEwl4I4L8mL6AeeRd0/bJ4GfpfMApdQNwFLgYync/Tjg+dgXhmG4lVI7gaPRgVW+/dUwjIvy8DoWEgfuMV9XSt2Y7Mbo1Mx4VuAvhmFcPsnrtiiljgE6DcMYTK2pQgghkpGpm0IIMY0opY5HBy+PGYbx39Fzv4mteSPJ1M3oFM1TgWOA/0Ovb/sj8HbgMsMw/jfBw54C6pVSI9MSlVJ1wBeA32T4fVwILAd+Mu6mtUqph5VSvx93f5tS6ifoLObbDcMYUkqtnmjqJrooTf+4c33ARuICwGyLrt3brZQ6J+50ADh9giIo/SrNiqNKqZuUUq3Rz09TSn08epMN8CV6jGEYV0aneMbW2lmANePW36X6+pXRabQx1wB3Acen830IIYRITDJ6QggxDSilZgH/il5/dTswspWAYRiXxt1vd7LnMAzjFeD/pfqahmF4lVJvBX6ilPpX4BBwLPDjaBumRCm1DLgbeJ9hGOMDkiejzx2J3teGzt59EXgaONUwjIFo+zYx8dRNLzB+mmYNOrg9CLRP9XuYxDHRYyB2wjCM36KzsNl0GfBnoAOd5TwPHThXMm6KqFLqOhIHYJXA7Uqp8QV9DhuGESu4EgIuVEodQE8JVdHzHuAF4P3Rr/9Vpm4KIUT2lHygp5Q6Dfgf4FOGYbSncP8bgUvRbzD/MAzji7ltoRBCFIV16AIkb4srBJLIYXSBEwCiJfAvSON1OgzDGCmmYRjGNuAkpdRsoB54M0FwljKl1Onoyp/XGobxZIK7HB73XmACFgEfMAzjiNL/cR7lyCqcr6C3bIi9dmX0ubajA76ciP/5KKU+DXw9zadYYRhGRwZNaED/HcR7BHgpwX3vSfIcw7FPDMO4Z4L7xfwJ6EqpdUIIIVJS0oFetBy1iyNHXJPd/zPotRWrDcMIK6XUZI8RQohyYBjGbegiJCilzkBn5k4EHOgsSz96+4SLDcPYH/e4kXVVSqlLgGsMwzgr7tx3gQOGYUwYjESfc/9E95mIUqoB+BLwQeAKwzD+ksrjokHTF5RSNdGqoslYgIcZm9X7LXCzUupEdObpBvQ6uT6l1FQCvWalN2WvRmceAT6hlBpAB1ffSdD+76IrUebT+OmwGIYxEuQpvW/i5egsYBV6mueb6OI6dxmGkXB9X3TbhI8Db0NXYLWgs7x/RxfHeSO734YQQkxvJR3ooSt0DSmlHo8/qZT6AnoDWwuwldGy358GTjIMIwxgGEY2K5QJIUTRU0q9A/gp8Fngo4ZhDEfPz42ee14ptSw2vTGD16kFjkJn1CxxHzZ0cFmBHqi7J8WnPAmYDRxvGMaBdNsT/X6qJmjvFYybumkYxj6l1FXAg9G2vogOcKZiB/Af6MqT3cBm4N/RRWmG0dmypIFodP3dGYZhfGGKr5+yiQKuaOXRdehpwM8DsZ/rscDN6A3r1yV4XB36e/4lepChAwijf6fvBp5RSp0VzQALIYTIgpIO9BKV1I6WoG40DOPs6NffQ7/pPIVeRH99dLrnMPAVwzCey2OThRCi0C4CfmEYxi/jTxqGsRed+foIsBo9lTETzegtGyLoDn0QvVYrgA52htCBTUrvQ4Zh/Ak9vS+vDMP4HfA7pZTVMIzgpA9I/jxdwA/Gnf5p/BdKqaSBKHra64Kpvn70+RuAPXGnzMDj0cktSt/liKznfxiG8R9xX78b+Ma4tXT9wD+ig6zPJHn5U4GIYRjXjzu/C7gt+r78DkACPSGEyJKSDvSSeA+wKC7LVwV0oqeJHAN81jCMLyulVgK/V0otjm3+K4QQ08CDwH1Kqb8Df4jL6DWjqx760dMUMxLdo+79k95Rv3amL5dzmQR5xcIwjB4myGqm6HHgU0qpl+Kzb0qpeeiCN48nedxmoCa65vBewzDc0cfZ0TNwzkRv0yGEECJLyjHQswBfMgxjY/xJpdR8YHcsg2cYxktKqUNAC3ptgRBClD3DMB5WSr0HPX3uv6Id7dgavYeAE2OdcFF0LplkneFHDMPYkOM2fAm9DOKu6PtqBD099xB6jd4nEj3IMIz9SqlT0IMJLyilHOj99cLoKbHvnaRYjhBCiDSpclimFs3eXWMYRrtS6pPohd7vNQwjqJRaCPQbhtGrlHoW+EQ0yFuELiu9rBxGaoUQQkxdtCpok2EYWwrdFiGEECIbyjGjdye6AMDz0c1/PcBHord9BPiuUsqCHsH+gAR5QgghMq0KKoQQQhSbssjoCSGEEEIIIYQYZSp0A4QQQgghhBBCZJcEekIIIYQQQghRZkp2jV5jY6Mxf/78QjdDCCGEEEIIIQpi06ZN3YZhNCW6rWQDvfnz5/PCCxlv9SSEEEIIIYQQJUkptSfZbTJ1UwghhBBCCCHKjAR6QgghhBBCCFFmJNATQgghhBBCiDIjgZ4QQgghhBBClBkJ9IQQQgghhBCizEigJ4QQQgghhBBlRgI9IYQQQgghhCgzEugJIYQQQgghRJmRQE8IIYQQQgghyowEekIIIYQQQghRZiTQE0IIIYQQQogyI4GeEEIIIYQQQpQZCfSEEEIIIYQQosxIoCeEEEIIIYQQZUYCPSGEEEIIIYQoM5ZCN0AIIYQQQpSH9o4+NmzuYm/vEHPrK1m7qpm21rpCN0uIaUkyekIIIYQQImPtHX3ctnE7vd4AM2sc9HoD3LZxO+0dfYVumhDTkgR6QgghhBAiYxs2d+FyWKmusGJSiuoKKy6HlQ2buwrdNCGmJQn0hBBCCCFExvb2DlHlGLsqqMphYW/vUIFaJMT0JoGeEEIIIYTI2Nz6SgZ9oTHnBn0h5tZXFqhFQkxvEugJIYQQQoiMrV3VjMcXxD0cJGIYuIeDeHxB1q5qLnTThJiWJNATQgghhBAZa2ut49rzF1PvtHFwwEe908a15y+WqptCFIhsryCEEEIIIbKirbVOArssku0qRCYkoyeEEEIIIUSRke0qRKYk0BNCCCGEEKLIyHYVIlMS6AkhhBBCCFFkZLsKkSkJ9IQQQgghhCgysl2FyJQEekIIIYQQQhQZ2a5CZEoCPSGEEEIIIYqMbFchMiXbKwghhBBCCFGEZLsKkQnJ6AkhhBBCCCFEmZFATwghhBBCCCHKjAR6QgghhBBCCFFmJNATQgghhBBCiDIjgZ4QQgghhBBClBkJ9IQQQgghhBCizEigJ4QQQgghhBBlRvbRE0IIIYQQooi0d/SxYXMXe3uHmFtfydpVzbKfnkibZPSEEEIIIYQoEu0dfdy2cTu93gAzaxz0egPctnE77R19hW6aKDES6AkhhBBCCFEkNmzuwuWwUl1hxaQU1RVWXA4rGzZ3FbpposRIoCeEEEIIIUSR2Ns7RJVj7OqqKoeFvb1DBWqRKFUS6AkhhBBCCFEk5tZXMugLjTk36Asxt76yQC0SpUoCPSGEEEIIIYrE2lXNeHxB3MNBIoaBeziIxxdk7armQjdNlBipuimEEEIIITIiVSKzp621jmvPXzzm53nVGQvk5ynSJoGeEEIIIYSYsliVSJfDOqZK5LXnL5bgZIraWuvkZycyJlM3hRBCCCHElEmVSCGKkwR6QgghhBBiyqRKpBDFSQI9IYQQQggxZVIlUojiJIGeEEIIIYSYMqkSKURxkmIsQgghhBBiyqRK5Didm6B9PfTvgdp50LYOWlYXulViGpJATwghxLQg5d+FyB2pEhnVuQkeuxUc1VA9G4Z79Ndn3yDBnsg7mbophBCi7MXKv/d6A2PKv7d39BW6aUKIctK+Xgd5jhpQJn10VOvzQuSZBHpCCCHKnpR/F0LkRf8esLvGnrO79Hkh8kymbgohhCh7e3uHWG3ZyZquh6n376fXPpsXai5gU+/CQjdNCFFOaufp6ZqOmtFzfo8+L0SeSUZPCCFE2TvZvptzDtxFZWiAfmsTlaEBzjlwFyfbdxe6aUKIctK2Dnxu8A2AEdFHn1ufFyLPJKMnhCguUq1M5MBa85O8aVQQMiqxKoXHqMRihFhrfhJ4X6GbJ4QoFy2rdeGV+PexUz4z6fuYFIsSuSCBnhCieEi1MpEjTaEDqLmz2dXjY9AfospuYcGs2TSGDhS6aUKIctOyOq33rFixKJfDOqZY1LXnL5ZgT2REAj0hRPGIr1YGo8f29RLoiczUzqNxuIfG+XGdJt8AVMi6GSFEYcUXiwJGjhs2d0mgJzIigZ4Qonj079GZvHhSrWxKZBrQOG3rdHYY9N+U36PXzZzymcK2Swgx7e3tHWJmjWPMuSqHhb29QwVqkSgXUoxFCFE8aufpDng8qVaWNtkzLoHYupmKBnDv10eZEiyEKAJz6ysZ9IXGnBv0hZhbX1mgFolyIRk9IUTxkKxLVsg0oCTSXDcjhChPxTbjYe2qZm7buB3QmbxBXwiPL8hVZywoWJtEeZCMnhCieEjWJSv29g5R5Rg7jlfS04A6N8FD18H9l+pj56ZCt0gIUaKKccZDW2sd156/mHqnjYMDPuqdNinEIrJCMnpCiOIiWZeMza2vpNcbGMnkQQlPA5JKrEKILCrWGQ9trXUS2Imsk4yeEEKUmbWrmvH4griHg0QMA/dwEI8vyNpVzYVuWvriK7Eqkz46qvV5IYRIU9nNeBBiApLRE6IMFdv6g1yYDt/jVMWmAcX/fK46Y0Fp/nykEqsQIovKasaDEJOQQE+IMjMdNl6dDt9jpspmGlDtPD1dM7anIkglViFKTDENzEnhEzGdSKAnRJkp1vUH2TQdvsdSNb5Dt7y5mq1d7ql38KQSqxAlKXYt2NbVzwG3n4WNTlqi2bRCDsyV1YwHISYhgZ4QZWY6bLw6Hb7HUjQ+07rz8CAPbdnH0lnVzG2YYgcvVom1fb2erlk7Twd5UohFiKIVfy1wR/eHe/OwF6fdQkOVHSjswFzZzHgQYhIS6AlRZpKtPzjZvhse+r/RznLbupLtLOdjjUUxTTUqFeMzrYc8fhxWM4cG/cxrdE498yqVWIUoKfHXgqFAmAqrmVDEYFe3l4YquwzMCZEnUnVTiDKTqOJi48BW1g3dp9c6xZeoL9H9yHJZVbK9o4+r73uBj97zPE9uP4zFrIpin6VSML6a3aA/hMNqwusPjZyTDp4Q5S/+WuC0WwiGDSxmxWD0WiDFT4TIDwn0hCgziTZe/XT981TXNpRNifpcbS4bm2706n4PVXYLEQNe7Ojnpb19vLrfzfUbtkiwN4G59ZUM+uKCOrsFXzCC0x4X/EkHT4iyF38tWNBQSTAcYTgQxmkzl/Z2L0KUGJm6KUQZOmL9wf0HoLK8StTnYo1FbLpRIByhwmYmEIww6A/hD5loctnoK+Xqnp2bxq5zy8HU3fHV7Ga47HQP+lnQ4CRiGFmpbidTaoUofvHXgvoqOwsbnezq8VJdYaXeaZPiJ0LkiQR6QkwHUqI+JbEiL1V2C/5QBLcviMWkCEcMQmGoqbThclhLr7pn5yY9VddRPXbq7tk3pB3sTRRoja9mt7Cpine1zRlTdTOTDp5sqyFEaRh/LVg0o4rrL1gi/6dC5JkEekJMB1KiPiWxIi8LGp1s6RwgEIpgNilMJkUwHGF2jZ3XD7jp9QYASieb1L5eB3mxQD92bF+fVqCXSqCVKNP6vhOy8l3IthpClBCpbClE4ckaPSGmg1iJ+ooGcO/Xxylkc8pdrMiL1WxieXM1FrMiEI5QW2Ghtb6Cjt5hvIEwdU5baRVo6d+jA/x4U5i6Gx9omZSiusI6kuHMh/HFXkCKuwghhBDJSEZPiOliiiXqp9OaqPHTjU4/qpEDAz6a6yp5/YAbpcAwYGEmWwVkSVobk2dp6m6h9y/Mx7YaQgghRLmQQE+IMpWNAG06rokaP90o9nPs9Qaoc9pY2Oik0aWDnUJlk8b/Xt48pDcmP3Z2NS31CTYmz9LU3UIHWuOLvWSjuIsQojRMp0FHIbIlb1M3lVJnKqUMpVTtuPNzlVJ/UUo9o5R6WinVlq82CVGuYoFArzcwJkBLd5phoafqFYO21jpuueQ43rFiDsfMqh4J8qBw2aTxv5dDg9GNyT3+xL+nLE3dTXX/wvaOPm56YBtX3P0cNz2wLWvTW3O1rYYQorhl6z1NiOkmLxk9pVQF8BWgJ8HN3wfuMAzjIaXUamA9cGw+2iVEucpW0YpCT9UrBrFR5Jf3DbB/wMeCBidzGyoLmk0a/3vxRjcmH5xoY/IpTt2NN35qa6IqmrnOAkuBByFK11SzclKISYipydfUzTuAbwI/iD+plKoDlhqG8RCAYRiblFJepdQxhmG8lqe2CVF2shWgFXqqXqHFBy1Hz3RRYTWzs9vLcDDEcc21BdsLavzvxWm34PWHqMrDxuSTBVrSIRNCJJLJIJAMOgoxNTmfuqmUugHYZxjGnxPc3ArsHnduD9B85F2FEKmaW68zTvGm0vFPdapeOWrv6OP6DVt4db+b1w966B3009rgZFVrHcc113LLJccVLHAZ/3uZUWXHFwwzw2Uv+O9JKmMKIRLJZClAtt7T8q5zEzx0Hdx/qT52bip0i8Q0k9NATyn1EeA44KtJ7mIHQuPOhYFIkuf7hFLqBaXUC4cPH85eQ4UoM9kK0KbrmqjYyHOfN0CVw4I/GGFrl5tuj68ogpbxv5dFM6r4t7cfw8KmqoL/nkq2QyaEyKlMBoFKctCxc5MugjXcA9Wz9fGxWyXYE3mV66mbX0IHbi8qpQDmAE8ppS4zDOMVoBOd1Ys3jyOzfAAYhnEncCfAmjVrjBy1WYiSl8paqnSeq9wDu/FiI8+1lTb8oQg2ix4T29UzhM1iLoqgJZcbk2dCKmMKIRLJZClANt/T8qZ9PTiqR7e1iR3b18setiJvchroGYaxNP5rpdRu4AzDMPqjt+9TSvUrpc4yDONxpdQqIGAYxq5ctkuI6WA6BmjZElsPsqDRyZbOAQAsZhgYCkjQMomS7JAJIXIu00GgXLyn5XTLhv49OpMXz+7S54XIk4Lso6eU+hhw0DCMPwIfBn6slPoPwAdcWYg2CVEO2jv6+OETb44EJytaarj6zEXSyU5TbOS5ocrOipYadnV76R/S++hNh6mrmZJBBiHEeMU2CJTzfWJr5+npmrFMHug9TGvnZf7cQqRIGUZpzoBcs2aN8cILLxS6GUIUjfaOPm7+w8t09g5TYTNjAL5gmJbaCm6+eJl0vCcwflR3eXM1D23Zj8thHTPyLEGeEEKUh5se2HbEVFL3cJB6p41bLjku8xeIrdFzVOtMnt8DPveU9jAVYiJKqU2GYaxJdFveNkwXQuTWhs1d9HmDVNot2Cxm7BYzFVYLfUPBabXBeboSbcS7/tkOguEIL3b08dT2w4QjES5aMZsNm7uyvgm4EEKI/Mt5heCW1Tqoq2gA9359lCBP5FlBpm4KIbJvb+8Q/lCYyri91KxmxVAgXPAqkcVs/L5vgVCYzv5hnDYLZyxuYtAXoqt/iPXPdtBcV5mbKT5CCFGmcroOLgN52Se2ZbUEdqKgJKMnRJmYW1+J3WImFB6djh0MG9gtpqKoEgn6Df+mB7YVVVZs/Kjurp4hHFYzgXBkZK+nPm+QvqHglPZ/EkKI6SrRjInbNm4vimt/MW3ZUIzvjaI8SKAnRJlYu6qZOqeVIX+IQCiMPxRmOBiirtJaFHsNFesb/vh937z+EAqoisuM+kNh/KGx23sWw356QghRzDLZJD3XimWf2GJ9bxTlQaZuClEm2lrrWHdiK9/663a6+ocBaHTaWHdSa1FMkxk/RTJ23LC5q6DtG1/y22Y24fWHWDq7euQ+dov5iMd19g5xeNDPFXc/V1TTkYQQoljEtqqJV0yDZMVQITjR8oE9PUN86uebOXfpTHlvERmRjJ4QZaK9o4/1z3YQNgzm1FbQXFtBxID1z3UUxchgzhe+T9H4Ud2ls1201FdgNZtGpvPUOa3UVVpHpvh09Hh5Zb+bRqddRmCFECLq9Rce49k7ruCl/zyPZ++4giWh18fMmIAcrIMrcfHvjd0eH1u73IQNg4hhyHuLyJhk9IQoExs2d9E3FKTCasFm0WM4Sin6vMExWbNCLYzPy8L3KRo/qjv+ZxTb0Dd27vCgn6WzqpnX6ASKJzsphBCF8voLj+HfeAsWiwtfxSwsvj7e5r6b9d4P4p5x/JQ2SZ8O4t8bd/UMYTWbUIoxWT55bxFTJYGeEGVCV92MUGkbnWZoMSuG/KGRrFnON4idwPgpksX8hj/RdJ4Nm7vY0tlPJGJQ5bDQUGUHiiM7KYQQhdL/z59hsbgI2fW095C9GgtwkfE4f3OeVBSbpBej+PfGQV8Iq1kRCBkcM8sFyHuLyIwEekKUibn1lezq9hIMG9gsCoBQ2MBuMY9kzQq5Ti42RXJ8pqxU3vDjg+Q6pw1vIMyWzgFWtNTQUGXPS3ayWMuUCyGEw9uJr2LWmHMhm4u64f3Z2YC8TMW/N5oUmJRiRUv1yCBiscx8EaVJAj0hysTaVc1s7eyns38YAzMKGA6EaamvGKm6WeiF8cWw8H2q4oPkhY1Otna5UQp2Hh7EajblPDtZyGysEEJMxudsweLrG8noAVgCHnzOlqw8fzkPdMXeG2PZvdga8WKe+SJKgxRjEaJMtLXWcfPFy1gzr45IxCAcMVgzv46b37ls5M1w/FYCIKOFqYpfMN/ocrC8uZpKm5lebyAvZbmLuUy5EELUnvIRrCEPFr8bDAOL34015KH2lI9k/NzTZQuCYtnyQZQPyegJUUbaWuv44YfWJL29lNbJZUu2RoHHF5NpdDmwWczUO215mZZU6GysEEJMZMmas3kdGP7nz/Q0TmcLVadcw5I1Z2f83MW6PU8ulPLMF1F8JNATIgOlNpVkyuvkOjdB+3ro3wO186BtHbSszk+jM5DN6Y6FDpKLuWqpEEKADvbIQmA3ngx0CTE1EugJMUWlumYq7dHCzk3w2K3gqIbq2TDco78++4aiD/YyGQVOFMQXsphMoQNNIYQoFBnoEmJqJNATYoqmzVSS9vU6yHPU6K9jx/b1RR/oTXUU+FfPd3D7IzsIhSNUV1gJhCIjQXyhqseVetVSIYSYKhnoEmJqJNATYoqmzVSS/j06kxfP7tLni9xURoHbO/q4/ZEdgN6wNhAy2NntZWGjs+BBvKzdEELkRDAIhw9n9zkrKqAuO9crGegSYmok0BNiiqbNVJLaeXq6ZiyTB+D36PNFbiqjwBs2dxEKR3BVWFGokT0JD3p82CxSqFgIUYbe8Q7YuDG7z2kywY9+BFdemZWnk4GuNDz1FHR06M9NJrjggqwF3aK0SKAnxBRNm6kkbev0mjzQmTy/B3xuOOUzhW1XCqYyCry3d4iaCiuBsIHVrIM8q1nhHg4yd1GZBfFCCBGJwD/+AW97G7znPdl73l/8Av7f/4MPfhAcjuT3G1fs6/VZF3F/Z1PJFDkrOoODcPbZEA6PnrvhBvj617P6MqVWjG66kkBPiCmaNlNJWlbrwivxVTdP+UzRr8+LSXcUeG59JcFwhDcPewGwmBXDwTAWs2lk43khhCgbe/aA1wtr18JVV2XveU0mePxxOHQIWlsT32dcsa++nv34t95CVdPHmVm3vGSKnBWVjg4d5N1+u87Unnce7NqV1Zco1WJ005EEekJkYNpMJWlZXTKBXaZimdpFTU4Oun0MDAexmE18/ryjp8fvWggxvWzbpo/HZbnQ1MyZ+njwYPJAb1yxrzfcFkwWF28Z3sjv61eUbpEzrxduvVVn10CvV7zhBqiuzv1r792rj2vWwNFHw7x50NmZ1ZeYNsXoyoAEekJkSKYvlJe21jouWjGbOx7dwSFPALNJsbKlisUzXYVumhBCjNXVpafkxQKKqXj9dX1ctiw7bYqJD/SSGVfsa9AfotLmot6/f+RcSRY5+8c/4BvfAJdLZzYHBuDYY+EjH8n9a8cCvblz9bGlBZ57LrsvMV2K0ZUBCfSEyIBMXyg/7R19rH+2g+FghCaXHQXsODjIzX94mZvfuUx+r0KI7Pvzn+GRR9J/3KOPwiuv6M58Jt773uxnm2bM0MeJAr1xxb6q7BZMvgF6HXHBXykWOfN49PGpp3SmtKYGNm/OT6DX0aGDyzlz9NctLfDb34JhgFJZeYlpU4yuDEigJ0QGinb6wrjF7bStmzZTLzO1YXMXfUNBKqyWkSqbSin6vMHC/16FEOXphhtgyxY9xS8ddjvcey9cdllu2pWJWEbvmWfg/e+HygRBwLhiX0dVh9g76OGRivcRMYzSLXIWy7BWVYHZDG1tOtDLh717YfZssEaDsJYW8PuhpwcaG7PyEtOmGF0ZkEBPiAwU5fSFcYvbGe7RX599gwR7KdjbO4Q/FKHSZsYXDOHxhQiGDcDg5X0DhW6eEKIcDQzoYOi++wrdkuypqNDBxl13wdNPw8sv60xTvHHFvuoa5nFo2VUMdjZxsJSLnMUHegCrVsGdd+ptDnJt82ZYtGj061i290c/guZJCorV1MAll0ya+Zs2xejKgAR6QmSgKKcvjFvcPnJsXy+BXgrm1leyq9vLoD+IxxfGpBQmBYah2D/go72jb8ybmazRFEJkzO3OT6GOfAsG9fG11/Q+fW9725H3GVfsawlwy5r8NC9nxgd669bpAKy/P/evvXAhXHHF6NfHHqsDtxtvTO3x//wnnHzypHebNsXoSpwEekJkoJimL8QCjktfe4lA5WwWNPlprLLrG+0uPY1TTGrtqma2dvZz8IBvZFAzFDGosltY0OAcM31T1mgKIbLC7daFO8rZ9u2JA71yNDiog6vYdNWTT9ZZzUI45hi9TtLrnfh+PT26Uuczz6QU6InSIIGeEBkolukL8QGHt7IZu7+fLZ0hVrTU6GDP79Fr9cSk2lrruPniZXz8Z88zFAhjAE0uO0tnuaivso+Zllu0azSRTKMQJcPvh0CgPDN6d98NfX06m7RnGg02Dg7qbF6Wip9krKlJf0xk/vycVOgUhSWBnhAZKobpC/EBx4t1F3LmvjsxAkGe3h5gUU2Eec4Q1ad8pqBtLCVtrXVcuHzOEdNy3cPBMdNyi3KNJuWTaZRgVUwLsQqN5RjoffSj+njnndMz0Cs1J54Iv/89nHJKoVtSnJYt02tOS4gEekKUgfiA4yVjES8E3ss7eYxZHKTXaOU3wXN5d2QhbYVtZklJZVpuUa7RpLgzjalq7+jj5j+8TJ83iD8UZle3l61d/bLFhSg/brc+lvPUzdZWXfY/HaVcPbpUA71PfUpP8TSMQrekODmdhW5B2iTQE6IMxAccu7q9+O1LuN1Ygt1q4oR59biHZWuAdKUyLbeY1mjGK9ZMYzp++MSbdPYOU2m3UGm3EAobdPYO88Mn3uSHHyr1Sg1CxCnnjF7MvHnwhz+kfv9Srx5dqoHeuefqD1E2TJPfRQhR7NauasbjC+IeDuLxBYkYBsFwhAUNOrNUap38UhELBuudNg4O+Kh32hJPj+zcBA9dB/dfqo+dm3Larrn1lQz6QmPOFUOmMR1bOgeosJmxmk0oFFaziQqbmS2dssWFKDOxjF45B3qtrbogyMqV8H//N/n946tHK5M+Oqr1+WKT6PqeKNDL8/uAECCBnhBlIT7gMCmFWSmWN1fT6NJZnVLr5BeD2NTBJ7cf5rUDbp7cfpib//Ay7R19Y+7X1lrHLZccxz0fO5FbLjkucZD32K16RDp+ZDqHb/LxgX/EMEYGANaummQPpVxLs6MzfvKQTCYSZWk6TN1873vhfe+DnTv1GjDQ0wPvuAMOHTry/v17dLXoeMVYPTrZ9b334NhArwDvA0KATN0U01Q5FnmIFYWJTSe0WcxEDKNophOWmqxNHSzAvoZTqQab8/+JNKdirWip4YU9fdFsniIYNvAFw6yZl36byvH/XZSR6ZDRO+YYnck75ZTRwO611+Czn4WHHoK//GXs/Wvn6WtE7HoJU68eneZav7SuF8mu770HYPGyye8XfR8Y/5ofbDnMkgMPleb6RFFUJKMnpp1YRcJeb2BMRcLxmZpSlfJ0QjGh53f3MhwKc9jjp9vjJxSOTG3qYJoj0+0dfdz0wDauuPs5bnpg25T/LifNNI57zZz/T6Q5FevqMxfRUluBScFQIIxJQUttBVefuSi9ly3z/3dRBqbDGr2YpiY4fFh/fvCgPm7X65zZskWv5fvnP3Vg43ODbwCMiD763Pp8OtLMpKV9vUh2fR8aHpvRm+B9YPxrVnW/hH/jLfT17Jfsn8iYZPTEtFOIioT5zii0NVfTNrsKrNbJ7yyO0N7Rx6AvhMmksJgU4YhB31CQKrteM5aWNEamC7UtQl7+J/r36E5LvAkC3th+hpn+35RDBVJR5sps6uaE73dNTbApGrDs3auPsY28H3xQV+Z85zuhu1tn++Mzcad8Jv2sVpozKtK+XiS7vvsjYwO9Cd4Hxr/mW4Y3ErS4eMNt4YR6U15mgYjyJYGemHbyXZEw35339o4+hj/wIVx73uQTn/wuK1pquPrMRdKpTcOGzV3UOm0MDAWJGAqTCSJhA48vxFlLJtl0dry2dXo0FnRg4/fokekE+xoWKijJyv/EP/+pp199+MOwcOGRt6cY8GZ7UKQcKpCKPNm8GTo78/+6mzbpjbVLsHR7vPaOPn74xJs8u6uXaoeVo2dWHfl+F8voGcaRgV5so+6eHr2BfMvqzAObNAeY0rpeXH45PPxnsIfh6++AmTP0NW3vfhj0jw30Jngf2PvXsa9Z799Pv62JIX9cQa1iXJ8oSoIEeqWqlPeXKbB8732Wz877r57vYMPPHuYXT/+JQXsloXCEF3b3cbP75aLaf6zY10zt7R1iRXMNmzv6GQqE8AUiGBEwmxTnHDMjvSdrWZ3yyHShgpKs/E/cdBP87W8wPAz/9V9H3p5CwJuLQZFi3etQFJmhITj5ZAgGC/P6zc1gKt3VNLH/3T09Q1TZLUQM2NblZnlzNS6HdfT9rqlJ/4wHBkaD6qEhuOIKeOKJ0SccGND3TeF1J3wvSXOtX1rXi40bYU4zvPoqPH8I3hLWz3vv8/r2OXNG7zvB+8Dc+m1jXtNvsrPAswmbCkFHHdQfBRbb1NYnimlPAr1SVOr7yxRYvvc+y1fnvb2jj9sf2cHNf7sXEwbVfi/DQ34qnA76vMWzj16hpiemI/Zmf9SMKrZ2DWA1mTBbFC6HhYe27GfxTFd6bU1xZDrrQUmKA0KZ/E/EOlof3bmfhUD3vm4aE91xXEfnsGUWG0wX8sxfw8yt38baVc05GRQp1r0ORZHZtUsHIP/5n3D++fl//ZaW/L9mFsX+dwPRtcwKBcCuniFWz6sbfb+bER0oO3xYZ/RMJj0D4G9/g4YGuOQSuO8+bv/1c7zkaJpwIDCl95I0ZlRA8uvFZ5cM6ErBsWvpisuhv19vMP6nP8GGLfDqsfDf18Hr34e3v13fFm/c+0B7Rx8bHtjGy/sG2D/gY0GDk1Mcu6nyH8IaHqLC6YKgDzqfh5rWpG0WYiIS6JWiAlTxKydTqUiYiXxlFDZs7mJh5w7e9trf2V/dxGz3Yep8HvptVkyKopmqNlFnPnYsZKavvaOPykMvcty+3zPbOMRBy0z+ajmXrRzF8uZqbBZzzoLmVIKSlLOhaQwITfV/Ir6j5Qz6AHjtzQNUdfQlfmy0ozO2g2YZ6aAN+kMcPXPsOqVMB0Xy/f8uStTOnfp49tmwalVh21KCYgOaVXYL/lAEq1lXy/X6Q2Pf72JZum9/W1fbvOiiMRup7/zpL1l4330Eu3uYuWzuhAOBKQ0MpTGjAhJfLz67ZIAlr94x9lr6p3+HSATq6+GWW+B//1dnJL/yFfD5YO3aCTO08dfAo2e6qLCa2dnt5UPqQbwVs6mfORf7UAcEPGB1Qs0c6d+JKZFArxSlOedcHCm2FUE+5CujsLd3iE8++XMGHFXcefrlfPVPd1Dnc3Oospa6SlvRTFVLluHc1tXPnh5vQTN97R19/O6h3/O+wfvwOivZ7anHGRzg46znlbrzWOp+kzr/PrqYAZ2fz/ob72RBSVrZ0LgBoe5BP7u6I4SHQ6jf3YH9Xbcfcf+p/E/Ed7RsvmEAnCH/pIFwsg7aAbePQV9owkGRqUz7zef/uyhRb76pj4nWl4pJxQY0FzQ6RyoTRwwDm9lE48BWPm1+Hu4/AN3Ra//3vw+zZsFVV415nr8dCLIQaAwN06fUhFn9iWbLHHmduDHla8AR14uHrjtycD0UrZRaXw8XXqg/3vEOnd0DWLaMiYy/BrY2OKmttLHwQC9LFi7WlYmJTv00IuDen1LbhRivdCeET2e18/T0g3hT3V9GpCbNjZ7j5Wu7g4WVcMor/+SB1W9nd+0sAK74+2+oHvZQ57TmfLPsVLcFmFtfyaAvNObcoC+ENxAeeeMzRd/gY2s78mXD5i7O8f+NkNWFYa+h0m4laHVhIcK73fdSGRqgWzUyw+LNWbnribZFiO8cTPozipbz7h70s6VzAH8wjLJXUzXclbXtBfb2DlHl0OOFNr8O9CqDvkkzcPGPi6lyWHDazBNu9J6s9Pmvnu/IypYUYhrbuVNvb9DQUOiWlKS1q5rx+IJYzSaWN1ejAK8/xAW1nVxn/Q1N5kE9QN1gwNH1cOoaeOkluPjiMc+zK6QDH4d3tI+TLKuf7L3EYTVld0uVRFsj+M36WF8/eu7ss0c/P/bYCZ8y2TWw02iS/p3IKsnolaI055yLDGVhTWQ+Mgrr1EHMkTAHV52Mgc6AvHvLI9RXWqj53Pqcvn4qmabYCGv8eoS5DZUjGc4quyXhG18+p5zu7R1iZvgAA1a9jsTlsBDxHOJo3qQyEmCO9xUMA2rsCnoc8Pdvw2X35bd9qa73jBYh2NUdwWY2YbWYcIQHOWSeyZ6eIT71882cu3TmhBmxybJn8dOSbT7dBvPw0KTZ42TTmY9rrh1Zq5coo5koE9jnDXD7IztY1VpXtGs+RRG55x74+9+PPP+3v+lsnlJ5b1I5GD8b4S2Lm1i7qpnmf9zEngMWugcCVNkHWNDoovHWd0JFw+h6vTi1c/TUzvhAL9lSh2SzZSpt5uyu9U1U0KUvGjTGB3qXX66nby5ePOmeiMmugTtmX8y5vl/qE1Pp30mhPjGOBHqlKM055yJDxbYmsrMTtm074vRRf9PrHIInnkT9gd6R82fOsEGOO7yTrZVIth5hOKg791edsYANm7uSr2U0DLjxRhgchG98AypzMw11bn0lBwdmURvx4DNX0YCbWaZdOCIBQpioCfdhNinM1hlABHb/Xb+x5unvIK31ntEBofBwCKu9Gkd4EHPAzb2BtxG2GUQMY8KgKJXgPdbRMoVDWIMBQAd6k2WPJ5rOPNGgSKJA96DHRygcyXzNp3SQpoebbtLl++sS/B285z35b08ZGf+/297RR9fu1xi0zsCkYP/AMHt6vMxw2VhZ/QY1CZ7jbacfA4Cpv4+IYUy41CHZVPdvP7KDmTW2MffNaNAw0eB6n56eOubvqKVlzHrDiSS7Bp58/vlgWjS1/p0U6hMJSKBXqrKxv4xITTGtiTQMXc0rQaAHwPHHc8MHT9Plqr8QPVdbm/PtDCbLNCVbj1DvtHHLJceNPCbpWsbt2+E//kPfaflyuPLKrLU93tpVzfxu37m8b/A+HEC9rwMAi8WCWSlChiIQNggO9ODGidVix/T0T2m6PD//i+M7B/V9Wzm370+cUDsIDx01NjiJDgip391B1XAX7opm7g9eyBu2ozApxvw+Eo10Jwre+4cCXL9hC3NqKkb+jq49fzEPPfXayONmWyMsSGHN3FQKpCQKdN3DQWrivo79bNJa8ykdpOnD54OPfhS+971Ct6TsbdjcxXn2OVSE+ukatmFSCqvZRHBogOcjdTQmKNq0cvEcIlYr9cEhDg74Jr02JBoYynoBtESD683nAs+PzeilYeJr4BT7d8U2KC2KggR6QkwmzX14cqK/X5f/3rlTB3lf+YoO+MZbtEgf4zJeA/sO5Xw7g8neWFOZcjjhG99vHxt94Fe/qqu1ffWrcPzxWWl/fBu46GKeeqqCo/f/nkWRfsyuJsw1Swh3Po8/YiIchuqvH4KLGti0uo3qN1+hK1mVySyL/xlZ9m3mPcP30zijkZq61sTBSctq7O+6nf+K/v437enDajYIhAyOmaXXnCQb6R7/O+sZ9PPGoUEihsHxrXVj/o5uPKt15H6O6Fq9mGSDDFOZzpxoFNxiNjHTZaeqr5shVw0Ri3Vkzeec2srUpm9JB2n68PnA4Zj8fiJje3uH2NJwISfs/gGOsINBKqlSw1QwxFN1H2Aw0f+iUphqa3nLDCtv+diJU3rdnBRAGz+4/lI0w5coM5yirC/pKKZBaVE0JNATYjLFsCbyne+Ep5/WnzsccM01Kb/BeHZ3TrxeYft2WLIEnn0WTsz8jfWY8HZW9PyJGv8+mucfA50fTXmENekb3yuv6OO998J3vqOnxyxZMjbQe+kl+Mtf9M/n4x8Hp3NK30tbax1tH3gf8D5deGe4B0IBAhEzNvyo4QgAzr/0ETnRRofhYnMe9ygc+Rk99FMYnjNpcBIfHJoUmJRiRUs1DVV2IPlI9/jf2a5uLyaTwmUfLQQD0b+jY6PTpOrrwesdeY5s75mYaDDg8+cdzdB3vssVP/8ftq88he984bvpr/mUDtL04fOB3V7oVkwLc+sreebwfB4PXMpa0xM0c5iOSBM/Cr+d7u7ZNPn7Ez+wrm50DdwU5GVLld5e/R5TTH9LxTAoLYqOBHpCTCbfayITrRXq6tIVvT73OZg/P7Ug7/nn4frrqXjplYk7vA8+qI+/+MWUA73YG+szT23k1H0/Rjmqmd26kEbzIDx2Kx9c+hlufaly5LVjI6z/b76Z3d+/m2d39dI9GCB03HLOOn8NK2eOCzxeekl/3x/6kP44+mjYvRu6u+G112DNGrj2Wnj0UX3/xkZYt25K38uIwUE49r3wyA0w0MGwqqDCCGEK6AunORjBxRD3G+/EKMQehWkEJ7HgMBaQW82mSde/jB8V7x8KYDIpFjSM/m5G/o4G9fo8Zs4cLVNPivtcpSnRYMAhxyAAi1/6J/OHezn/rasnXvM5nnSQpodwGEKhksvo5Xrqfa6sXdXMJ3++mQHT0fx7eBHBiAGAzawweQOEDYP2RLMhamszCvQgxwXQ/vY3uO02mD178vvmUzEMSouiI4GeKF/ZLK6QrzWRydYKDfTBMRfAu96V+nOtWQMnnkjNU08zOBxkecfLXHXTx/jvH/6ZzsoG5tZX0t7RR++mXZwDPLHXS00GUxDbWutoq3kWbHPjOswVACw58BDXnn/jESOsc9e9G9ffn2B+3PMELDYIBY58gYsuGv18wQI9lfWyy3Rwd+ON8PLL8L73wa9+pW/L1OmnwwUXwPFzYKgHk8VLb7AOs8/HLPoB+D6Xsct+DG+J/iwz6ox1buLw0z+lt2sHnUYTO2ZfzMlnnJ/8OaYQnLS11nHRitnc9fQuuj1+Gl12rjw98Uj3+FHxOqeNRqedRtdoJ3kkcPJGO2UzZ8Krr+rOtMWSXpXQDMyI+EY+/8K/vgdMEVaEQ/jNFv7v/32JV9suTh7UdnfDT1+FvS+ByQomC0RCcFw93PzjrLZTFJjfr48lFOhlOyueT22tdcyqtqMwOOD2owC71YRCEYwYLGx0Jh70aWiAQ4cK0uaUbN6sj7ffXth2jCeF+kQCEuiJ8lSqxRWSrRUa9IDLlfxx8U8RF3C871CEC0NBwr29nLX+e5gjYZq2vcirK87kzMWN3LZxOx/26GyIGzM/ybQDMUGWKdEIa8/OXWxtO51HrriOJZuf4sJ7b8MWCvDwh6/lgraWsc8TvyZx/nz49a9HO24PPggHD8IJJ+jy1tkI9Hbvhi1bGFhkZbtpOYfwMxAKMmP4MLPoBOCpoVYWVel9ozLqjHVuwv3nm3mzF0KWBhrNXmZ13cmvHhqGiy5O/BxTGL3dumUnL/z1BY6efxTHt9Yx6Avx0Jb9LJ7pShrsjd+s3T0cPHLdy8t79QNi5dK9XqipyX5RhGT6+nSRnkvOhacfALMNk9mK49HXeNfjd7Jndiu2OasST9/66U/ht3+GxQvBNwDhIPT6YMBa3NcKkT5fdECgmKbbTSIXWfF8Oq65ljm1AZ7Z2UMgFCZsgFKKGS4bLfWViQd9mpr0wF2x6u4Gm00PLBYbKdQnxpFAT5SnUi2ukChQsjghEIaqqkkfHuuMhyMGB90+Hh+u4ELgXM8eZh7W5eZrVXgkU+NyWLEPugHoO9zHnp4hfvjEm/zwQ2um1v50skyGQVXPIfpXv4VDcxfhrmviwntvY6B+Br888zIuSLYQv3MT+F/VBWoAmhpg61b9+dKlOgjcvXtq7Y/n9TK8czfP9x9PHW6CYSsmBVb/6Aa9ZpOJ+korW7vcmXXG2tezx2shZK3EajHhowoHcI7/b2zYvDrxc0xh9Hb2Befw3/v38G8btqTdzgnXvTyrBwuYOXPkZ0dNTW6KIiTS36+ne51oguVnjvz9qb4h6l4+wFdfuAuWvhN2JHjsj3+sBwiee2703PvfPzpqL8pHCWb08pUVz5XYNaDKbiFsM2NSikAowrGzq5MP+jQ26mCqWHV36zbKnouiBEigJzJWlOsHSrW4QqJAaSA6LW6SjF57Rx/Xb9jCIbefQDiCy2HhpZWncfiPDZz+zRuo69dTYS5tsUJrHd9+ZAdWs8Kyf59+6cAQEcPg2V29iddNpCKdLJPHgz3go7uqAQBfVTU/+trddNTMSp7xiWVq60yj586aCb/u0Z8fe6ye1vn88+m3PV4goKcfdnbyuON63tV/D+agDXBSExgtNvLDP/wXPVV1/PHKf8usM9a/h+6gg0r76PflMzmZGTww8XOkOXrbuF///atIBMNkSrudSde9xAqwxGf0yFNRBNCB3ty5R/7fn30MPLcLfvoU8FTyx//oR2O/djhGsz+ifMR+pyUU6OUtK54jsWvAD594k2d39VLtsLK8uRqr2ZR80KepSW8RNDSUsz1TM9LTowM9IUqABHoiI0W7fqBUiyskCpT6o5ufT5DRi/0eDrn9DPqDRAwIhCIYThs/uPhTvOeJXxGYNYd5r7XDgQOA7kA8uf0wMzw6SHINDwKKaod1wgzPhIF9Olmm/fsBOFBVNzIdcMvClXh8Qa5NtuF2LFPbVgcrd0FDFbx9BXT44di3wLx5OqO3YQP85CdjH3veefr2VEQDlQqvh6e7Gzhofz9vMf5KizqM32ceuds5W58AYMv1t2TWGaudR2P3TgZCOqMH4Ih4OWieIOhl6oMs9qFBfFXV6bczmcFoRm9coAc5LooQ09+vp27WNo39vz/taLi/Aex18PZvsGVvH//76Bu47FacDjNeXxh3IMRnLlhBW/zz2e0S6JWjEgz08pYVz6G21jp++KE1Y65Xs2psyQd9mpr08fDh1K/Z+dTdrdcRlqJs1i4QJUECPZGRol0/UKrVpxIFSisvAv4yYUZvw+YuQmEDfyhCJAImk55S0usN8Melb+GJ489hbl0l93z9cvjWt+D55/miP8QlBzzMPazXVzmHPATDEY5rrk6a4UkpsE81y7RPZxLPP6eNfRW21DI+sYyNwwS3vFufMyLw6Tb44N3667Y2CAaP3FT9Ax+A+++fvF2gR5KjZrm72dlyDE8NteILRvik79ecxEtj7r62bTa3/e0NYIqdsbZ1zNt/M6/0ugkaLlymYSxBD49WXcLa8UHvk09CdTXt9fOmPMgS6e4m4nRlr9OYJKOXN/39uhJtov/7iBdOvgacTn792i7sNdU4KqyEAUcFBIaDR1yvDgehanCYT979XPHMUhCZK8E1ennLiudByoM+sUCvu7s4A72eHj2wVGpKtXaByIgEeiIjRbt+oJSrT40PlGJrhyYI9Pb2DnHQ48NuVviCEIoYKMAAPL4Qi5qqdNamqUkXKtm1i6pFi6iqtLF1/nEs370Nl9/L8uZqbBYzs2tsCV8nPrBf9swjnPqn9dz2xR9MLbCPZvSOalvMLUuXpvaYVDK1l10GZ56pg72Yt78d3O7U2xYXqFQe2k+XcxahSAQFVPn13/bvl5/DxVv1dg5tNn9mnbGW1VS//WYWxapuhpvY0fx+3p2o6uaZZwKw4XdbEw+ybOqkrVrpNWtJzA4P88yAL3udRrcbzObR6Uz/9m96vd6nPz3S3pyJRGBgQH+/k/zfp3K9au/oo3PfEG8L+otrloLIXAmu0YM8ZcWLSXxGrxjF1uiVmlKtXSAyIoGeyEhRrx8ol+pTHo8+TjB1c259JS929BIIG1jMiqWRHVxqfpK56jD71QxeD72Td6+6eHSB+9e/Dh/+MMMdfXxn43Y+9+ObaN27HZvFPGGGJ76jvPT5x1n48gvM9Rzi1eAkb3p33qk3M48X229tzpxJfwQjUs3Uzpo19uvqahgeTvllXn9jP0uin89wH8ZkUkTCsNL0BueFNhGssGBc1sQvzryO93/3W7BrF22nnZZZZ6xlNU2Xr6YJWAKcO8ndkwUtp9z53/Ce+3XwU12d8LFXr6zn6rdObc/EhGKB1rHHwqmn6s2En3sOLJbcB3puNxjGaGA7wf99KterDZu7OLmiAmswgIkimqUgMleCUzenpVgQVYyBXiSir2+lOHWzVGsXiIxIoCcyUkrrB4qyaEwqYuufJsjorV3VzIbNnSzjDa6y/oETjJcZMCrZHmmhGg/v89yL5VDr6N5EZ50FjE4LCvyqFturbuqd49ZNvPmmDjTnzoWGhjEd5YbduoThob8/z741Zx1ZwGXnTti+XQeXV1+tn2N84HHppUmDkYSmmqmtqEg50Gvv6OOhJ1/jxujXs93dhCMGK9UbXGP+DZX+Yfw2G7ZgP/OJBqvbtsFpp415jnT+1lK+v2GMfDo+aKk91EXT1s1c+HB0euqrr8JJJyV+wd7elH4WKYtVvayvh7//XZ+74AL9+89Ee/voxsk2m/5+LOPetmLVVyfIYMakcr3a2zvEqZU6ELAEA4Rs9uKYpSAyV4JTN8tZ0uteLKN3553wz3/CUUeNTpU85hj9XlIIbreethmJlGZGr1RrF4iMSKAnMlIq6weKtmhMKlLI6LW11nGuay/v8/yKlsgBPFSggBXm3byqFuEzVdH32F2Yf/xzjnrucWhtHfNYjl8If+3jFv/L8LPfwT336E5RdB0dRx0FO3aMdJQHBn3M6tqpbzq0m/1O+5E/z/PP18Ee6Dfu9na9jipTU8nUVlSMBgST2LC5ixmMbqEwb7iPSCTCu01P4jYqwG8QtFvoC1Vgqq5hJehA9uSTYeXKtP/WUrl/rEPU3XmIH0QfF/td9A8FOOj2cesPvsxJb744+sSvv5480IsFT5NJdeF+fz/U1Iw9t2SJDvoMY2plyHfvhuOPH3vu/vv1Wst40eJCqQR6qVyv5tZX4lU6eLYE/YRs9uKZpSAyU6JTN8vRhNe9ubVwxhnw2mv6I36rheOOG91OJ59+8QtYt27069h65FJSqrULREYk0BMZK4X1A0VbNCaZ+A72P/v1uUm2V1hrfhKvyYndCDNk2DEAmxFgtXqFiL+SCIpfh9/FUbfdduSDYwHY5Zfr41ln6eqVK1fqzvqDD4JhjHSUb//Rn3EEdafp4lefZPkfQgRCEYYeNMGxzfD5z+u1gFddxfYLL2WDu4LXf7ejcJnUioqUqyhu6+pn+QGd8QqYLNT1HSIUgbnmwxww6rH6QwTtVpRSuM0uDlx4FLP+9IbukKxcmfbf2mT3j+8QzTeN/R4uWjGb2x/ZQSgc4ehDu3lm9TlseM/V/PdX16Fef33sC0Uio5+nktFLZ+F+LKMXb8kSnY3etw+aowVlvF79d2G1wuLFEweAsWlb//mfcOKJcO65sGOH3vbife+Dzk79PW3apO+X4gj7ZNertauaaf+Nrnxq8vtxm4JFO0tBpEmmbhaNSa+TTz45eucdO/RslHvv1fteer3gdOa3wc8/r/9u/vM/9fFd78rv62dDKdcuEFMmgZ6YFoq2aEwi4zvY7t36/MCOI9eexWkMHuA1XHgNBzaCmAhTo4ZQhsGg4UKZzJy690fQuejIC/tHPqL3KzrrLH1cuHC0Ex4Mwm9+o99cq6poa61jxZCeArr7mOOZ1bGDhkcfAMCIROAPQ7owh2HQcfRyvt5To0dtnZbCZVInmLoZP33IYTWx89Agi/t1FnV33RxmDhzGAPYaTdThwe4LMlxdgcWsqIx42Xvq8TrQiwYm6f6tTXb/+A5RVdfAyH02bO4CYFVrHTPCw9R7+ug9diVD8xbRM6OFxtde03c8dEhn8OLXQqYS6KWzcL+/Xwd28RYv1serr9aZ3Y4O/TsIh/X5n/4Urrgi+evHCuKcdJL+u5w1C/buhTfegN/9Dlat0gVfPvtZPSBxyimTf08paGuto76tFX4OA71u6hfPKMpZCmIKZOpm0djbO4TVrHhht4dBf4gqu4X5DZXs7R1KPKXztKP1NfbOO3VG7+ST89vgXbv0++LnPpff1822cqldIFImgZ6YFoq6aMx44zvYITMoYPvvYMkZSR920DyTWtXDHjWHlbxONV5MRIhgwhweZq9jOWarLXFHfdYs+EyS6Rv19frY2zsyfXRhUFew/L/PfYP+GTqAcA8Haaiw8LUPngwPPwzA34YcuOYUQSbV4Tgy0OvcxOGnfwq7X+M8+xy2NFzI/Xsb8QbD2KKVNXc2NHNyh54m9NvIW/i86de4egc5PK8BR9iDSw3zB9dlrFa/wRRd/5ju31r8/bs9Pnb1DDEwFKDOaaO9o08HgtV2Lrjvds584Kcjj+s67CZitjCzxsGM7bsAONyyUAeJTXNpjGX0li/XwV5X1+iLbtkCv/+93lw+WZnwdBbux4qxxFuzRgdgL72kS6S/7W16EGH5cnj/+3XHaSKxQC+2YfLcuTrQ26HXhvK97+Wss9farAstfPNdS48MYEXpkqmbRcNhNfHC7j4q7RYUBvsHfOzu9lJfZePm379Mc13lkVM629r0g3/9az0ANJUp4VO1e7e+XgpRYkyFboAQ+bB2VTMeXxD3cJCIYeAe1tOxjtijrBj079EdaoADA/DL58BqgYGOCR+2Y/bFVBpeXMoHGJiJoDAIGWYiEYNeb4BKV136FbbiA72ok+16ZHyfo2bMz/M9a+bqjvHmzQC8ZqujyjF2PKkgmdTxGb1o1vTAgS4GrTOoxcPbDv2Eeb7XsJpNVIX11gy765up9Q3iCvl4xbSY+/rPwxIMYzSY6DNc/NB0GX/obyFQUzdS6Cbdv7XY/fd0e9nSNYDXH8KkFE1Vet2jw2rCvnfPmCAPYFGFwVkd7bz1rv/m7N/cCcDh5gUM+kJ45y/Sma9weLQAT0fc388jj+ipR6edNqbAyxi18/QajnixhfuHD+ts3E036ccnmrpZW6vXZXZ0wFNPwW236Wqvl12mb+vpSfy6MbFALzZFq6VlbKB39NETPz4TsUBANk0vLzJ1s7go8AXC9A0FCUcMTEoPGHb2DxMIhTEpRXWFFZfDqmcwzJunf3e33QZ//nN+27prl17OIESJkUBPTAuxtWX1ThsHB3zUO23FW4gl1sHu9cJt0S0JzlgwaWWsk884n7ss66iN9BExTHiMSg4bteyjkSEctBpdHO4+zGFL8umfCSUI9GYP9xOqq6e6turIn+exx47cr3LhfAZ9oTFPV5BMakUFkeFhbnpgG1fc/RzP/u4O+iIOukMVWK1mfOYqhs1VXMLjKMAR0B3C4xt1MZmv+3/MpTMPEOrRQevX6q/km5ZPcKh6OUtnVeN21Y1M3Zzob629o2+kDTc9sG2kUum15y+m2+snEjGosltYObeW1gYnLofOCloPHjjiW7pkoYv3/eZ7nPXXXzLvlc10zV/C7uoZeHxBWk89Xmcv4tfpPf+8Pt57r17X9tnP6kI/Q0mC7rZ1eqG+b0BvSu8b0F+3rYPHH4ef/UwHbj/7mV6LN74Yy0QaGiafPjo+0IvP6NXV5ba8uQR65UkCvaLhC0Zom1uLLxzGAKxmRaPLTsQAh9XMrp7R69LI4KBSsGGDPhndhzXnwmF9zRkYkIyeKEkydVNMG6VQNAYYrYx146PQOQCXroSLF+rzScTWNHQ5l7LP38Bm42jqcLPCtAsrIYKYcRmDeCNeNoTfwtWptCNWEGbrFv31jufhnHP05/v2YWmewy2XHHfk42Kbn9fU8K5TFhXF9hv7AzA7EKDPM8zMGgf2g5080VtDBIXHp6itsILFyQJLFyG/wbzQfiImhcWug9QZkUE+1Pcznu/TBT8+v3QzLvuj9Fpn84LjAnora5gRy5yR+G9tsuqac2oqOL61DlPcdKQqh4WDAz4+OP/INUXLqk1woIvu93+I71x6nV7P4qrgylXNtHZEN4v/3e9GH/Dcc/rY2qrXt8UCv76+xIUNJlq43/vC6P0++lF9TKHq5Yj6+skzerEAND7Q83rhr3/NbTYPJNArV7Gpm7JGr+BiU9adNguNTjNKKYLhCA6rCQV4/aMDhGMGB0+M7v+ZbIAq2z76UbjvPv35UUfl5zWFyCIJ9IQoNi2r4awvw4Hfw8I6+MBZsOZDSRdQxwcQba11dPXOoAY3vdSwxVjAQnWAWgbpM1z8yHQ5Q/75kwd68QVhZrXoc8/8Et5xnm7H/v3JNzq/7LKRfeXysf1GKnvQbekJMBuotxgc8AZ4I1hPLR4GlZNQ2KB7MEBLZYDBijnYwyaWhXYRtprxV+gptI5BxdLvP8FSIGwzY63w029uojI0wDkH7sJaY5t0c9/JqsxNtLZvQVd0L8WmptHXOXgQenpoPPbo6D6K+mewYXMX5nkzWQ5w442jDYhtWB+r3hqrtNrfr6dFJpJs4X4sG/fCC3otHqQX6DU0jE4pTWZ8Ru+EE3S1zp079f6LuSSBXnny+fQ+jGZzoVsyvSTYpmXtqoXctnE7NrOJQDiCSSkCoQjHzKrmzUODOO0WIoZx5OBgbM1uivuiZmzHDj1L5QtfgLe/PT+vKUQWydRNIYpR9dEQCsMnvwyXfHvCKlnxAYRJKR6vOI9qNYwLL/3UsN2Yy6vGPG7iap4PLkht2mR8QRhX9P5+kz4PumT+7NmJH3v00fDLX44Ud2lrreOWS47jno+dyC2XHJf1IO+2jdvp9QbGZMnaO8buE3c4qLNk1oCfXd1e/mo9l2rTMFWGl0a7iRVPb6Xh76/z5uyLuemiY5k15CZis+Cs1h3+6u7RtWphmwUPTlAKj1GJx6hgTs3gpIHL3t6hCdcrTri2b/9+3TndsQN+/nP94FdeAWBPZf0RP4P/2dRDsGmmvt9HPgIf+hCYTLrDsnChPh8L9FLdUy9eb69e97h6td72AHQQlqqGhtGM3u23w5e+dOQIfSzQiwVdb3mL7twFg/Bf/5V+m9MRy/hIoFdefD6ZtplvsUHD4Z4x27S0mXZy7fmLWTrbhdcfQgHLm6tprLLTUl/B0tmuxMssYr+/fGX0Bgb0dfOKK8Bmy89rCpFFktETohgdPKiPE2ynEDO+PL9qXs0dO4JczOPM5TCdzOAe4yJ22ZdgM5tSK0ATX3HRbgGbGYYNff7mm/UeZskCvTxKdc+6qlqdxbIEfAz6TbxhW8KPjfdzkekxFu9+k9ZH9PTSRfP+AV27YeshwnOqCURH/iviphEpmwW71TxSEnzBrNlUVL2pA6bhYR0AJTBZNc4Js58HDugNemtqRqtkRgO9RwbtuJqO/Bl89+af8PljnXD66TqLMV6mgV5s7ebVV8Pf/qanVqYqtkZv3z649lp97pxz4K1vHb2P16tH701x45H5ysTEOpOxqX6iPPh8Mm0z3ybYpqXtom/xww+tGTMrY1aNbeJZHyaT/v/MV6DndkN1dX5eS4gckEBPiGJ0IFp8I4VAb3wA0ehycGBGGw91K95jeoL5HOJSnuSvysoF512YWkatdp4eeY29KVc54NUu2OiEezforN373z/V7y5rUt2z7vjFOij1u704bbV4A2G2chS0rMJ36BFaeQFPbQOu//1fAEJ1tWx/xwICYf08Fs9oRi9ihhPm19E96GdX9xBvdB6ge+YcjuFVXV0yPliJs3ZV88h6RX8ozI6Dg/QOBaitsPLu7z3Ncc21rF3VnHjd44EDo4F1bOrlyy8D8JqlJmGm8CV/E5x1YvIfXrYCvUsvnTjDm0h9ve5APfvs6Lnu7rH3KcSmyDEydbP0GQY8+qjOyMS8/rpk9PIthW1a0l4/X1mZ34xeOoWmhCgyEugJkaJU1oJlLLaW4S9/118bkxSsYGwAESt4siS8nQ86fkt30E4PjSyq9PPt2j9SPfsEoHXydsQKwoB+U26uhq374fVDeurfCy8UxShnqnvWzZuri6g0msNU26zMe3Mbx0U8NPbZmfVqOwC7n3iO5a8+j/+L/8b/XPZFdjjDfGDgTwAE+oMjzxUxQV/vYbYcjFCjhqk2DfPQcetYaHmK4JduwHnSSQk7BrGM3Q+feJNNe/qwWUxYTYpQxGB3j5d9Az4e2rKPkxbUc/WZi8b+bcUHUrGf+z/+AYBz0XwO+0Lp7xGZrUAP0s/uxipmbtw4em78GkcJ9EQmtm6F88478vyqVflvy3Q2ftAQRrdpmarKyvys0QuHdUXhInivE2KqJNATZS8bAVp8wROLWfHk9sPsbH+Cq6qf4XiXm5rZR+ngaIK1dJOKL4Dii64F2H4vLD1qwkIsGzZ34fEFOeD2UWW3MMNl5xz/I4StLlxV1ThCETxhG2GbSrxZeiLjKy5+7X0w/x0wZ6UOYopk+lOiIDdhVc/odMpPn9LCp5cuxWg4CxUanY4Zrqhk+fIFsGIhX7cvo9cboOugh2/5ZvBW/kjvoIsWdDnvg+YGdveYmclh3I5mHqldR7fzWF5ffhLLX3wKrr8efvSjhO1ta62jyeXg1EWNbD/owR+KEApHGApECIQM6p02Xt3v4baN27nujFZWfuPLOsh78UW4+GL9JHV18NWv6g18ly3jkpMWTK2yaSwYnWqgl0nly1igt3693ndxx44jM3pDQxLoiamLFQy66y5dyCemNYWBLpE94wcN/R69Tcspn5n6c1ZU5CejF5vJIRm9vMnLgPo0I4GeKGuTlbRPVWwtWCAUZluXm+W8wcdNv8LrdbIpVM3xtv3UPXarDo6mGuy1rwdbFVAJPV4wKWhsTBqcxX9vi2dVj3TwAZYYu6j19+Ec6gcUHlM1+7sXU2cOpN6eZBUXi0jKVT3jO+5vvqmDvNtv11sO/OIXmOfM1ns0MToddEFDJZsP6kvkjMHRPd9qbMPYPHsZqJvHi7Vvp9Op9w381bX/ReVXrmLRb38L3/9+0vVksecf9IeosJnpHwphMSm9l5TFxHAgjMthZcu9v2PlPffo7SquvBJuuEE/gVJ6nWTsZwBTq2xqNusOTDYyeuk67TQ9xXVoSBeKufHGxBm9yhQKB+WCBHrJJaigWJTXiVjGZ9kyWLGisG2ZzibapmWq8jV10+3WR8no5UW2+mtiLAn0RFlLtVjHZGKd8017PFjNJi4KPcaQqQpPpIJGi5k33GZOmFWdesYskf49cPOT8Ga0w9tYBRXVY9YypPK9+XY9xwzjAPbwIGFlARQ14X4cnheh+Zypta2IpbS+I1YgZXh4NKA46yxdpOQXvxhz19h00EaXA1WpO/yNntFAz2I1OEQDc/39nH/wLjbOvJJO57H0KjvPX/oxFv3PdQRnN3Pr9/7Ebk/oiFHJ2PNX2S34QxGCYQMFWMyKUFhvmF7lsDDzmSd0uzdvnnRd0ZT3iKyry2mgl3R0du7c0e0eAL797eJaoydVNxOLn3UQV0ExowGuXIkFekmKI4n80NcAO3t7L9PXgBXNtLVk2GnP19TN2PrOIsrolXPGK1v9NTGWbK8gytpkJe1TNbe+kkFfCK8/hNWsmBU5xKBRgcVswmoxMegPHbHAPG322TrIO2E+XHEaXHP+hGsZkn1vF0YeJRIxMBPGZvixGAEggl0FIboXd3tHHzc9sI0r7n6Omx7YdsR2BGUnPtB78039+aJFerQfCHX3jPw8Dnt8dPUN4R4OUlvlwGe1YzEiI08VNFuZVzHMTP9OZgzv4D2d36C2dwtd/UM8tfQUDtXPwnr4IN27OhNu+RDbRmGGy44/GEYpCBsRKqxmAqEICxqdnLThbt76t1/pLQVyWTyiru7IAGsyw8M6AKqb+I031a0vgLH7A8YUMtAzm/UggFTdHCu+gqIy6aOjenTblWIigV7BpXUNSMc0zejl7OdZJLLVXxNjSaAnylosQIuXUqGKcWKdc5vZxKA/yM5QA+agh3BEb+haZbfooOxPXfDOd+o1R+mqPkUfz5gH7z4eFlfrtQxt69L63tqse6kwBgkpKxFMmIhgIoyy2iE4XPZvFgmND/SamnT1ymP1tMvwsG/k52E2mUBBOBKhusLKsHVsoKVMYZbxJk0VEDC7cAT7uajnbo4KvA42Oz9528cAOHigj75ooRiXw8qGzV3A6HTThU1VNNdV0OC0YjObcFjNLG+uxmo2seyFx/WLxU3RzIlTToGHH4b779frCrdsmfwxe6KDGck2WY8av7/j+J/DGI2NxZXRAx1gf//7sHjxkR/Llo0Uw5lW+vfoAa14mQ5w5YoEegWX1jUgHflao1dkGb2c/TyLRLb6a2IsmbopylrKxTomEeuc/+efX+WFPUP8znwm11p+zWDERN9QBUfXhMEbgF88ox8wYwb85CepPfm+fXpD7FejG24vaAX3/knXMiT73mZXhDCFbYQiEMCKSYFVhbEqA2rnTc/pEbHO3pe/rAOVk0/WX8+YwbMXXMY/1pw37udRSTgSYdmcGkJ2BwyNlmh32YNY7A4w25hhDoB1Bp3Bat5pPMHDFScyoPTzVEWC7Or20lBlP2JUcvxUy/H7SC3wHNIbncfamSv/+q9w5516nRzotXNPPz3xY2KDGIsXT3i3VLe+AHTg/eSTekro3r16U/T+/sKt0QMdZG/alPi2X/5STz099dS8NqngclFBMVck0Cu4tK4B6ZimGb2c/TyLRLb6a2IsCfREWUu5WEeKz3X0zGpMysShwSruHLLwLvU4R5m62R9oofXY9wMPgs0GP/uZzpRYrXDNNWOrvsV74gndoQyOlu+/ynIls6rqJ13LkOx7q/xLDQR6sQQGdeEOBUQMMBS0rWPvX8v7zSKhujr9u9izR5dXv/pqfV4pfvDea5lZ4xiZ3tDifYWj9j2Iw9vFIfNMQtaxEx8sphCYLBAKQDgAM5fR3WkwW0X3PoxmoZxhv57Sy+SjkmMCP78fDh6ABXl4c1uwQG+VceiQLojS3z/5Y7brN+LJqm6muvUFADNn6oxerBpnTGPj5O3JleuuS37b00/Drl35a0uxyEUFxVyRQK/g0roGpGOartHL2c+zSGSzvyZGSaAnyt6UC1UksLd3iLkNlcxrdAKn8Qyn8Q/D4OCAj5MGohm5e+/VAVwwCK++Cp///MRPunYtu952CQs+obMq9U21KVebSvi9zV4Jtkro2w3ebjAAex20ngAtq5lbv62s3ywScrn0lM3h4SMyUfFvni3eVzhz3510eC0cUvU0mLy4LJ6xz2V36A5uRS3MXAZVTTRaOzho6M3t6xt1p0ANDeG0mXEPB9MbldyzR2/2nI9AD2DlSn287z694ftkduzQhVgmKcaS1ujspz6lg7xwWE8JrajQgxSnn57ud5MfCxZMz0AvFxUUcyWW8ZFAr2BylqHJRkbvn//U79UTeeklfSySjN50yHhls78mNAn0hEjDhCNqr7+uT5x3Hlx2mf48HNZreZJV75sxA1au5O4HtlF34100hIZG5t7DFKdTxkbdm1eNHXU/7RpgerxZJDR3bsLT8T+P4/v+xMGAHbfhwGExMWRyErLZxj5g8dkwM6iLUNhd4BtgnjPEb4Ln4h4OMrtWdwocQR/VFVbqnbb0RiXfeEMf8xXoxTidenPg8Z56Cn7609GvH3kkpT300hqdnTkTPvvZDBqfZwsW6J/DdFQC264AelDHbgeTlCIolJxlaLKxRu+b34QHHzxyFsF4Z5xR2LXCcSTjJaYiL4GeUuoOYClQDbwOfMIwjOG4298HfBPYGT31tGEYN+ajbUKkIxYUVA4OcMVPbsHiHSQUMWitr4S9u/RUs/g3DrNZv1FMYm/vEIG2Ezgc3csNMphOOcmou7xZjBX/83Du7eJQpAaHVWEy6d9FSI3dD+/pvgr+5nkHF/Q/yjGODmpmH0X1KZ/h3ZGFbNjcxf6Aftz/O3E28z912uQNCAT09gIej546+d3v6vMLF469X673L3M6dQGU8b79bfjDH2DWrNFz731vSk9ZtqOz8+frtbV+/+hWDKK4DA9LNq8I5OQaEMvoGcbI/qdp27sXzj137DYvJaBsr6kiZ/KV0bvRMIwBAKXUvcD7gJ/F3V4P/NgwjFvy1B4hpiQWFLTf/WuWPf8YB+YvpqqpnipTBObNg7e9LfUni3Xc97/Ef3Z34z1YwaGqJWyObsKd0XTKSUbd5c1irJGfx0MrMb2xk0MBB/3DQV15c7+uRuqz2HCEAgwaZoZmtvGdnsXs6vYyO+hgWcjO2lVwyyXHwfEuuAnmV6aYSfjnP+GLX9QdFpNJV2299FJobh69Tz72L6uq0p3jSESv1/vGN/Sm8vv2wZlnwsaN2XmdcrBwoe5kzplT2IzRySfrIFwcSQK98hUr0uT3T7r9TNJ95/buhRUr8tBYIQorL4FeXJBXDcwGXhp3l3ogzc2chCiMttY62pbrrN2sB38NK1aMfTN5YNvkm5jGOu6RELg7qbeAdchNYMjBef67eKD2CrrUUeU/nbLYtK1j3v6b6R9yYxh2XHixRcs9P7PweM7a/izmCge9g352dntRCtzDwbFrKmOdkFSnFsUquz3/PKxOErTF718Go8f29dkL9GLTk4aG4NFH9eePPw5dXXD22dl5jXJx0UW6WEs+CkIk889/Tt/po6mQQK98xX6vQ0MTBnqxrYRcDuuYrYSuO3M+Kw8enHSLGCHKQb6mbp4DfANYDNxqGEb7uLs4gKuUUh8GdqAzgB0JnucTwCcAWltbc9pmISYUV3Y52ZvJhIVUYh33Q6+A2Y7NbqPK5CMU6GWXmsdZwxt527sulKxbvrWspvrtNxP+1bdpCXXSRRO3XfFZ7AeGmblvNwADholdPUNYzSasFoU3EB67pvKt0SmXqQZ6nmixl6qq5Pfp36MzefGyvX9ZLNAbHBwtNLJjh976Iz67KHQhmm9+s7Bt+NrX4MUX9Tpgs3ny+083EuiVr7hr1es7X6L/nz/D4e3E52yh9pSPsGSNHpiKbSXUSIB3fu9rbHz/p+msrOeRR9tZaRhJ120LUU7yMufEMIxHDcM4CZgHrFJKXTPu9q8YhrEKOBXYCPw2yfPcaRjGGsMw1jQ1NeW62UIkFxfoTWkT09jGw/5BMOsgwW63M9Me5ORj5nFS3aAEeYXSspp7aj/D75Z9h4fnfYFfHX0xvzzr/YSt+vfU4Y3Q5w1gNStCYYMqux4vG1lTGRthTjXQixVAcbmS36d2ni6qEy/b+5fFOk+HD+vtFgBeeQVCIT1FURSX2O8r0bpKIYFeOYsOPO3Z+CD+jbdg8fXhq5iFxdeHf+MtvP7CY4Be+17lsLBk81OseexB3vODm6lyWPDv3K2fRwI9MQ3kdXGBYRhu4AfAuUluNwzDuA+Yp5SyJrqPEEUhFui5XCNvJvEmLaQS67jbqyAc3UMvHASbq3g3IJ5G5tZXMugLjWTubBYTRrToRshiJRiOMBwMEwhFWNCoO9wjaypNJt3BTNYB7+rSwVRMKhm9tnW6cqpvAIyIPvrc+ny2xF7/lVf08ZRTRm+TjF7xkUBvYhLola/58wEIPvlrghYXIXs1KEXIXk3Q4qL/n7oEROw6PnPvmwAsefHvfObaS/nk3V/TzyNTN8U0kPNATynVoJSaFf1cAZcAT427T1Pc528HdhqGEUSIHGvv6OOmB7Zxxd3PcdMD22jv6EvtgW637kRYrSNvJvEmLaQS67hXzYKwHwJeCPnBNSv7HXiRtrWrmvH4ggwMBbCYIRiOEDTrbRaU3Y5hGAAsanJS57SN7JW3dlU0IJpon6eWFr2tRkwsozdRoBerpFrRAO79+pjNQiwwGjhs3aqP58aNx0mgV3wk0JuYBHrla54eCHUe2EfINnYmRMjmwuHtBEav4zPe0INXL55wLgca5qBWHQ//8i9H7KmaK1PuZwiRBflYo1cF/EopFQbCwBPAt5RSHwMOGobxR+BmpdQJwDDQB1yWh3aJaS6dtXXjK3d9bt9hGqObqE5pX7r4LRACQxAY1FM5G47Kftl8kbZYddXrN2yhzxugttJGbb3uUDQ2VnPJ8S2sXdWcfIuKVDf0HRjQgwZO5+TVGyeopJq0slw6YoHDtm36+I53wNe/rj/P955+YnKxgYFEex8KHejFbwkiykdlJcyYwey/vknT03sxlGJwQSPbrzobS8CDz6kzdW17tvGD712L9eVt/OPkC/jzF/+Htauaqc3jsogpreEXIotyHugZhrEHOCnBTXfH3ef/5bodQowXv7YOSLpJeaIL9Y439lFVWYWDDPalK5WNh6epttY6/nvtipHfvbVSZwc8hmkkkEr6Ox4X6I0EYj1e7omd/M534Jpr9OczZ065nVnrSMQCvZdf1usMTzoJtm/XhT7yuCY6K0HrdCAZvYlJRq8o5Oz/ubcXgKGWKszDEWpe2YfFN4A1PEjVKdfo+/zpTzhf2gxvfzunXn89p77luMxfN02p9jOEyJV87aMnRNHZ2zvEzJqxpZkTra1LdKGu9g/RY7ITm9CWrNMvndbSFh/E94f1xrwXnbCAhZP9Dp1O6OuDa67h5cs/zm0vD+FyWJlnHZ3iO/jzXzIyWXOiQiyTyFpHIhY4vPEGHHOM3tfv6KOn3K6pkNHvNEigN7GhodH91kRB5Or/ub2jj3l1M6g7vI+7PnkrFz9+F0ft3YQRcWI///MjVTfp64PGRnjooSx9R+lLtZ8hRK5IoCemrbn1lfR6AyMdY0i8ti7RhbrS72XAWsFEK5ek01oeRoL4zqPhV7CwpWHyB1VW6g3GN24k2P4mrqv+neoKK1UHDozcper5Z0bvP9H6vElkrSMR1wb3zGb+54FteR+gkNHvNMjUzYlJRq9wOjdB+3oqXt/Gx00z2WZ7B53q2Kz8P8feVxd+8fvMGurlzaaV/HXGYY5iE2su+aoepIrp7YW6wl43Uu1nCJErea26KUQxiS3Udg8HiRjGkQU1ohIVW7F5B1HRNXrJxDqtwXCEzXv62NzRx56eIX74xJtZ/15EHkSrbo4cJxKXSQgOekeqsjrdvWPvp3SWcKJNfyczpWJAicQyRMDzqoZeb2DMAEU+CghMqYLtdDXdM3rf/a6e8pzso7dXMnqF0LkJHrsVhnvYF6mnFg/nH7yLFq8uiJLp/3PsfdW/YCF7lq2husJKqCE6tfzgwbF37utLPdDr3AQPXQf3X6qPnZum3MZ4qfYzhMgVyegVOZn6lzuprq1LVGzFNjTIzLkTr6va2zuE1azY2uXGZjFRYTMTDEV4dlcv7R198nsscuP/964cDDEP0g70Wnr3M+gLUV1hxenuH3u/t74V/vIXnX1IRXSknP49eguOtnWsXbUw/WJAicQFeu5ZcwuSVZPR7zRMt0Dv8OHRrUjCYbjlFl299vTTE9/fZIKPfCR/7RNa+3pwVIOjhipHH56gCcywqv/PdDqPzfj/OdEMhtCMJIFeb29qBXliwamjGqpnw3CP/joLlY2nvIZfiCyRQK+IFe3UvwSdzVItKjJhQY24+4y/UNeFfFhmN074uLn1lTy5/TA2iwmrOZY8V1RHN1OXC33xSvS/9/vtfXwGxmTfkg7EXH45gwcO49vTSePeNznqFz/hXdseZeGe1wD46Ye+yMWBLhreEQ30Upl+l6Qz0nb2DdnpSFhHgytv89iNhPOVVZtSBdvpKhboTYepmx0dsHChDvDi3X8/nH9+YdokEuvfo69PwILGSrZ0DuAxVdDo3zeSzcrk/znRYNBBe43+ZPsL8NCzo32T7oOwdOnkTxoXnAKjx/b1WenbpNLPECJXJNArYonWq/QPBbh+wxbm1FQUJsOXw5GvYjbmQv3lL8NA/6QFNNauauahLftw2i0YhkEwbBAMRziuuTqnnWbJAmcu0f/ertVn8PBHruOC5cuBiQdiOOWt3DY4n1Nf/gdX334tn//j9/E4RjNmq7/0KRqObYVnouv0UsnKTNAZabvoW9n5Hf/+9/zxwX/wwrJTiF/dlK+smox+p2E6ZfQ2bdJB3te/DnOjgxB1dXDeeYVtlzhS7TzdL3DU0FhlZ0VLDfsPHqQrNIN6py3j/+dEg0GD1koMkwn1/O9hycmjfZPuQ2BNYUvmuOB0hN2lzwtR4iTQK2Ljpyj0DPp549AgEcPg+Na6wmT4cjzylVW5yjzec48+fuADE96trbWOkxbU8+p+D8PBME67hWNmVWGzmJldY8u8HQkUbRa4xOztHcJiVjy/24PXH8KkQKF4fOFb+fvvXxnZQy9Z4RAAl8PKntPP45aVT2IOBdlnc/HdD58IwIql0c7q7GjnIpWsTD46I+98J80rT6d343Zcw8GCZNVk9DtFZrPOLk+HQO8Vvb6Lz30uo8JFIg/a1unBXwC7i0aLj8YGBWd/nnNbMt/eINlgkKp2wIsH4SNVoExgdcFQEHwdkz9pXHA6wu/R5/OpjGZLieIhgV4RGz9FYVe3F5NJ4bJbMSlVmIp0JTLy9foLj2F64hv0hB2YHTUcFd5PXTYyj4cOwf798K1vwbJlk9796jMXjQRe+eg0F3vVwlLJNjqsJl7Y00eF1YLC4KDbT8QAq9nEk9sPs7WzH6Vg8ayxBXnipzjGBmmGXLUAOA2D//zC9/lSg3u0CEts/7yFCydvVJ46I2WZVSvXDpTTOT2mbr78MsybJ0FeKWhZrd9n4//fTvlMVv/fEg4G1dlhVx9sfBkuXAFDAX3enMLsmXHBKX4P+Ny63fkyTWdLidyTQK+IjZ+i0D8UwGRSLGgYnUKV94p0xTLyNYH2jj66H7uLOipQjmr8oQgvHjI4foaDukwzjy+9pI9tbSndPd+d5mLesyfX2casB5GGjsf6h4JEIvqUWUHEgM7+YSqsJuZEi6zExE9xTFRUxHbqGXBJ3Ki2wwF/+AOsTuFvMo+dkbLKqpVzB6qqauoZvfZ2+MUvwDAyb0dlJVxySfYDMasVtm/X7bzwwuw+t8idltX5/9/62mXwoR/C4WjBHo9PH5tmJ39MTB6C00mV0mwpUVIk0Cti44OEOqeNRqedRtdoRz7vFemKYeRrEhs2d7E2dJDOcB2h4WEsZhMVVjNvDJg5wTyFzOO+fXDddbrD0d2tz61cmfLD89lpLuaqhbnMNmY7iPQFIxzfWsvuniF8oQgmBTarGQCbxYSBmeFgGI9Pr/9IlK1NuajIRRel1qhi6IzkQM6zvOXcgXI64eGHp1aQ5Lnn9KbicUV4pszvh3//98yfJxmzGd75ztw9vyh9Z10J9T+DngEwIvjvexI78AvfUl55YNvk15VCBKfxSmS2lCg9EugVufggIdaZdRdo7QxQEp3NbV39rAzWU8cgQ6YqwhEDty+ILeSGhcdM/gTj/fd/wy9/Ce94B8yZA5dfDg0pbJpdAMVctTCX2cZsB5GxgHnN/Hq6+ofBAJNSmKIzLhVgM5smzNbmJJNb6M5Ilo0P0Ku6X6L7/77BQO0gNbOPys4Uy3LuQH3kI/DggzpgS9db3gLf+x60tmbejs5OeOKJ7GQH4/X2wu7deiuFuO0/cq1UppiLOC2roXkeDPoZ2PUmNX/fDcCe5eeXxlr1EpgtJUqTBHqlonMTbVvWc0fkDV7rreNh8znY5qwqzNqZIu9segNh/mQ6h6uNX2JC4VWVOCNeHOFh3XFMV08PLFgADz2U/cZmWTGvr8pltjHbQWR8wFxfaeOQx8cK403eb3+KZu9h9hpNvDH7nbS1npv0Z1tW0x9zJD5Ab/G+wvn99+Chku3D1ZyQrSmW5dyBuv56/VFoLS2TFqcqFVLQqoQ1z8e7/xDXd17Kj/gzt3zoq3Q4m2gssrXqCZXAbClRmiTQKwVxa0xqZrZykt/DSb4HYM0yaCnSi1YBVdktvDx0FHeZ38+FkceYFT5Ip2rir673csJUOoxuN1RXT36/yeSpIESxBhi5zDZmK4iMH8nv9QbYtKcXXzDCcvUGn7P8Bk+wkg5qqTV5uCKwHjqPLepBj1zLNPMRH6Cv6v8zw+YqQuYqhgJhcNRHXyTDKZZl1oGSbFNuFXtBK5Fcr7OWcGc7Vfs6AdhbPYOtXW6WN0N9lb0o1qonVQKzpURpkkCvFEywxqQ9sjBnb/ql2qFYNqeGCquZXZ5j+C//UVRVWJjhsrOwaYqFAtzuSffMm1Q5F4RIUS6zjdkIIuNH8ocCIV4/6EEBM6rtXO5/mv5QBcNmJ5U2C2argz3eQSxP/5Smy8vj95fu/3s2Mh/xAXq9fz/91iaCoQhV9uhbUzamWJZRB0qyTblXzAWtxMReCTk4wd3HjN79AGx31BMxIuzqGcJmMScd+Cuavk6Rz5YSpUkCvVLQv4duUz27DvQx6A9RZbewoMGBtf8NbjuYmzf9RB2Km//wMrOqHfiCkaIO/GKd/sUzXWM6/WtXNU/tCT2e0TL4U1XOBSHSkKtsYzaCyPiR/KffOIzVbEIBXn+YZg6x31SPSSmaXHYAgkEXvV07aMr6d5N/UwkgspH5iA/Qe2yzsPv78VLJMbOjAyvZmmJZJh0oyTblXjEXtCp7Gc56eVNVcnrQz+KevfgsNg5V1BAZDhIIGcypcSQc+JPBE1HuTIVugJjcYcss3ti7H38wTKXNjD8Y5o29+3nRUz3yph/bV8/lsI5s2JyJ+A6FSSmC4QidvcO8ut8z5mLY3tE35ddo7+jjpge2ccXdz3HTA9syeq54sU5/vdPGwQEf9U5bZhftbEzd7N+jsxPxyqUgRJFoa63jlkuO456PncgtlxyX9u97b+8QVQ499uULRjCbdPGVYNhgb6QJlxoiFBktNuEyDdNplFaYl+x/bvz/eyrXkvifV0y6mY/4/9WNlnOpNg1z/AxFo9MKvgE9xXIq62rLVDZ+5mJia1c14/EFcQ8HiRgG7uFgZgOFIjWxWS/DPWNnvXRuSvkpDlXo9+kTd77IwbqZWMwmIgYYGEn7AFO59glRSiTQKwEbwm/BpYZxqSEwDFxqCJca5r6h03L2pj++Q7Gr20uFzUwgHMnKxTA2itbrDWQtcIx/7qxOw8jG1M3aeTo7Ea9cCkKUibn1lQz6QoDeMD0cMaKbpCv+YDqLKoaoNen/QUd4EEvQw47ZFxe41amb6H9uKgFE/M8rZiqZj1iA/tVPfZijL/sGdQ2zwb0fKhqm1dTmVGTrZy6Sy/pAoUhN/KwXZdJHR7U+n6LuOfMBaO7Zx5tzjqKmwkad08bCRmfS358MnohyJ1M3S8Az/vkEZ13JmoGHqffvp9c+mxcaLuPVriaOn2DD5kyMn74y6A9hNimc9tE/mUwuhrmagpSTaRgeT+YZvTIrCFGO4qcRLpnp4sW9/SigusLG9tBibvNdypVVz1ATPMRB8ywerbqEd58xhf3LCmSi/7mpTFebbF3klAZcymSKZa4U8/Yp5aRYC1qVtSxsg2I7/TT+Zc6fGej30GmrwWE1MbeugkUzRtfnj78uOawmBnPUjxKiGEigVwLm1lfymncx+5qXjZxzDwdZ0RKZcMPmTIzvUNjMJryBEEtnjWa2MrkY5mrBe9YDyFBI71GVaaBXRgUhylX8Oj+vP8SaeXV09g3h9YdpdNk57YS38ozvXH5d6AX7UzTR/9w15x2dNIBIFrBNtC6ykOteiqawQg4U8/YpQmQkC9ugrF3VzG09XhpmzmBegvX5ia5LBwZ8emNUKmXwRJQlCfRKQLJR3GvPXwyQkzf98R2KpbNdHBjwYbOYiRhGxhfDrCx4T7Bwe29veKQz2+3xsatniEFfCJNiah2+wUF9zHTqJki2ogSMGcnv3ATtvxn9+zouN9th5MtE/3PJAghgwoAtWeajUEVDpkNhBck2ibKUhVkvkw2EJL4uVRKORKh32mTwRJQlCfRKwGQXr1xdkMZ3KMaPlGdyMcx4ClKS7QpOtl/OVt9RBEJhtna5sZpNWM0Kk1JT6/C53fqYjX30ROkok+0w4v9nHVYTB9w+ko1cJwogbnpg25QCtkKVqJeqlEKUqCzNeploICTZdenggI9bLjluyk0XophJoFciimEUN5ttyHgKUpLtCtaGn+Qf3nns6RnCYlYoBYGQwYqWaqxmU/odvligl42MnigdZbAdxvjs1qAvBAaEIxEODvhS+p+basBWqBL1sgeaECUsx7NeZOuM3CnnKfOlTgK9aaTY/hEzChyTLNxucu/n2vMX86mfbyZiGLgcVo6Z5aKhyk7EMNLv8HmilTIloze9ZKEwQKElm6ZU77SlPHo91Y5RoYqGSEdOCJGMFDPKjekwZb6UyfYK00QutzMoiAm2K2hrrePcpTNZPa+eNfPraajSG1yn1OELh8d+LVM3p6fo31f3oJ/nd/fx2OuH2bqzk8OWWYVuWcqyUTY87T3FOjfBQ9fR9uRVfMt5H8t5I68l6mUPNCFEMrJ1Rm7IXoTFTTJ600TZrV2ZZOF2bOSudfhVzhj6KzW+fexXM5h79pXJn/PGG+HWW+Gss2D9epg9Gzo79W3td8LO20aKvpTK9L1sKraMcE61rcP955t5oxdCFhf15mFMAQ/f7T2Bd3f0lcT3PVl2K5XfZ1pTrMeta2zye7g6+EuufusN0JKf9S9SlVIIMZFiWAZTbmTKfHGTQG+aKLt/xEkWbre11nHDyiFMT/yMnrCDIccsVteEqXv1DphVnThQe/FFfXzmGfjUp+C66+AH/wsznNBgQEWRF+VIUIU0W22cdlMzWlazvvJDLB18iFnhg/RaZrO58XK6TUeXzODIRNOU0vl9ptwxKpJ1jdKRE0KI/JEp88VNAr1poiz/ESdZuL3kwEPQOoej4/fl8Q0k73h6PHDmmXD22XDzzfDAA/r8B1ZDZa3+vFiLcuS4SmTZZYRT8Ix/Prta/xWTUiPnqqayzrNAJspuTbWa5oTKYF2jEEKI9Mjax+Imgd40MS3/EdPteHo80NICX/kKnH++3ij9sVtg9bLUn6NQcpxNSTUjXE7TO8thcCRZdisnGf4sbHgshBCitMiU+eI2YTEWpdQJSimzUqpSKfWWfDVKZN+0XIQ8QcGWhAYH9TYKSsGpp8J558EpayA8lPpzFEr/Hh2AxstiQDq3vlKX548zPugpt4I/5VzYI5XfZ9ra1ul1sr4BMCL66HPr80IIIcpWW2sdt1xyHPd87ERuueS48u5blpjJqm5+HnACs4Gv5745Ipem3T9iuh1PjweqqjJ7jkJJN6hNUypBT7lV3irnwZGcBLGxdbMVDeDer4/FuJZVCCGEmCYSTt1USr0LuAQ4Gfg+4AAWK6XuBn4PPAL8KHr3TxqG4c59U4VIU3zBlh88BO2H4Fv/kbzj6fEcuTH6JEVfisYkVUgzfvoUpmaUXcEfyrewR86m2uR4w2MhhBBCpC7ZGr12oB+4J+7cHdHjHuAW4O/Rr/8D+HT2myZEFsQ6np97APb1w+ZdcHmC+4XDek3e+EAv/jmKWR4C0smCnnJY0zadlGsQK4QQQggtYaBnGMYedEA3QilVB7zVMIwnlFKnGobx+ej553PfTCEy1BddJ7Z795G3GQbcER3HSBTolYpYUBcL9trXjz0/TrYLp0zLgj9CCCGEEEUq6Ro9pdRXYh/RUyHgo9HPjbi7RnLVOCGyIhyG/n79+a9/De9979jb//Qn+Pzn9eelHOjFtlgY7hm7xULnpiPumovCKeW8pk0IIYQQotRMtL3CFcC/A18BvmYYhkcpFaubHR/ohXPUNiGyo69PZ+1ifvMbCAbBGp1iGAiM3ja+GEspSWOLhVztiyfTAYUQQgghisNEgV63YRg/U0rFr78byQAqpcyAin6IIlBOe5hlVU+PPi5dCq++qj8fGIDGxiPvW8oZvTT2DSzHwilCCCGEEGLUVDdMfxD4Ljqz9/vsNUdMVWwqnsthHTMV79rzF9Nm2jm2SEfbOpi1Er73PV1pUim4/HJYtKjQ30ZuxAK9+MDO7R792h1XNLaUA700NqyWwilCCCGEEOVtokBvJFOnlPpw9OtYr/C/gWuin9+ek5aJtCSbivfMUxtpi/xST+mLX7dleytcc83oE+zfD9/9bgFangexQO/LX4YPfAB6e3VGL6ZcAr00tliQwilCCCGEEOVtokDvzrhjrPf3EwDDMCLAbTlsl0hTsql4R+/6PcxLsG7r0V/r4+7dcM45OvgpV7FAb/FiXYzl3HPHBnrxn5fyGr00tljI2T5qIm9kqrYQQpQPuaaLXEga6BmG8eP4oyhuiabi7e0ZwuXbx2O7Z1Ll6GNBYyWNVXad7dm5Rwc1ra1QWzs22Ck33d362NAwus1Csoye3Z6/duVCGnv+SeGU0jXhVG35nQohREmRa7rIlbTW6CmllgAdhmEM56g9pe0nP4F//rMgL33NoJ+tXQNYzSasFhNeX4j+4SDN5g4cpp0EDQudy1vg3GNotPigO6QzXErpQC+2/UA5MQz44hfh4YfBYoHqaqiJZjQTBXqf/KQOfIUocrmqmiqEECL/5JouciXdYizfAG4GtmS/KWVg61YdVBRAA3BqOMJwMEw4YhCOGJiVwqbC2CNeTP4wTZt30911EKpMsMcNbz1VP7imBg4eLEi7c2rfPvif/4GmJr13nlKjgV40uGvv6MP0SgczZrTwvbd+irWdA6MX1c5NRxaxSTFbJkQuSdVUIYQoH3JNF7mSNNCLbqvwW3QRls8ZhnF99HOUUjbga+jN0r9qGEYwD20tft/+tv4oEFv0A+CKu59jZo0Dk1K0eF/hpD0bWHHbH5j58GujDzjrLH0s14zeUPQCefvtuggL6KwewMDAyFSJL3g8hKpcR1YqfezWI4vYnH2DBHui4KRqqhBClA+5potcMU1w28eBk6MfZ4277auMbpT+79lvlsjU3PpKBn0hADqdx7Lh2Ju49jtP8dVfb9YbhAeD8IlP6DvX1JTnGr1YoFdRMXrObtcfAwMjUyWq/F78FVVUV1hxOaxs2Nw1dvNxZdJHR7U+L0SBrV3VjMcXxD0cJGIYuIeDeHxB1q5qLnTThBBCpEmu6SJXJgr0EjHQWb2zDcO4AbgJODfrrRIZS3TRcAfCvPvE+WC16jVrMbW1MDgIoVChmpsbsUCvctyIWDSw3ds7RJXDgmPIi7/SCcRNlejfo4vWxEuy+bgQWdW5CR66Du6/VB87Nx1xl1jV1HqnjYMDPuqdNlm0L4QQJUqu6SJXUl6jp5T6LBDbUTsCYBhGWCll5KJhIjNplc+PX7dWX5/fhubSJIFebKqEfXgQf4XeVmFkqoQl9c3HhciG9o4+nnlqI6fu/RHKUc3sGU00TjBlWKqmCiFE+ZBrusiFdIqxqLjP44O7SJbaIrIs5YtGba0+9veXV6A3HC0OGz91E3Sg98gjXL+ni719Q1T3HGK40jkyVeKqMxaAKfXNx4XIVGy96MfdD2HYq3FHKjnc5WFFSw2NDvSUYVkbKoQQQog0pDx10zCM7wA7o19aAJRSCjDnoF0inxJtOVAOkmX0PvpRWLYMl81Ea62DjqXH8/Sy08dOlYhtPl7RAO79+iiFWESOxNaLzgofxGdyYrWYsJlN7OoekinDQgghhJiSiTJ6dcAZ6ExefJrHAJ5QSt0cve2xnLVO5Ed8Rq/EtXf0jUxXvbh9O++BIwO9T31KfwBV0Y/rEz1ZGpuPC5GJWGntXvtsKkMD+MxVWC0mBv0hmTIshBBCiCmZKKN3HzAIeIBfjLvtq4ADsANfyU3TRN7EAr13vQsaG+H73y9oc6YqNv2t1xtgZo2DgMcLwLbeQIFbJsTEYlVyN9e+nYrwII7wIMFgmEbLsJ4y3Lau0E0UQgghRIlJmtEzDOOmCW7zA1/KSYtE/i1fDjfdBL29cP/98PTTIxmvbIvPuM2tr2TtquasLT6OTX+L7UPjim7v+MDrfRwniTlRxNauaua2jdt5xXE0kRkfZ0XPn6gJ7mNW8zFw+kclsyyEEEKItKVTjAXgRmA3gFJqhWEYW7LeIpF/Fgt87Wv686eeGl3blmWxjJvLYdXT1OI3KB8X7E0lIIxNf4ux+X0A7BqUekGiuMVXyd3Uu5BDC65n7apmmqQCmxBCCCGmKK1AzzCMl+O+vAdYldXWiMJzOsHrzclTj8+4xY4bNneNCeImCwiTBYGx7RJiz2sJ+AiZLcyZUZ2T70eIbJLS2kIIIYTIpgkDPaXUX6OfhgE/0A88ZxjG9xm73YIoFzkM9MZn3CBug/I4EwWEQNIgMDb9Lfa8hncIv83B2lXNOfl+hBBCCCGEKFaTZfRagHeji7a4oh+e6G2yUXo5cjrh0KGcPPX4jBvEbVAeJ1lAaNm3Gf/eb/LF4S7cFc1srn07nc5j6fMGuH7DFubUVFBpMxOORDg44MMVCWCrckqWRExJLteTCiGEEELk2mT76PkMw3jdMIxXgUNAHxBSSq0GKiZ+qChJlZU5y+itXdWMxxfEPRwkYhgjG5SPz7jFKhDGq+/byrrh+zGGexm0z6QyNMD5B++i6nA7b3YP0hettGk2mRgKhLnmvKM5YWYFVpczJ9+LKG/jK7jGMsftHX2FbpoQQgghRErSWaP3LuD0uK9nZrktohjkcOpmfMGJWJbkqjMWjGZJOjdB+3r+9fAbPN9fxaa6C+mtW86gL8S5fX+icUYj/W4L/mAYLFUAHNf9Rx4yXUV1pRWTUmPX/Q0NHbmHnhApSHU9qRBCCCFEsUo50DMM4zvAd2JfK6VezEmLRGE5nTmrugkTFJzo3ASP3QqOampmtrLa1sO87p+xPvBBbHNWcULtIDV1rSywBdnSOaAfY3bSGOwkYjVY0DiauRtZ9zc0BBVlkHiOBsD079EbZ7etk3L7OZbqelIhEpFpv0IIIYrBZFM3RyilTlBKvSf2AUgpw3IUy+gZeV6C2b4eHNXgqAFloq6+iaNb5/DV1pe45ZLjqJl9FPg9NFbZWdFSg91qxvC76bHN4qgZVTRU2UeeamTdXzlk9GIB8HAPVM/Wx8du1edFziSaPpxoPakQ48m0XyGEEMUi5UAPWAScEPchPZ5y5HRCOAyBQH5ft38P2F1jz9ld+jzoLJbPDb4BGp1WTphl4uTZFpa89V+wRUKcd9d/8Z7vfYVjNj4wuu5veLj0A71xATCOGv11+/pCt6yspbqeVIjx4qf9xqaTuxzWkarBQgghRL5MNnVzpPyiYRi/BH4Z+1op9bZcNUoUkDM6BdLrBbt94vtmU+08na1y1Iye83v0edBTFc++YewUxlM+w5KW1XzpwKMs+fPPiSgTR219jpX/9mk9TaocMnr9e3QmL158ACxyYtL1pEIkIdN+hRBCFIsJAz3DMC6Y4GbZR68cxQd69fX5e922dXpKIuhAxu/RGbxTPjN6n5bVCdemLanQ00xNJ55AXXs7dXNr9Q19fVBd4jOMJwuARc7IBuZiKlLdRkYIIYTItXSmbo53ZdZaIYpHLAOWo8qbScUydhUN4N6vj2ffkFrREbdbHxcvBr8f3G7adx4mcuAAvz8MNz2wrXTXx8RNWcWI6KPPrc8LIYqOTPsVQghRLNLZXmEMwzCkGkQ5is/o5VuSjN2k4gM94NXNr/PTTQf5TiRCaPackWII156/uPQyNEmmrErVTSGKk0z7FUIIUSymHOiJMhUL9HK4xULWjQv0/vH3l2mOmAHwNMws/T3QphoAi4KQ0vpCpv0KIYQoBhLoTXPjO6UfMsIshsJk9KYqFugdfTQAvq79zHLp/fPcDTMAKYYg8iNWWt/lsI4prV+S2WQhhBBClLRM1uiJEpdov6efvXRY35gs0LvnHr2Oz+HQH0uXQiiU+L754nbrtsydC8D8oAfHwf36pvqZgBRDEPkhpfWFEEIIUSwk0JvGEnVKzTXR6o7vf78O6Fwu+PWvRx90110wYwZccw2ceSa89hr09BSk/SMGBnR1zfp6sFg4fU87R7f/g5DZgsdVK8UQRN7s7R2iyjF2ooRkk4UQQghRCDJ1cxpLtN+Tf24rv3zfZ7l8XnQPvQcfhOuug5df1hup/+Mf8JWvwM03w/33w1//qgOtmTPz/w0AnHUWPPEEHHUUmEywdCk1jzzM8cD++Us44AlIMQSRN1JaXwghhBDFQgK9aSxhp9Qf5uX3XwWXHKdPXHCB/vj3f9dfV1bCZZfpz2PZv4GBPLZ6nCeeGNuWZ56B7m4AZjc1cU9FRYEaJqajtauauW3jdkBn8gZ9ITy+IFedsaDALRNCCCHEdCOB3jSWUqf0nHMgEEj8BLW1+tjfn9N2piS2TrCyElpbC9sWURamUj1TSusLIYQQolhIoDeNZdwpLYaMXszBg4VugSgjmVTPlNL6QgghhCgGEuhNcxl1SiXQE2UqvlARUPp7MQohhBBi2pGqm2LqimHqpj1aNMbhmPh+QqRBqmcKIYQQotRJRk9MncsFShUuo2cYev3grFnw8MOFaYMoH52boH099O/hk94qHo+cT3/9ipGbpXqmEEIIIUqJZPTE1JlMOtgrVKA3NKSDvWuvhZUrC9MGUR46N8Fjt8JwD1TPZnF1gLcf/gm1vVuIGIbsxSiEEEKIkpOXQE8pdYdS6hGl1HNKqfuUUhXjbp+rlPqLUuoZpdTTSqm2fLRLZEFtbeGmbno8+lhVVZjXF+WjfT04qsFRA8pEXX0Tc+fM4qzhjRwc8FHvtKVUiEUIIYQQoljka+rmjYZhDAAope4F3gf8LO727wN3GIbxkFJqNbAeODZPbROZqKkpXEYvFui5XFN6+FTK54sy1b8HqmePfu3tps79BicNd3PSggZen3UR92+Gbz+yQ/5WhBBCCFES8pLRiwvyqoHZwEux25RSdcBSwzAeit53E+BVSh2Tj7aJDNXUwJ498Le/wdat+X3twUF9nEJGL1Y+v9cbGFM+v72jL8uNFCWhdh74owMH3m7YtxmCg1DZQF/Pfvwbb6Gq+yX5WxFCiAJr7+jjpge2ccXdz3HTA9vkWizEBPI1dfMcpdSzwB7gL4ZhtMfd3ArsHveQPcARi2GUUp9QSr2glHrh8OHDuWquSEdzM7S3w3nnQVsb9Pbq84ahM32xYCwXMsjoxZfPNylFdYUVl8PKhs1dWW6kKAlt68DnBt8AHN4BKP033HA0b7gtBC0u3jK8Uf5WCkQ6dkIIkEFaIdKVr4zeo4ZhnATMA1Yppa6Ju9kOhMY9JAxEEjzPnYZhrDEMY01TU1PO2ivS8KMfwZNPwk03QSQC3d36/Ne/rtfvuVzwwAO5ee0MAj0pny/GaFkNZ98AFQ0w3A02J8w5HqqaGPSHCNlc1Pv3j9w9l38rEtSMJR07IUSMDNIKkZ68Vt00DMMN/AA4N+50JzqrF28eR2b5RDGqqYEzzoATTtBfu936+PLLUF+vP9++PTevncHUzbn1lQz6xo4vSPn8aa5lNVz0LTj23TBzGVTpwaQquwVLwEOvfXQNX67+ViSoOZJ07IQQMTJIK0R6ch7oKaUalFKzop8r4BLgqdjthmHsA/qVUmdF77MKCBiGsSvXbRNZVF2tj7HCLP39sGiR3oIhlnnLtgwyemtXNePxBXEPB6V8vhgrfhqnEeGo6hDWkIcnK87P+d+KBDVHko6dECJGBmmFSE8+MnpVwINKqX8ATwLDwLeUUh9TSr0jep8PA1+N3uebwJV5aJfIppoafYxl9Pr7oa5OZ9syCPQmnMaWQUavrbWOa89fTL3TJuXzxVjx0zjd+6lrmI39/JsYbFyZ878VCWqOJB07IUSMDNIKkZ6cb69gGMYe4KQEN90dd583gLNz3RaRQ4kyevPm6WzbFAO92DQ2l8M6ZhrbSCd73z6w2UZfO01trXUS2InEWlbrj6glwC1rcv+yc+sr6fUGqK6wjpyb7kHN2lXN3LZRT/+uclgY9IXw+IJcdcaCArdMCJFvsUHa+K2RrjpjgbyXC5FEvvbRE+UuUUavtjajjF78NDZg5Lhhc5e+qG/dCkuXgtmcYeOFKA4S1BxJOnZCiHgySCtE6iTQE9mRKKMXq7o5xS0W9vYOMbPGMebcmGlsW7fC2ZIIFuVDgprEpGMnhBBCpE8CPZEdVitUVOiMns8Hfv9ooDfFjN6E09i2bYOuLjjuuCx9A5Nr7+gb0wFfu6pZOp8i6ySoEUIIIUQ25HV7BVHmqqt1Rq+/X3+dYaCXdNH18XNgxQp9p1WrstL0yUjZ++lJ9rQTQgghRKmSQE9kT02NzuhlKdBLWhmzwQaGARddBOeeO/kTZYGUvZ9+JLgXQgghRCmTqZsie7Kc0YMk09j27dPHd75T79OXB5OuFxRlZ9JiQEIIIYQQRUwyeiJ7spzRSypW2XOK2ypMhezlNf3InnZCCCGEKGUS6Insqa6GTZvgE5/QX9fU6EDP54NQaOLHpiNW2TOPgZ5s0jr9SHAvhBBCiFImgZ7Inn/5F71u7sQT4eqrYfFiHehBdrN6BcjoJV0vKFP4ypYE90IIIYQoZbJGT2TP296mP+LFB3p1WQqKYoFebJP2PJGy99NLqe1pJ9t/CCGEECKeBHoit2JZt2OPhWuuga9/PfPnLEBGT0xPpRLcxyqEuhzWMRVCJesshBBCTF8ydVPk1vnnw7/9G8yYARs3Zuc5C7BGT4hiJtt/CCGEEGI8CfREbtXWwn/8B5x9NnR2Zuc5Yxm92LRQIaY5qRAqhBBCiPEk0BP50dIC+/dDMJj5c7nd4HSCRWYeCwFSIVQIIYQQR5KessiPlhYwDB3stbZOfN/+fvjRj8DvB6sVrrwSmppGb3e7ZdqmEHHWrmrmto3bAZ3JG/SF8PiCXHXGggK3TAghhBCFIoGeyI+WFn3s7Jw80Pvd7+BLXxr9uqoKPvOZ0a8HBiTQEyJOqVUIFUIIIUTuSaAn8iMW6O3Zo/fZg+RTL/v79fHQIV3Epbd39LadO+FXv4ITTshZU4UoRaVSIVQIIYQQ+SFr9IpZ5yZ46Dq4/1J97NxU6BZN3dy5+rhunZ6OabXCPfckvm+s2Ep9vS64Egv8AO66Sx9POilXLRVCCCGEEKLkSUavWHVugsduBUc1VM+G4R799dk3QMvqQrcufbW1cP/9sGuX/vprX4OXX058X7dbT9c0m/Xj+vpGb+vr0+v17rgj1y0WImtkM3MhhBBC5JsEesWqfb0O8hw1+uvYsX19aQZ6AB/4wOjn3/kODCUp/R5fbKWubmxGb2AAampy1kQhsk02MxdCCCFEIUigV6z69+hMXjy7S58vB5WVqQV6tbVlF+hJdmd6id/MHBg5btjcJb93IYQQQuSMrNErVrXzwO8Ze87v0efLQTqBXvzUzRLfWiGW3en1BsZkd9o7+iZ/sChJspm5EEIIIQpBAr1i1bYOfG7wDYAR0UefW58vB6kGemU2dTM+u2NSiuoKKy6HlQ2buwrdNDGJ9o4+bnpgG1fc/Rw3PbAt5eBcNjMXQgghRCFIoFesWlbrwisVDeDer4+lWoglkalm9Eo80JPsTmnKJBO7dlUzHl8Q93CQiGHgHg7i8QVZu6o5Dy0XQgghxHQla/SKWcvq8gnsxqus1EFbIuMDPY8HQiG9716JT92cW19Jrzcwsk4LJLtTCjJZZyebmQshhBCiECTQE4XhdML+/YlvGz91M3aurk4fSzijt3ZVM7dt3A7oTN6gL4THF+SqMxYUuGViInt7h5hZ4xhzLp1MrGxmLoQQQoh8k6mbojCSTd00jCMzegBf+AL88Y8QiZR0oBfL7tQ7bRwc8FHvtEmZ/RIg6+yEEEIIUWokoycKI1mgNzSkg7lYoNfWBjNnwr33wp//rM+V8NRNkOxOKSqKTGznJr2PZv8eXX23bV35Tu0WQgghRMYkoycKY3ygt3s3fOITcOWV+utYMLdyJRw4oDN6sameJZzRE8UtWWXNgmdiOzfBY7fCcI/eX3O4R3/duSk/ry+EEEKIkiMZPZEbk2Ufxgd6v/oV/PjHMHcuHH00nHDC2Oc77bTRzyXQEzkQq6zpcljHVNaMBXQFzcS2rwdHNTiif/uxY/t6yeoJIYQQIiHJ6InsSyX7UFkJwaD+AJ2tczqhowO2b4dVq8Y+5ymnjH4+a1buvwcx7RT1Hof9e8DuGnvO7tLnhRBCCCESkEBPZF989kGZ9NFRrc/HVEaLWMSyevv3TxzA1dfDtm3w7LN6OqcQWVbUexzWzgO/Z+w5v0efF0IIIYRIQAI9kX2pZB+cTn2MBXoHDsDs2RM/77JlcOKJoFT22ipEVFFX1mxbBz43+AbAiOijz63PCyGEEEIkIIGeyL5Usg+JMnqTBXpC5NDaVc14fEHcw0EihoF7OIjHF2TtquZCN02vwzv7BqhoAPd+fTz7BlmfJ4QQQoikpBiLyL62dXpNHuhMnt+jsw+nfGb0PokCvQsuyG87hYgTq6y5YXMXe3uHmFtfyVVnLCierTBaVktgJ4QQQoiUSaAnsi+WfYivunnKZ46sugk60PN6weORjJ4oONnjUAghhBDlQgI9kRuTZR9igd6VV0JFhf5cqmkKIYQQQgiRFRLoicJoa4N3vxvcbv31RRfBOecUtElCCCGEEEKUCwn0RGHU1MBvf1voVvz/9u4+yK67POz495G10uplV9Ji1TirlzgxDjBJZlktLZB4ihLcDCbx0IqQidNSZwYzNMEk8aQDxU1j6pi2Q2qSZkqwSYN5qaEGpSY1HRyZsSehYGxJ2TBAHAUDegNcmZW8K2mvd1f79I9zVr272pV2V3tf9uz3M3Pm3vs7v3vvT6Nn7p7n/N4kSZKkSnLVTUmSJEmqGBM9SZIkSaoYh25KkqS2NXjk5LRtT/b097o6riTNg4meJOmivNBWqwweOck9+w7R1dnBVZs6GTozxj37DnH7DdcZg5J0CQ7dlCTNaepCe+jM2LQL7cEjJ1vdNK0Aew8ep6uzg+51HayKoHtdB12dHew9eLzVTZOktmeiJ0makxfaaqWjQ2fZ2Dl98NHGztUcHTrbohZJ0vLh0E0tOYd5SdVxdOgsV23qnFbmhbaaZXvPeobOjNG9ruN82enaBNt71rewVZK0PNijpyXlMC+pWrb3rOd0bWJamRfaapY9/b2M1MYZHh1nMpPh0XFGauPs6e9tddMkqe2Z6GlJOcxLqhYvtNVKfTu2cPsN19GzYQ3PPl+jZ8MaF2LRnAaPnOR3Hvoat/zpk/zOQ1/zJrNWPIduakk5zEuqlqkL7frh2Ldef40X2mqavh1bjDddkiu0Shcy0dOSarf5FM4XlC6fF9qS2l39iCLg/OPeg8f9/dKKZaKnJbWnv5d79h0Cip6807UJRmrj3Hr9NU1vy4NPHeEDj/49E+cm6V7XwdjEpHf3pBbwhoukRnNEkXQh5+hpSbXLfIrBIyf5wKN/D0BXZwdjE8m3njvDxLl0vqDURC7QJKkZXDhKupA9elpy7TDMa+/B40ycm6RrXQdBsGZ1APDsSI01q72/ITWLw6kkNUM7jSiS2oVXvKqko0Nn2bSug4lzeb6s44pgeHTcu3tSE7nhtaRmaJcRRVI7sUdPlbS9Zz3j5yZ55sQZAFZfEYyOn2P1FatcFl5qonZboElSdbXDiCKpndijp0ra09/LFauCH926gTVXBCOj4wD81uteMu2PgHvuSI3lPnySJLVGZOala7WhgYGB3L9/f6uboTZ2qZX+6vfcqR/P71APaWm56qYkSY0REQcyc2C2cw7dVGVdagiHi0RIzeFwKkmSms9ET9Vz7AAMPgCnDsPmndB3M2zbdUE199yRJElSVTlHT9Vy7AA8djeM/gC6ry4eH7u7KJ/BPXckSZJUVSZ6qpbBB6CzGzo3QawqHju7i/IZXCRCkiRJVWWip2o5dRjWdk0vW9tVlM/gnjuSJEmqKufoqVo27yyGa3Zu+v9lL4wU5bNwkQhJzeYqpJKkZrBHT9XSdzPUhqH2PORk8VgbLsolqcWmtnUZOjPGVZs6GTozxj37DrmHpyRpydmjp7awZHe4t+2C3XdMX3Xz1bfNuuqmdCn2vGipua2LJKlZTPTUcvUbl9ff4V70fLltu0zsdNmWPC4l3NZFktQ8Dt1Uy9Xf4V4VQfe6Dro6O9h78Hirm6YVzLhUI7itiySpWUz01HJHh86ysXN657J3uNVqxqUawW1dJEnNYqKnlvMOt9qRcalGcFsXSVKzOEdPLbenv5d79h0Cih6T07UJRmrj3Hr9NS1umVYy41KN4rYukqRmsEdPLecdbrUj41KSJC1n9uipLXiHW+3IuJQkScuViZ7annuZSZIkSQvj0E21tam9zIbOjE3by2zwyMlWN02SJElqWyZ6amvuZSZJkiQtnIme2pp7mUmSJEkLZ6KntuZeZpIkSdLCNSXRi4j3RsQXIuJLEbE3IjbNOP/miDgSEY+Xx+81o11qf3v6exmpjTM8Os5kJsOj44zUxtnT39vqpkmSJEltq1k9ek9n5s9m5muAbwDvmXG+B/hwZr62PP5tk9qlNudeZpIkSdLCNWV7hcz8ZN3Lp4A3zajSAzzXjLZo+Zi5rcJvvu4lJniSJEnSPDR1jl5EdADvBB6ccaoTuDUivhwRH4uIHXO8/20RsT8i9p84caLRzVULua2CJEmStHhNS/Qi4lpgH/DpzHy4/lxm/rvM7AdeU9b5s9k+IzPvy8yBzBzYunVrw9us1nFbBUmSJGnxmrUYy03AR4Ffz8x756qXhY8DO8veP61QbqsgSZIkLV7DE72IuAp4P3BjZn59jjpb656/HvhWZo43um1qX26rIEmSJC1eMxZjeQVwJfDZiJgqGwIeBp7NzM8Bd0bEK4FR4CTwS01ol9rYnv5e7tl3CCh68k7XJhipjXPr9ddcUHfmoi17+ntdtEWSJEkrWmRmq9uwKAMDA7l///5WN0MNNJ8EbmrRlq7OjmkJoVswSJIkqeoi4kBmDsx2rinbK0iL0bdjyyWTtfpFW4Dzj3sPHjfRkyRJ0orV1O0VpKXmoi2SJEnShezR0zTLbb7b9p71DJ0ZO9+TBy7aIkmSJNmjp/OW4yble/p7GamNMzw6zmQmw6PjjNTG2dPf2+qmSZIkSS1joqfzluMm5X07tnD7DdfRs2ENzz5fo2fDGhdikSRJ0orn0E2dd3ToLFdt6pxWthzmu81n0RZJkiRpJbFHT+e5SbkkSZJUDSZ6Os/5bpIkSVI1mOjpPOe7SZIkSdXgHD1N43w3SZIkafmzR0+SJEmSKsZET5IkSZIqxkRPkiRJkirGRE+SJEmSKsZET5IkSZIqxlU31XrHDsDgA3DqMGzeCX03w7ZdrW6VJEmStGzZo6fWOnYAHrsbRn8A3VcXj4/dXZRLkiRJWhR79NRagw9AZzd0bipeTz0OPmCvnrRQ9o5LkqSSPXpqrVOHYW3X9LK1XUW5pPmzd1ySJNUx0VNrbd4JL4xML3thpCiXNH/1veOxqnjs7C7KJUnSimOip9bquxlqw1B7HnKyeKwNF+WS5s/ecUmSVMdET621bRfsvgPWvQiGv1c87r7DeUXSQtk7LkmS6rgYixpm8MhJ9h48ztGhs2zvWc+e/l76dmy5sOK2XSZ20uXqu7mYkwdFT94LI0Xv+Ktva227JElSS9ijp4YYPHKSe/YdYujMGFdt6mTozBj37DvE4JGTrW6aVE32jkuSpDr26Kkh9h48TldnB93rOgDOP+49eHz2Xj1Jl8/ecUmSVLJHTw1xdOgsGzun30fY2Lmao0NnW9QiSZIkaeUw0VNDbO9Zz+naxLSy07UJtvesb1GLJEmSpJXDRE8Nsae/l5HaOMOj40xmMjw6zkhtnD39va1umiRJklR5ztFTQ/Tt2MLtN1w3bdXNW6+/5qLz8+a9SqckSZKkizLRU8P07dgy70RtapXOrs6Oaat03n7DdSZ7kiRJ0gI5dFNtoX6VzlURdK/roKuzg70Hj7e6aZIkSdKyY6KntuAqnZIkSdLSMdFTW3CVTkmSJGnpmOipLbhKpyRJkrR0TPTUFqZW6ezZsIZnn6/Rs2GNC7FIkiRJi+Sqm2obC1mlU5IkSdLc7NGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIox0ZMkSZKkijHRkyRJkqSKMdGTJEmSpIpZ3eoGSPNy7AAMPgCnDsPmndB3M2zb1epWSZIkSW3JHj21v2MH4LG7YfQH0H118fjY3UW5JEmSpAuY6Kn9DT4And3QuQliVfHY2V2US5IkSbqAiZ7a36nDsLZretnarqJckiRJ0gVM9NT+Nu+EF0aml70wUpRLkiRJuoCJntpf381QG4ba85CTxWNtuCiXJEmSdAETPbW/bbtg9x2w7kUw/L3icfcdrropSZIkzcHtFbQ8bNtlYidJkiTNkz16kiRJklQxTUn0IuK9EfGFiPhSROyNiE0zzm+PiEci4omI+GJE9DWjXZIkSZJURc3q0Xs6M382M18DfAN4z4zzHwT+KDNfBfwG4AZpkiRJkrRITUn0MvOTdS+fAq6eehERW4CXZebDZd0DwJmIeGkz2iZJkiRJVdPUOXoR0QG8E3iwrngH8J0ZVQ8DvbO8/20RsT8i9p84caJh7ZQkSZKk5axpiV5EXAvsAz491XtXWgtMzKh+Dpic+RmZeV9mDmTmwNatWxvXWEmSJElaxpqyvUJE3AS8C3hbZn59xuljFL169XZyYS+fJEmSJGkeGt6jFxFXAe8HbpwlySMzvwuciojXlvX7gbHM/Haj2yZJkiRJVdSMHr1XAFcCn42IqbIh4GHg2cz8HPAW4MMR8T6gBry1Ce2SJEmSpEpqeKKXmZ8HXnSJOt8Edje6LZIkSZK0EjR11U1JkiRJUuOZ6EmSJElSxZjoSZIkSVLFmOhJkiRJUsWY6EmSJElSxURmtroNixIRJ4DDrW7HLK4Enmt1I1RZxpcazRhTIxlfaiTjS43WjjG2MzO3znZi2SZ67Soi9mfmQKvboWoyvtRoxpgayfhSIxlfarTlFmMO3ZQkSZKkijHRkyRJkqSKMdFbeve1ugGqNONLjWaMqZGMLzWS8aVGW1Yx5hw9SZIkSaoYe/QkSZIkqWJM9CRJkiSpYkz05iEifioivhQRfeXrHRGxNyL2RcRfR8Q76upuj4hHIuKJiPji1HvKc2+OiIPlZ90fEZ3N/9eo3cyMrxnnPhIRD9W9Nr60YLPFWES8rPwN+0oZTz9SlhtjWpBZ/kauioj3RsTjEfHliPiDiFhVnjO+tCBlLH2hjIu9EbEpCu+LiAMR8WRE/HZdfWNM8zZHfFXnOj8zPS5yAB8D/ifwNaCvLHslcG35fD3wt8DLy9f/C/j58vku4Bvl86uBbwCby9d/CLy71f8+j/aLr7pzu4GvAg/VlRlfHgs65vgNW1e+fmVdvak528aYx7yPOeLr9cDn62Lq08Cby+fGl8eCDuCX657fBfwnYE8ZVwGsAZ4EXlXWMcY85n3MEV+Vuc63R+/S3p6Z/xR4bqogM5/KzG+Wz89SBMCLI2IL8LLMfLg8dwA4ExEvBd4MfCozT5Ufcx/wxqb9K9SuLogvgIh4MXAn8J66MuNLizFbjN1CcQPhqamCzExjTIswW3w9C2wE1kXEWqAb+L7xpcXIzE/WvXyK4oL6nwN/lIUx4KPAG40xLdRs8VWl63wTvUso/4PnFBE/Drwc+DKwA/jOjCqHgV7gh4FvzVKuFWy2+IqIjcD/AG4DTtedMr60YHP8hv0jij9OnyuHnvyHiOjAGNMCzRZfmXkQ+ArwbYqYOZqZf4nxpctQ/ka9E3iQuePFGNOizIiv+vJlfZ1voncZIuJXgD8B3piZo8BaYGJGtXPA5Cznpsql88ofmgeB38/Mr844bXxpqbwY+DHgnwGvpfhjdBvGmJZARNwCbAW2AzuBiYj4TYwvLVJEXAvsAz5d9qbMFS/GmBZslviaKl/21/kmeotQTjS/n+Ku+M9k5tPlqWMU2X69nRTZ/8xzU+VSvd3ATwB3RcQgxQ/M7oj4DMaXls73KYZuvpCZExQ9yK/AGNPSeBNwX2aOlfH1X8sy40sLFhE3UQzN/PXMvLcsnitejDEtyGzxVaXrfBO9xfk14HRmvrN+2Epmfhc4FRGvBYiIfmAsM78N7AX+RURsKKv/K+DjTW212l5m/kVmbs/MvszsA94KPJaZbzK+tIQeAn5paiVE4A3AE8aYlsggRUxNeSPwN8aXFioirgLeD9yYmV+vO/UZimsxynmg/xL478aYFuIi8VWZ6/zVrW7AMvXTwE9ExON1ZQ9k5n3AW4APR8T7gBrFhTqZ+XcR8QfA4xExSTHh8yNNbbWqwPjSUvgs8JPAX5Xx8iTwofKcMabL9XvAByLi/5SvnwGmlic3vrQQrwCuBD4bEVNlQxQLX/x4RDwBXAHcm5mHyvPGmOZrrvgaoyLX+VNLH0uSJEmSKsKhm5IkSZJUMSZ6kiRJklQxJnqSJEmSVDEmepIkSZJUMSZ6kqTKiojbI+JFDfz8l0bE5st4/6sWWP/nI+LVi/0+SdLKYaInSVq2ImIgIgbrjmciYm9dlZuArlne95aI+GJEPFkeX4qIX11EE94N9C2y+QCfmqVt++v+PX8TESN1pweAH7uM75MkrRDuoydJWrYycz91iVZEvAuYvNh7IuKXgV8EfiEzT5ZlPcAnImIiMz8+o/71wPuAjcA54A9n1rnId+0C7p1R/HJgQ86xv1FmDtS9/yeBD0XEvcAu4IeA98znuyVJK5uJniSpEiJiI8VmtrsjYrAsvnaWqj8FfGwqyQPIzKGI+ARwPXA+iYuIbcCHgTdk5jMRsQn4TEScyMzPX6pNmXmAohdu6vO6gC/PleTN4jbgo5l5b/n+O+f5PknSCufQTUnSshcRq4GPAP8xM/9vZvZlZh+wf5bqDwHvjoifjogN5XE98K+BP5tR9zXAI5n5DEBmPg/8MfD6RTb1F4BH5lMxIm6iSBL/dJHfJUlawezRkyQtaxHxQ8AngI9k5sfL1/+7PH1Bj15mPhoRvwa8FbirLP4m8PbM/MqM6k8DvxURV2TmubLs1cDfLqKda4DfBt5UV9wVEW8Hzmbmx+rq3gK8C7ghM8cj4s8phm06dFOSNC8mepKkZSsi7gJeBfybqSQtM79LOW8vIh6f7X1l3ZlJ3Wz1vhoRDwJ/FRFPUCyE8hzFIiwLaWcAHwQ+lZnfqjs1CZwCRst6A8CdwGbgn2TmsbIdN5Xn71zI90qSVq6Y/zQBSZLaS0RcmZnPlfPz3gr8Y2ArRTL2lxTDJA+VvWIDwJ+Ub+0CAhguX2+iSLqmVri8JTMH676nG/hR4LuZ+Wxd+f3A/Zn5+EXa2Ekx/HIoM98x49x3MvOH617/HNCTmZ+c47N+BBjNzO/N9X2SJIE9epKkZSwznyufPgI8CvwG8H3gHwC/Cvw3inl201bojIh3AJ2Z+fvl63cDpzLzQ3N8zzDw1wttX0S8AfjPwAcz87/M49/zSPm+R4ErZ6nyYorexPsX2hZJ0spioidJWtYiYiewLjN/t674GHBXuaDJS4C/W8Tnbgd+F7iC4u9lB7AWWFM+3zePj/k65YqdC/nuzHzdHG26cyGfI0lauUz0JEnL3TFgfUTcmJlTi7BMDYPcDHx7kZ/7PYpEb5Ji/7xzwBjwQmaOld9x/8U+IDO/s8jvliTpspjoSZKWtcw8Vw6RfG9E/HsgKebfHQJunErKFvG5E8DxpWupJEnN42IskiQtUkSsB8bKpHAx7+8u5//Nt/4/BIYz8+nFfJ8kaeUw0ZMkSZKkilnV6gZIkiRJkpaWiZ4kSZIkVYyJniRJkiRVjImeJEmSJFWMiZ4kSZIkVYyJniRJkiRVjImeJEmSJFXM/wPHO3FySkpZ7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_d_in, test_d_in, train_d_tg, test_d_tg = train_test_split(diesel, traffic, random_state = 42)\n",
    "print(train_d_in.shape, train_d_tg.shape, test_d_in.shape, test_d_tg.shape)\n",
    "\n",
    "model = KNeighborsRegressor()\n",
    "model.n_neighbors = 10\n",
    "model.fit(train_d_in, train_d_tg)\n",
    "\n",
    "x = np.arange(1200, 2200).reshape(-1, 1)\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.scatter(train_d_in, train_d_tg, alpha=0.6)\n",
    "plt.scatter(test_d_in, test_d_tg, alpha=0.6)\n",
    "plt.plot(x, model.predict(x), \"r\")    \n",
    "plt.rc('font', family = 'Malgun Gothic')\n",
    "\n",
    "plt.title('경유의 가격에 따른 교통량')\n",
    "plt.xlabel('경유의 가격')\n",
    "plt.ylabel('교통량')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0811a9",
   "metadata": {},
   "source": [
    "# 6. 모델 평가\n",
    "\n",
    "[ 내용 ]\n",
    "- 학습이 완료된 모델을 통해 값을 예측해보고, 최종적으로 모델의 성능을 평가\n",
    "- 반드시 '테스트 데이터'를 이용하여 모델의 학습을 진행해야 함 (# 필수 조건)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3123e2",
   "metadata": {},
   "source": [
    "[ 모델 평가 ]\n",
    "- 성능 평가 결과 : 다항 회귀로 작성한 모델의 성능 평가\n",
    "  - 약 0.2의 성능으로 교통량을 예측하는 것을 알 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "3b6eb6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "a3a0aad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "휘발유 가격에 따른 교통량 예측 ( 다항회귀 )\n",
      "\n",
      "훈련 데이터로 평가한 성능 : 0.16331795644231217\n",
      "테스트 데이터로 평가한 성능 : 0.19199192520470187\n"
     ]
    }
   ],
   "source": [
    "print(\"휘발유 가격에 따른 교통량 예측 ( 다항회귀 )\\n\")\n",
    "\n",
    "score_train_lr_poly = model_lr_g_poly.score(train_poly, target_l_train)\n",
    "print(f\"훈련 데이터로 평가한 성능 : {score_train_lr_poly}\")\n",
    "\n",
    "score_test_lr_poly = model_lr_g_poly.score(test_poly, target_l_test)\n",
    "print(f\"테스트 데이터로 평가한 성능 : {score_test_lr_poly}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "b705b1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "경유 가격에 따른 교통량 예측 ( 다항회귀 )\n",
      "\n",
      "훈련 데이터로 평가한 성능 : 0.1303998765235217\n",
      "테스트 데이터로 평가한 성능 : 0.17451721228908124\n"
     ]
    }
   ],
   "source": [
    "print(\"경유 가격에 따른 교통량 예측 ( 다항회귀 )\\n\")\n",
    "\n",
    "score_train_lr_poly = model_lr_poly.score(train_d_poly, target_ld_train)\n",
    "print(f\"훈련 데이터로 평가한 성능 : {score_train_lr_poly}\")\n",
    "\n",
    "score_test_lr_poly = model_lr_poly.score(test_d_poly, target_ld_test)\n",
    "print(f\"테스트 데이터로 평가한 성능 : {score_test_lr_poly}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c1536a",
   "metadata": {},
   "source": [
    "[ 모델 평가 ]\n",
    "- 성능 평가 결과 : K-최근접 이웃 회귀로 작성한 모델의 성능 평가\n",
    "  - 약 0.35정도의 성능으로 예측하는 것을 알 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "0fb245f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "휘발유 가격에 따른 교통량 예측 ( K-최근접 이웃 회귀 )\n",
      "\n",
      "훈련 데이터로 평가한 성능 : 0.3297059303551336\n",
      "테스트 데이터로 평가한 성능 : 0.3540039838484954\n"
     ]
    }
   ],
   "source": [
    "print(\"휘발유 가격에 따른 교통량 예측 ( K-최근접 이웃 회귀 )\\n\")\n",
    "\n",
    "score_train = model_g.score(train_g_in, train_g_tg)\n",
    "print(f\"훈련 데이터로 평가한 성능 : {score_train}\") \n",
    "\n",
    "score_target = model_g.score(test_g_in, test_g_tg)\n",
    "print(f\"테스트 데이터로 평가한 성능 : {score_target}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "67cb099e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "경유 가격에 따른 교통량 예측 ( K-최근접 이웃 회귀 )\n",
      "\n",
      "훈련 데이터로 평가한 성능 : 0.357071822291531\n",
      "테스트 데이터로 평가한 성능 : 0.3194190148060877\n"
     ]
    }
   ],
   "source": [
    "print(\"경유 가격에 따른 교통량 예측 ( K-최근접 이웃 회귀 )\\n\")\n",
    "\n",
    "score_train = model.score(train_d_in, train_d_tg)\n",
    "print(f\"훈련 데이터로 평가한 성능 : {score_train}\") \n",
    "\n",
    "score_target = model.score(test_d_in, test_d_tg)\n",
    "print(f\"테스트 데이터로 평가한 성능 : {score_target}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1311ada8",
   "metadata": {},
   "source": [
    "# 프로젝트 마무리\n",
    "\n",
    "[ 결론 ]\n",
    "- 진행한 프로젝트 정리"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36a1d57",
   "metadata": {},
   "source": [
    "- 요약 (# 필수 조건) : 유가 변동에 따라 어느 정도의 교통량 변화가 있긴 하지만, 여러 상황에 따라 달라지기 때문에 모델이 잘 예측하지는 못하는 것을 알 수 있다. \n",
    "\n",
    "\n",
    "- 의의 (# 필수 조건) : 유가 변동에 따른 교통량 변화를 다항 회귀 및 K-최근접 이웃 회귀를 통해 예측할 때 평가한 성능이 0.1~0.3으로 높은 성능에 해당하는 0.5 이상에는 못미치는 것을 알 수 있다. 따라서, '교통량'은 많은 요소들에 의한 작용으로 이루어 지기 때문에 유가변동이라는 한 요소로 예측하는 것이 정확한 예측에 도달하지는 못한다는 것을 알 수 있다.\n",
    "\n",
    "\n",
    "- 느낀점 (# 필수 조건)\n",
    "  - 20609 김형진 : 프로젝트를 진행하면서 확인하게된 교통량 변화에 대한 예측의 성능이 상당히 떨어지는 것을 보고 국가교통데이터베이스와 같은 믿을수있는 기관의 방식을 찾아보게 되었다. 국가교통 DB 교통수요예측기법에 의하면 현 주제의 문제상황처럼 급격하고 세부적인 변화에 따른 오차는 발생한다 라는 답을 찾을수있었다. 토한 토지이용, 사회경제 지표, 교통체계 등의 다양한 변수들이 주로 사용되는것을 보고 프로젝트의 성능을 향상시키기 위한 키워드를 알 수 있었다.\n",
    "  - 20615 장인수 : 국제 정세에 따른 유가 급등으로 인해 사람들이 자가용을 이용하지 않고 대중교통을 이용하는 사람들이 더 늘어날 것으로 생각되어 유가 변동에 따른 교통량을 주제로 프로젝트를 진행해보았으며, 유가 변동과 교통량은 어느정도의 상관관계를 갖고 있었다. 아무래도 교통량은 매일매일 크게 변동하고 여러가지 변수가 많기 때문에 예측에 어려움이 있었다. 유가 뿐만 아닌 공휴일이나 기온, 유류세의 변화 등 더욱 많은 데이터를 고려해 교통량을 예측해 본다면 조금 더 나은 모델이 생성될 것 같다.\n",
    "  - 20620 최재혁 : 평소에 유가 변동으로 사람들이 교통량이 줄었다는 뉴스 등 여러 가지를 봐왔었는데, 사람이 필수적으로 이용하는 수단인 교통인 만큼 교통량이라는 요소는 유가 변동 말고도 여러 요소가 교통량에 작용함을 알 수 있었다. 또한, 교통량 그래프에서 볼 수 있듯이 굉장히 변동이 큰 데이터에 대해서는 K-최근접 이웃 회귀나 선형 회귀 방법을 이용하여 예측하는 것이 부적절할 수 있다는 사실을 알게 되었다. 추가적으로, 교통량 변화를 다양한 외부요인에 따라 분석을 진행해본다면, 예측하는 모델의 성능이 굉장히 상향될 수 있겠다는 생각을 가지게 되었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f31a41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "fb4569285eef3a3450cb62085a5b1e0da4bce0af555edc33dcf29baf3acc1368"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}