Ttareungi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97ff729b",
   "metadata": {},
   "source": [
    "## 1. 라이브러리 및 데이터\n",
    "## Library & Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90a78155",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "487d27e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./따릉이/train.csv') # 모델 학습 파일\n",
    "test = pd.read_csv('./따릉이/test.csv') # 모델 시험지 파일\n",
    "submission = pd.read_csv('./따릉이/submission.csv') # 답안지 파일"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a0268b",
   "metadata": {},
   "source": [
    "## 2. 탐색적 자료분석\n",
    "## Exploratory Data Analysis (EDA)  \n",
    "  \n",
    "### pd.DataFrame.head()\n",
    " - 데이터 프레임의 위에서부터 n개 행을 보여주는 함수\n",
    " - n의 기본 값(default값)dms 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e183b340",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>76.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>0.042</td>\n",
       "      <td>73.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>0.033</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>54.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>0.040</td>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>431.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  hour  hour_bef_temperature  hour_bef_precipitation  hour_bef_windspeed  \\\n",
       "0   3    20                  16.3                     1.0                 1.5   \n",
       "1   6    13                  20.1                     0.0                 1.4   \n",
       "2   7     6                  13.9                     0.0                 0.7   \n",
       "3   8    23                   8.1                     0.0                 2.7   \n",
       "4   9    18                  29.5                     0.0                 4.8   \n",
       "\n",
       "   hour_bef_humidity  hour_bef_visibility  hour_bef_ozone  hour_bef_pm10  \\\n",
       "0               89.0                576.0           0.027           76.0   \n",
       "1               48.0                916.0           0.042           73.0   \n",
       "2               79.0               1382.0           0.033           32.0   \n",
       "3               54.0                946.0           0.040           75.0   \n",
       "4                7.0               2000.0           0.057           27.0   \n",
       "\n",
       "   hour_bef_pm2.5  count  \n",
       "0            33.0   49.0  \n",
       "1            40.0  159.0  \n",
       "2            19.0   26.0  \n",
       "3            64.0   57.0  \n",
       "4            11.0  431.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "30913b70",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>0.041</td>\n",
       "      <td>44.0</td>\n",
       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>0.061</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>52.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0.041</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   id  hour  hour_bef_temperature  hour_bef_precipitation  hour_bef_windspeed  \\\n",
       "0   0     7                  20.7                     0.0                 1.3   \n",
       "1   1    17                  30.0                     0.0                 5.4   \n",
       "2   2    13                  19.0                     1.0                 2.1   \n",
       "3   4     6                  22.5                     0.0                 2.5   \n",
       "4   5    22                  14.6                     1.0                 3.4   \n",
       "\n",
       "   hour_bef_humidity  hour_bef_visibility  hour_bef_ozone  hour_bef_pm10  \\\n",
       "0               62.0                954.0           0.041           44.0   \n",
       "1               33.0               1590.0           0.061           49.0   \n",
       "2               95.0                193.0           0.020           36.0   \n",
       "3               60.0               1185.0           0.027           52.0   \n",
       "4               93.0                218.0           0.041           18.0   \n",
       "\n",
       "   hour_bef_pm2.5  \n",
       "0            27.0  \n",
       "1            36.0  \n",
       "2            28.0  \n",
       "3            38.0  \n",
       "4            15.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6bc499fb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "   id  count\n",
       "0   0    NaN\n",
       "1   1    NaN\n",
       "2   2    NaN\n",
       "3   4    NaN\n",
       "4   5    NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05af60d0",
   "metadata": {},
   "source": [
    "- id : 날짜와 시간별 id\n",
    "- hour_bef_temperature : 1시간 전 기온\n",
    "- hour_bef_precipitation : 1시간 전 비 정보, 비가 오지 않았으면 0, 비가 오면 1\n",
    "- hour_bef_windspeed : 1시간 전 풍속(평균)\n",
    "- hour_bef_humidity : 1시간 전 습도\n",
    "- hour_bef_visibility : 1시간 전 시정, 시계(특정 기상 상태에 따른 가시성을 의미)\n",
    "- hour_bef_ozone : 1ㅣ간 전 오존\n",
    "- hour_bef_pm10 : 1시간 전 미세먼지(머리카락 굵기의 1/5에서 1/7 크기의 미세먼지\n",
    "- hour_bef_pm2.5 : 1시간 전 미세먼지(머리카락 굵기의 1/20에서 1/30 크기의 미세먼지 \n",
    "- count : 시간에 따른 따릉이 대여 수"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcb399d",
   "metadata": {},
   "source": [
    "### pd.DataFrame.tail()\n",
    " - 데이터 프레임의 아래에서부터 n개 행을 보여주는 함수\n",
    " - n의 기본 값(default)은 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df2f4fb9",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "      <th>count</th>\n",
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       "      <th>1454</th>\n",
       "      <td>2174</td>\n",
       "      <td>4</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.031</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2175</td>\n",
       "      <td>3</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.039</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2176</td>\n",
       "      <td>5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.009</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2178</td>\n",
       "      <td>21</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>0.082</td>\n",
       "      <td>71.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2179</td>\n",
       "      <td>17</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>38.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  hour  hour_bef_temperature  hour_bef_precipitation  \\\n",
       "1454  2174     4                  16.8                     0.0   \n",
       "1455  2175     3                  10.8                     0.0   \n",
       "1456  2176     5                  18.3                     0.0   \n",
       "1457  2178    21                  20.7                     0.0   \n",
       "1458  2179    17                  21.1                     0.0   \n",
       "\n",
       "      hour_bef_windspeed  hour_bef_humidity  hour_bef_visibility  \\\n",
       "1454                 1.6               53.0               2000.0   \n",
       "1455                 3.8               45.0               2000.0   \n",
       "1456                 1.9               54.0               2000.0   \n",
       "1457                 3.7               37.0               1395.0   \n",
       "1458                 3.1               47.0               1973.0   \n",
       "\n",
       "      hour_bef_ozone  hour_bef_pm10  hour_bef_pm2.5  count  \n",
       "1454           0.031           37.0            27.0   21.0  \n",
       "1455           0.039           34.0            19.0   20.0  \n",
       "1456           0.009           30.0            21.0   22.0  \n",
       "1457           0.082           71.0            36.0  216.0  \n",
       "1458           0.046           38.0            17.0  170.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d62463",
   "metadata": {},
   "source": [
    "### pd.DataFrame.shape()\n",
    "- 데이터 프레임의 행의 개수와 열의 개수가 저장되어 있는 속성(attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f9cea33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 11)\n",
      "(715, 10)\n",
      "(715, 2)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)\n",
    "print(submission.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4620662d",
   "metadata": {},
   "source": [
    "### pd.DdataFrame.info()\n",
    "- 데이터셋의 column별 정보를 알려주는 함수\n",
    "- 비어 있지 않은 값은 (non-null)은 몇개인지?\n",
    "- column의 type은 무엇인지?  \n",
    " - type의 종류 : int, float, object 등등"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b90e1458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 11 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   id                      1459 non-null   int64  \n",
      " 1   hour                    1459 non-null   int64  \n",
      " 2   hour_bef_temperature    1457 non-null   float64\n",
      " 3   hour_bef_precipitation  1457 non-null   float64\n",
      " 4   hour_bef_windspeed      1450 non-null   float64\n",
      " 5   hour_bef_humidity       1457 non-null   float64\n",
      " 6   hour_bef_visibility     1457 non-null   float64\n",
      " 7   hour_bef_ozone          1383 non-null   float64\n",
      " 8   hour_bef_pm10           1369 non-null   float64\n",
      " 9   hour_bef_pm2.5          1342 non-null   float64\n",
      " 10  count                   1459 non-null   float64\n",
      "dtypes: float64(9), int64(2)\n",
      "memory usage: 125.5 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35717d98",
   "metadata": {},
   "source": [
    "### pd.DataFrame.describe()\n",
    "- 숫자형 (int, float) column들의 기술 통계량을 보여주는 함수\n",
    "- 기술통계량이란?  \n",
    " - 해당 column에서 비어 있지 않은 값의 개수  \n",
    "- 기술통계량 종류  \n",
    " - count : 해당 column에서 비어 있지 않은 값의 개수 \n",
    " - mean : 평균  \n",
    " - std : 표준편차  \n",
    " - min : 최솟값(이상치 포함)  \n",
    " - 25% (Q1) : 전체 데이터를 순서대로 정렬했을 때, 아래에서 부터 1/4번째 지점에 있는 값  \n",
    " - 50% (Q2) : 중앙값 (전체 데이터를 순서대로 정렬했을 때, 아래에서 부터 2/4번째 지점에 있는 값)  \n",
    " - 75% (Q3) : 전체 데이터를 순서대로 정렬했을 때, 아래에서 부터 3/4번째 지점에 있는 값  \n",
    " - max : 최댓값(이상치 포함)  \n",
    "- 이상치 : 울타리 밖에 있는 부분을 이상치라고 정의함  \n",
    " - 아래쪽 울타리: $Q_1$ - $1.5 * IQR$\n",
    " - 위쪽 울타리: $Q_3$ + $1.5 * IQR$\n",
    " - $IQR$ = $Q_3 - Q_1$\n",
    " \n",
    " <img src=\"https://miro.medium.com/max/10125/1*NRlqiZGQdsIyAu0KzP7LaQ.png\" width=\"700\" height=\"500\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "add750b7",
   "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>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1457.000000</td>\n",
       "      <td>1457.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1457.000000</td>\n",
       "      <td>1457.000000</td>\n",
       "      <td>1383.000000</td>\n",
       "      <td>1369.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1105.914325</td>\n",
       "      <td>11.493489</td>\n",
       "      <td>16.717433</td>\n",
       "      <td>0.031572</td>\n",
       "      <td>2.479034</td>\n",
       "      <td>52.231297</td>\n",
       "      <td>1405.216884</td>\n",
       "      <td>0.039149</td>\n",
       "      <td>57.168736</td>\n",
       "      <td>30.327124</td>\n",
       "      <td>108.563400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>631.338681</td>\n",
       "      <td>6.922790</td>\n",
       "      <td>5.239150</td>\n",
       "      <td>0.174917</td>\n",
       "      <td>1.378265</td>\n",
       "      <td>20.370387</td>\n",
       "      <td>583.131708</td>\n",
       "      <td>0.019509</td>\n",
       "      <td>31.771019</td>\n",
       "      <td>14.713252</td>\n",
       "      <td>82.631733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>555.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>879.000000</td>\n",
       "      <td>0.025500</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1115.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1577.000000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1651.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2179.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id         hour  hour_bef_temperature  hour_bef_precipitation  \\\n",
       "count  1459.000000  1459.000000           1457.000000             1457.000000   \n",
       "mean   1105.914325    11.493489             16.717433                0.031572   \n",
       "std     631.338681     6.922790              5.239150                0.174917   \n",
       "min       3.000000     0.000000              3.100000                0.000000   \n",
       "25%     555.500000     5.500000             12.800000                0.000000   \n",
       "50%    1115.000000    11.000000             16.600000                0.000000   \n",
       "75%    1651.000000    17.500000             20.100000                0.000000   \n",
       "max    2179.000000    23.000000             30.000000                1.000000   \n",
       "\n",
       "       hour_bef_windspeed  hour_bef_humidity  hour_bef_visibility  \\\n",
       "count         1450.000000        1457.000000          1457.000000   \n",
       "mean             2.479034          52.231297          1405.216884   \n",
       "std              1.378265          20.370387           583.131708   \n",
       "min              0.000000           7.000000            78.000000   \n",
       "25%              1.400000          36.000000           879.000000   \n",
       "50%              2.300000          51.000000          1577.000000   \n",
       "75%              3.400000          69.000000          1994.000000   \n",
       "max              8.000000          99.000000          2000.000000   \n",
       "\n",
       "       hour_bef_ozone  hour_bef_pm10  hour_bef_pm2.5        count  \n",
       "count     1383.000000    1369.000000     1342.000000  1459.000000  \n",
       "mean         0.039149      57.168736       30.327124   108.563400  \n",
       "std          0.019509      31.771019       14.713252    82.631733  \n",
       "min          0.003000       9.000000        8.000000     1.000000  \n",
       "25%          0.025500      36.000000       20.000000    37.000000  \n",
       "50%          0.039000      51.000000       26.000000    96.000000  \n",
       "75%          0.052000      69.000000       37.000000   150.000000  \n",
       "max          0.125000     269.000000       90.000000   431.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a2711dde",
   "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>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>715.000000</td>\n",
       "      <td>715.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>679.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1056.854545</td>\n",
       "      <td>11.472727</td>\n",
       "      <td>23.263305</td>\n",
       "      <td>0.051821</td>\n",
       "      <td>2.388515</td>\n",
       "      <td>56.668067</td>\n",
       "      <td>1359.494398</td>\n",
       "      <td>0.041196</td>\n",
       "      <td>36.930678</td>\n",
       "      <td>24.939617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>625.574471</td>\n",
       "      <td>6.928427</td>\n",
       "      <td>4.039645</td>\n",
       "      <td>0.221820</td>\n",
       "      <td>1.171270</td>\n",
       "      <td>19.661880</td>\n",
       "      <td>614.171870</td>\n",
       "      <td>0.020656</td>\n",
       "      <td>12.641503</td>\n",
       "      <td>10.075857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>507.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>20.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>807.250000</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1489.000000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1616.500000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>26.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.275000</td>\n",
       "      <td>73.750000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.052250</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2177.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>33.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.138000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id        hour  hour_bef_temperature  hour_bef_precipitation  \\\n",
       "count   715.000000  715.000000            714.000000              714.000000   \n",
       "mean   1056.854545   11.472727             23.263305                0.051821   \n",
       "std     625.574471    6.928427              4.039645                0.221820   \n",
       "min       0.000000    0.000000             14.600000                0.000000   \n",
       "25%     507.000000    5.500000             20.300000                0.000000   \n",
       "50%    1046.000000   11.000000             22.900000                0.000000   \n",
       "75%    1616.500000   17.000000             26.375000                0.000000   \n",
       "max    2177.000000   23.000000             33.800000                1.000000   \n",
       "\n",
       "       hour_bef_windspeed  hour_bef_humidity  hour_bef_visibility  \\\n",
       "count          714.000000         714.000000           714.000000   \n",
       "mean             2.388515          56.668067          1359.494398   \n",
       "std              1.171270          19.661880           614.171870   \n",
       "min              0.000000          16.000000           126.000000   \n",
       "25%              1.500000          42.000000           807.250000   \n",
       "50%              2.300000          55.000000          1489.000000   \n",
       "75%              3.275000          73.750000          2000.000000   \n",
       "max              5.900000         100.000000          2000.000000   \n",
       "\n",
       "       hour_bef_ozone  hour_bef_pm10  hour_bef_pm2.5  \n",
       "count      680.000000     678.000000      679.000000  \n",
       "mean         0.041196      36.930678       24.939617  \n",
       "std          0.020656      12.641503       10.075857  \n",
       "min          0.003000       9.000000        7.000000  \n",
       "25%          0.027000      28.000000       17.000000  \n",
       "50%          0.039000      35.000000       24.000000  \n",
       "75%          0.052250      45.000000       31.000000  \n",
       "max          0.138000      94.000000       69.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63d54d1",
   "metadata": {},
   "source": [
    "### pd.DataFrame.groupby()\n",
    "- 집단에 대한 통게량 확인\n",
    "\n",
    "<img src=\"https://s3.amazonaws.com/files.dezyre.com/images/Tutorials/Split+Apply+Combine+Strategy.png\" width=\"700\" height=\"500\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d376479",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9c657bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hour'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train.groupby('hour').mean()['count'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e79a6c1",
   "metadata": {},
   "source": [
    "### plt.plot()의 스타일 \n",
    "색깔\n",
    "\n",
    "|문자열|약자|\n",
    "|----|-----|\n",
    "|blue|b|\n",
    "|green|g|\n",
    "|red|r|\n",
    "|cyan|c|\n",
    "|magenta|m|\n",
    "|yellow|y|\n",
    "|black|k|\n",
    "|white|w|\n",
    "\n",
    "마커\n",
    "\n",
    "|마커|의미|\n",
    "|----|----|\n",
    "|.|점|\n",
    "|o|원|\n",
    "|v|역삼각형|\n",
    "|^|삼각형|\n",
    "|s|사각형|\n",
    "|*|별|\n",
    "|x|엑스|\n",
    "|d|다이아몬드|\n",
    "\n",
    "선\n",
    "\n",
    "|문자열|의미|\n",
    "|-----|-----|\n",
    "| - | 실선|\n",
    "|-- | 끊어진 실선|\n",
    "| -.| 점+실선|\n",
    "|:|점선|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "146e2c48",
   "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.plot(train.groupby('hour').mean()['count'], 'co:')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06b68e8",
   "metadata": {},
   "source": [
    "### plt.plot(label, fontsize)\n",
    "- 그래프 제목 생성\n",
    "\n",
    "### plt.xlabel(label, fontsize)\n",
    "- x축 이름 설정\n",
    "\n",
    "### plt.ylabel(label, fontsize)\n",
    "- y축 이름 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f4efc6c",
   "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.plot(train.groupby('hour').mean()['count'], 'co:')\n",
    "plt.grid()\n",
    "\n",
    "plt.title('count by hours', fontsize = 15)\n",
    "plt.xlabel('hour', fontsize = 15)\n",
    "plt.ylabel('count', fontsize = 15)\n",
    "\n",
    "plt.savefig('picture.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cda888b",
   "metadata": {},
   "source": [
    "### plt.axvline(x, color)\n",
    "- 축을 가로지르는 세로 선 생성\n",
    "  \n",
    "### plt.text(x, y, s, fontsize)  \n",
    "- 원하는 위치에 텍스트 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3ca50522",
   "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.plot(train.groupby('hour').mean()['count'], 'co:')\n",
    "plt.grid()\n",
    "\n",
    "plt.title('count by hours', fontsize = 15)\n",
    "plt.xlabel('hour', fontsize = 15)\n",
    "plt.ylabel('count', fontsize = 15)\n",
    "plt.text(8, 120, 'go work', fontsize=10)\n",
    "plt.text(18, 120, 'leave work', fontsize=10)\n",
    "\n",
    "plt.axvline(8, color = 'r')\n",
    "plt.axvline(18, color = 'r')\n",
    "plt.savefig('picture.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b614d8a",
   "metadata": {},
   "source": [
    "### 상관계수\n",
    "- 상관계수 : 두 개의 변수가 같이 일어나는 강도를 나타내는 수치\n",
    "- -1에서 1 사이의 값을 지닌다.\n",
    "- -1이나 1인 수치는 현실 세게에서 관측되기 힘든 수치다.\n",
    "- 분야별로 기준을 정하는 것에 따라 달라지겠지만, 보통 0.4 이상이면 두 개의 변수간에 상관성이 있다고 본다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa9ffec",
   "metadata": {},
   "source": [
    "![상관계수](https://t1.daumcdn.net/cfile/tistory/99DEE1425C6A9F2008)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e30d67e",
   "metadata": {},
   "source": [
    "- 상관관계는 인과관계와 다르다. 아래의 예시를 확인해보자.  \n",
    "  \n",
    "  ![상관성 예시](https://miro.medium.com/max/684/1*JLYI5eCVEN7ZUWXBIrrapw.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed095c99",
   "metadata": {},
   "source": [
    "- 선글라스 판매량이 증가함에 따라, 아이스크림 판매액도 같이 증가하는 것을 볼 수 있다.\n",
    "- 하지만 선글라스 판매량이 증가했기 때문에 아이크스림 판매액이 증가했다라고 해석하는 것은 타당하지 않다.\n",
    "- 선글라스 판매량이 증가했다는 것은 여름 때문이라고 볼 수 있으므로, 날씨가 더워짐에 따라 선글라스 판매량과 아이스크림 판매액이 같이 증가했다고 보는 것이 타당할 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197f84f1",
   "metadata": {},
   "source": [
    "### pd.DataFrame.corr()\n",
    "- correlation coefficient의 줄임말."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "75fe2382",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8ac7fff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(train.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ff3080",
   "metadata": {},
   "source": [
    "## 3. 데이터 전처리\n",
    "### Data cleansing & Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b2a78b",
   "metadata": {},
   "source": [
    "### pd.Series.isna()\n",
    "- 결측치 여부를 확인한다.\n",
    "- 결측치면 True, 아니면 False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "80700096",
   "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>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>64.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>381</td>\n",
       "      <td>1</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>404</td>\n",
       "      <td>3</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.049</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.044</td>\n",
       "      <td>37.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>1196</td>\n",
       "      <td>20</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>39.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>1420</td>\n",
       "      <td>0</td>\n",
       "      <td>14.788136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>1553</td>\n",
       "      <td>18</td>\n",
       "      <td>20.926667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>1717</td>\n",
       "      <td>12</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>0.044</td>\n",
       "      <td>61.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>1855</td>\n",
       "      <td>2</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.044</td>\n",
       "      <td>37.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  hour  hour_bef_temperature  hour_bef_precipitation  \\\n",
       "18      33    13             22.600000                     0.0   \n",
       "244    381     1             14.100000                     0.0   \n",
       "260    404     3             14.000000                     0.0   \n",
       "376    570     0             14.300000                     0.0   \n",
       "780   1196    20             16.500000                     0.0   \n",
       "934   1420     0             14.788136                     NaN   \n",
       "1035  1553    18             20.926667                     NaN   \n",
       "1138  1717    12             21.400000                     0.0   \n",
       "1229  1855     2             14.000000                     0.0   \n",
       "\n",
       "      hour_bef_windspeed  hour_bef_humidity  hour_bef_visibility  \\\n",
       "18                   NaN               41.0                987.0   \n",
       "244                  NaN               55.0               1992.0   \n",
       "260                  NaN               50.0               2000.0   \n",
       "376                  NaN               49.0               2000.0   \n",
       "780                  NaN               31.0               2000.0   \n",
       "934                  NaN                NaN                  NaN   \n",
       "1035                 NaN                NaN                  NaN   \n",
       "1138                 NaN               44.0               1375.0   \n",
       "1229                 NaN               52.0               2000.0   \n",
       "\n",
       "      hour_bef_ozone  hour_bef_pm10  hour_bef_pm2.5  count  \n",
       "18             0.046           64.0            39.0  208.0  \n",
       "244              NaN            NaN             NaN   38.0  \n",
       "260            0.049           35.0            22.0   17.0  \n",
       "376            0.044           37.0            20.0   58.0  \n",
       "780            0.058           39.0            18.0  181.0  \n",
       "934              NaN            NaN             NaN   39.0  \n",
       "1035             NaN            NaN             NaN    1.0  \n",
       "1138           0.044           61.0            37.0  116.0  \n",
       "1229           0.044           37.0            20.0   20.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['hour_bef_windspeed'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "297405aa",
   "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>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_bef_temperature</th>\n",
       "      <th>hour_bef_precipitation</th>\n",
       "      <th>hour_bef_windspeed</th>\n",
       "      <th>hour_bef_humidity</th>\n",
       "      <th>hour_bef_visibility</th>\n",
       "      <th>hour_bef_ozone</th>\n",
       "      <th>hour_bef_pm10</th>\n",
       "      <th>hour_bef_pm2.5</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>1420</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>1553</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  hour  hour_bef_temperature  hour_bef_precipitation  \\\n",
       "934   1420     0                   NaN                     NaN   \n",
       "1035  1553    18                   NaN                     NaN   \n",
       "\n",
       "      hour_bef_windspeed  hour_bef_humidity  hour_bef_visibility  \\\n",
       "934                  NaN                NaN                  NaN   \n",
       "1035                 NaN                NaN                  NaN   \n",
       "\n",
       "      hour_bef_ozone  hour_bef_pm10  hour_bef_pm2.5  count  \n",
       "934              NaN            NaN             NaN   39.0  \n",
       "1035             NaN            NaN             NaN    1.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['hour_bef_temperature'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8232e5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0     14.788136\n",
       "1     14.155738\n",
       "2     13.747541\n",
       "3     13.357377\n",
       "4     13.001639\n",
       "5     12.673770\n",
       "6     12.377049\n",
       "7     12.191803\n",
       "8     12.600000\n",
       "9     14.318033\n",
       "10    16.242623\n",
       "11    18.019672\n",
       "12    19.457377\n",
       "13    20.648333\n",
       "14    21.234426\n",
       "15    21.744262\n",
       "16    22.015000\n",
       "17    21.603333\n",
       "18    20.926667\n",
       "19    19.704918\n",
       "20    18.191803\n",
       "21    16.978333\n",
       "22    16.063934\n",
       "23    15.418033\n",
       "Name: hour_bef_temperature, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('hour').mean()['hour_bef_temperature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a1933fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1d099a44ca0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train.groupby('hour').mean()['hour_bef_temperature'].plot()\n",
    "plt.axhline(train.groupby('hour').mean()['hour_bef_temperature'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6a1b00b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hour'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train.groupby('hour').mean()['hour_bef_windspeed'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b63b9e",
   "metadata": {},
   "source": [
    "### pd.DataFrame.fillna()\n",
    "- 결측치를 채우고자 하는 column과 결측치를 대신하여 넣고자 하는 값을 명시해주어야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e8505a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['hour_bef_temperature'].fillna({934:14.788136, 1035:20.926667}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1f9593ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['hour_bef_windspeed'].fillna({18:3.281356, 244:1.836667, 260:1.965517, 780:3.278333, 946:1.965517, 1035:3.838333, 1138:2.766667, 1229:1.633333}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "73e7b9d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "hour                       0\n",
       "hour_bef_temperature       0\n",
       "hour_bef_precipitation     1\n",
       "hour_bef_windspeed         0\n",
       "hour_bef_humidity          1\n",
       "hour_bef_visibility        1\n",
       "hour_bef_ozone            35\n",
       "hour_bef_pm10             37\n",
       "hour_bef_pm2.5            36\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "73d32cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['hour_bef_temperature'].fillna(19.704918, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d825e95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['hour_bef_windspeed'].fillna(3.595082, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1a8ad3",
   "metadata": {},
   "source": [
    "## 4. 변수 선택 및 선택 구축\n",
    "## Feature Engineering & initial Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "20bb05c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [ 'hour', 'hour_bef_temperature', 'hour_bef_windspeed' ] \n",
    "X_train = train[features]\n",
    "y_train = train['count']\n",
    "X_test = test[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bf7aa94",
   "metadata": {},
   "outputs": [],
   "source": [
    "X -> MODEL -> Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b101d3",
   "metadata": {},
   "source": [
    "### sklean.ensemble.RandomForestRegressor()\n",
    "- 랜덤 포레스트 모형"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306a6aea",
   "metadata": {},
   "source": [
    "### pd.Series.isna()\n",
    "- 결측치 여부를 확인해줍니다.\n",
    "- 결측치면 True, 아니면 False"
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