22.05.29.20.54.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0084728a",
   "metadata": {},
   "source": [
    "# 따릉이 데이터를 활용한 데이터 분석\n",
    "- 각 날짜의 1시간 전의 기상상황을 가지고 1시간 후의 따릉이 대여수를 예측해보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76ded6b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "623770a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('Data/train.csv') \n",
    "test = pd.read_csv('Data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54e0207c",
   "metadata": {
    "scrolled": false
   },
   "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": "9e4b579a",
   "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(視程), 시계(視界)(특정 기상 상태에 따른 가시성을 의미)\n",
    "- hour_bef_ozone : 1시간 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": "code",
   "execution_count": 7,
   "id": "4e178c6b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fd9624a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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",
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       "    <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",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7539e296",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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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       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ecaed86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "hour                        0\n",
       "hour_bef_temperature        2\n",
       "hour_bef_precipitation      2\n",
       "hour_bef_windspeed          9\n",
       "hour_bef_humidity           2\n",
       "hour_bef_visibility         2\n",
       "hour_bef_ozone             76\n",
       "hour_bef_pm10              90\n",
       "hour_bef_pm2.5            117\n",
       "count                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7958efe1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "hour                       0\n",
       "hour_bef_temperature       1\n",
       "hour_bef_precipitation     1\n",
       "hour_bef_windspeed         1\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c75735fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train.drop(['count'],axis = 1)\n",
    "train_y = train['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a240cc2",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.606557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.409836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.377049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.524590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.114754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.557377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>62.360656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>136.688525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>93.540984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>78.803279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>88.327869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>111.901639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>120.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>134.590164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>152.967213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>169.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>187.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>262.163934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>201.606557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>164.868852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>168.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>148.245902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>103.852459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count\n",
       "hour            \n",
       "0      71.766667\n",
       "1      47.606557\n",
       "2      31.409836\n",
       "3      21.377049\n",
       "4      13.524590\n",
       "5      13.114754\n",
       "6      24.557377\n",
       "7      62.360656\n",
       "8     136.688525\n",
       "9      93.540984\n",
       "10     78.803279\n",
       "11     88.327869\n",
       "12    111.901639\n",
       "13    120.050000\n",
       "14    134.590164\n",
       "15    152.967213\n",
       "16    169.100000\n",
       "17    187.133333\n",
       "18    262.163934\n",
       "19    201.606557\n",
       "20    164.868852\n",
       "21    168.816667\n",
       "22    148.245902\n",
       "23    103.852459"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['hour','count']].groupby('hour').mean() #시간대별 따릉이대여수 평균"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe89d3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.fillna(0,inplace = True)\n",
    "test.fillna(0,inplace = True)   # 결측치를 어떻게 메꾸느냐에 따라서 정확도가 달라질까?\n",
    "# 결측치가 5~8%를 차지하는 train데이터의 열들을 제외하면?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "52728ca2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame.describe(train)\n",
    "winter = (climate.hour_bef_temperature <=12) & (climate.hour_bef_temperature>=0)\n",
    "climate_winter = climate.loc[winter,['hour','hour_bef_temperature','count']]\n",
    "sf = (climate.hour_bef_temperature <=20.05) & (climate.hour_bef_temperature>=12)\n",
    "climate_sf = climate.loc[sf,['hour','hour_bef_temperature','count']]\n",
    "summer = (climate.hour_bef_temperature <=30) & (climate.hour_bef_temperature>=20.05)\n",
    "climate_summer = climate.loc[summer,['hour','hour_bef_temperature','count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "54458be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3ef232ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2279551fa30>]"
      ]
     },
     "execution_count": 119,
     "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": [
    "plt.plot('hour', 'count', '*', data = climate_sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b05c4b40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2279557d640>]"
      ]
     },
     "execution_count": 120,
     "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": [
    "plt.plot('hour', 'count', '*', data = climate_summer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f1dc059d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x227955d4310>]"
      ]
     },
     "execution_count": 121,
     "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": [
    "plt.plot('hour', 'count', '*', data = climate_winter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "2b923068",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2279561fee0>]"
      ]
     },
     "execution_count": 122,
     "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": [
    "plt.plot('hour_bef_temperature', 'count', '*', data = climate_sf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c61b7aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2279568d130>]"
      ]
     },
     "execution_count": 123,
     "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": [
    "plt.plot('hour_bef_temperature', 'count', '*', data = climate_winter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a96bd79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x227956e0850>]"
      ]
     },
     "execution_count": 124,
     "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": [
    "plt.plot('hour_bef_temperature', 'count', '*', data = climate_summer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "21d7da7b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-117-0e9dd5eb635d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    302\u001b[0m                 \u001b[1;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m             )\n\u001b[1;32m--> 304\u001b[1;33m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[0m\u001b[0;32m    305\u001b[0m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0;32m    306\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    812\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 814\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    815\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    816\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    661\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    662\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 663\u001b[1;33m             _assert_all_finite(array,\n\u001b[0m\u001b[0;32m    664\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0;32m    665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[0;32m    101\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m    102\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m    104\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m                     (type_err,\n",
      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "model=RandomForestRegressor(n_estimators=100)\n",
    "model.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79ad6ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f92f9a68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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\n",
       "..    ...    ...\n",
       "710  2148    NaN\n",
       "711  2149    NaN\n",
       "712  2165    NaN\n",
       "713  2166    NaN\n",
       "714  2177    NaN\n",
       "\n",
       "[715 rows x 2 columns]"
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     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "submission = pd.read_csv('Data/submission.csv')\n",
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e2bbfc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['count'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29c4da72",
   "metadata": {},
   "outputs": [
    {
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       "       id   count\n",
       "0       0   91.10\n",
       "1       1  230.04\n",
       "2       2   79.95\n",
       "3       4   29.53\n",
       "4       5   68.11\n",
       "..    ...     ...\n",
       "710  2148   54.71\n",
       "711  2149   68.87\n",
       "712  2165  121.63\n",
       "713  2166  169.84\n",
       "714  2177  173.86\n",
       "\n",
       "[715 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08c76330",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('베이스라인.csv',index = False)"
   ]
  }
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