bike_sharing_demand.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1aab8362",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4e8825",
   "metadata": {},
   "source": [
    "# read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8951708d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c1291a",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7931906a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   datetime    10886 non-null  object \n",
      " 1   season      10886 non-null  int64  \n",
      " 2   holiday     10886 non-null  int64  \n",
      " 3   workingday  10886 non-null  int64  \n",
      " 4   weather     10886 non-null  int64  \n",
      " 5   temp        10886 non-null  float64\n",
      " 6   atemp       10886 non-null  float64\n",
      " 7   humidity    10886 non-null  int64  \n",
      " 8   windspeed   10886 non-null  float64\n",
      " 9   casual      10886 non-null  int64  \n",
      " 10  registered  10886 non-null  int64  \n",
      " 11  count       10886 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "269d86f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6493 entries, 0 to 6492\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   datetime    6493 non-null   object \n",
      " 1   season      6493 non-null   int64  \n",
      " 2   holiday     6493 non-null   int64  \n",
      " 3   workingday  6493 non-null   int64  \n",
      " 4   weather     6493 non-null   int64  \n",
      " 5   temp        6493 non-null   float64\n",
      " 6   atemp       6493 non-null   float64\n",
      " 7   humidity    6493 non-null   int64  \n",
      " 8   windspeed   6493 non-null   float64\n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 456.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9060a140",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['casual','registered'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f60c3ed6",
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.00000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.506614</td>\n",
       "      <td>0.028569</td>\n",
       "      <td>0.680875</td>\n",
       "      <td>1.418427</td>\n",
       "      <td>20.23086</td>\n",
       "      <td>23.655084</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>12.799395</td>\n",
       "      <td>191.574132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.116174</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>0.633839</td>\n",
       "      <td>7.79159</td>\n",
       "      <td>8.474601</td>\n",
       "      <td>19.245033</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>181.144454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.94000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.001500</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.50000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.24000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>16.997900</td>\n",
       "      <td>284.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season       holiday    workingday       weather         temp  \\\n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
       "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
       "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
       "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
       "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
       "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
       "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
       "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
       "\n",
       "              atemp      humidity     windspeed         count  \n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  \n",
       "mean      23.655084     61.886460     12.799395    191.574132  \n",
       "std        8.474601     19.245033      8.164537    181.144454  \n",
       "min        0.760000      0.000000      0.000000      1.000000  \n",
       "25%       16.665000     47.000000      7.001500     42.000000  \n",
       "50%       24.240000     62.000000     12.998000    145.000000  \n",
       "75%       31.060000     77.000000     16.997900    284.000000  \n",
       "max       45.455000    100.000000     56.996900    977.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6277ad02",
   "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.hist(df_train['count'],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e101f36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "std_3 = 3*(df_train['count'].std())\n",
    "count_mean = df_train['count'].mean()\n",
    "df_train = df_train.drop([x for x,y in enumerate(df_train['count']) if np.abs(y-count_mean) > std_3],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9cb92439",
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.500978</td>\n",
       "      <td>0.028960</td>\n",
       "      <td>0.676786</td>\n",
       "      <td>1.420710</td>\n",
       "      <td>20.130507</td>\n",
       "      <td>23.548846</td>\n",
       "      <td>62.049632</td>\n",
       "      <td>12.787706</td>\n",
       "      <td>183.003818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.119043</td>\n",
       "      <td>0.167702</td>\n",
       "      <td>0.467726</td>\n",
       "      <td>0.634956</td>\n",
       "      <td>7.777571</td>\n",
       "      <td>8.463179</td>\n",
       "      <td>19.241364</td>\n",
       "      <td>8.171075</td>\n",
       "      <td>166.681429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.001500</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.240000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>16.997900</td>\n",
       "      <td>276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>734.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season       holiday    workingday       weather          temp  \\\n",
       "count  10739.000000  10739.000000  10739.000000  10739.000000  10739.000000   \n",
       "mean       2.500978      0.028960      0.676786      1.420710     20.130507   \n",
       "std        1.119043      0.167702      0.467726      0.634956      7.777571   \n",
       "min        1.000000      0.000000      0.000000      1.000000      0.820000   \n",
       "25%        2.000000      0.000000      0.000000      1.000000     13.940000   \n",
       "50%        2.000000      0.000000      1.000000      1.000000     20.500000   \n",
       "75%        4.000000      0.000000      1.000000      2.000000     26.240000   \n",
       "max        4.000000      1.000000      1.000000      4.000000     41.000000   \n",
       "\n",
       "              atemp      humidity     windspeed         count  \n",
       "count  10739.000000  10739.000000  10739.000000  10739.000000  \n",
       "mean      23.548846     62.049632     12.787706    183.003818  \n",
       "std        8.463179     19.241364      8.171075    166.681429  \n",
       "min        0.760000      0.000000      0.000000      1.000000  \n",
       "25%       16.665000     47.000000      7.001500     41.000000  \n",
       "50%       24.240000     62.000000     12.998000    141.000000  \n",
       "75%       31.060000     78.000000     16.997900    276.000000  \n",
       "max       45.455000    100.000000     56.996900    734.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11810241",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_train.append(df_test)\n",
    "data.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a352049d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "data['date'] = data['datetime'].apply(lambda x :x.split()[0])\n",
    "data['time'] = data['datetime'].apply(lambda x :x.split()[1])\n",
    "data['hour'] = data['time'].apply(lambda x :x.split(':')[0]).astype('int')\n",
    "data['year'] = data['date'].apply(lambda x :x.split('-')[0]).astype('int')\n",
    "data['month'] = data['date'].apply(lambda x :x.split('-')[1]).astype('int')\n",
    "data['weekday'] = data['date'].apply(lambda dateString: datetime.strptime(dateString,\"%Y-%m-%d\").weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b68e8d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=2,ncols=2)\n",
    "fig.set_size_inches(12,10)\n",
    "sns.distplot(data['temp'],ax=axes[0][0])\n",
    "sns.distplot(data['atemp'],ax=axes[0][1])\n",
    "sns.distplot(data['humidity'],ax=axes[1][0])\n",
    "sns.distplot(data['windspeed'],ax=axes[1][1])\n",
    "\n",
    "axes[0][0].set(xlabel='temp',title='distribution of temp')\n",
    "axes[0][1].set(xlabel='atemp',title='distribution of atemp')\n",
    "axes[1][0].set(xlabel='humidity',title='distribution of humidity')\n",
    "axes[1][1].set(xlabel='windspeed',title='distribution of windspeed')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797be26c",
   "metadata": {},
   "source": [
    "# 修正windspeed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b88b111",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_winspeed_0 = data[data['windspeed'] ==0 ]\n",
    "data_winspeed_not0 = data[data['windspeed'] !=0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88cd7df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictforwinspeed_column = data_winspeed_not0[[\"season\",\"weather\",\"humidity\",\"month\",\"temp\",\"year\",\"atemp\"]].values\n",
    "predictforwinspeed0_column = data_winspeed_0[[\"season\",\"weather\",\"humidity\",\"month\",\"temp\",\"year\",\"atemp\"]].values\n",
    "rfs = RandomForestRegressor(n_estimators=1000,random_state=42)\n",
    "rfs.fit(predictforwinspeed_column,data_winspeed_not0['windspeed'])\n",
    "windspeed_predict = rfs.predict(predictforwinspeed0_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21a8c0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kojun\\AppData\\Local\\Temp/ipykernel_3680/1046263128.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_winspeed_0['windspeed'] = windspeed_predict\n"
     ]
    }
   ],
   "source": [
    "data_winspeed_0['windspeed'] = windspeed_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d7ad3886",
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>1297.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "      <td>2164.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.740758</td>\n",
       "      <td>0.031885</td>\n",
       "      <td>0.694085</td>\n",
       "      <td>1.371072</td>\n",
       "      <td>20.016109</td>\n",
       "      <td>24.011026</td>\n",
       "      <td>68.345656</td>\n",
       "      <td>10.655831</td>\n",
       "      <td>153.057055</td>\n",
       "      <td>10.020795</td>\n",
       "      <td>2011.488447</td>\n",
       "      <td>7.274030</td>\n",
       "      <td>2.971811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.117582</td>\n",
       "      <td>0.175736</td>\n",
       "      <td>0.460900</td>\n",
       "      <td>0.584524</td>\n",
       "      <td>7.751489</td>\n",
       "      <td>7.592057</td>\n",
       "      <td>18.773604</td>\n",
       "      <td>4.040597</td>\n",
       "      <td>157.052887</td>\n",
       "      <td>7.150973</td>\n",
       "      <td>0.499982</td>\n",
       "      <td>3.418187</td>\n",
       "      <td>1.990184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>5.305000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>6.037991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.120000</td>\n",
       "      <td>17.425000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>7.352589</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>9.631735</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.240000</td>\n",
       "      <td>30.305000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>12.774916</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.360000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>32.518844</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season      holiday   workingday      weather         temp  \\\n",
       "count  2164.000000  2164.000000  2164.000000  2164.000000  2164.000000   \n",
       "mean      2.740758     0.031885     0.694085     1.371072    20.016109   \n",
       "std       1.117582     0.175736     0.460900     0.584524     7.751489   \n",
       "min       1.000000     0.000000     0.000000     1.000000     0.820000   \n",
       "25%       2.000000     0.000000     0.000000     1.000000    13.120000   \n",
       "50%       3.000000     0.000000     1.000000     1.000000    20.500000   \n",
       "75%       4.000000     0.000000     1.000000     2.000000    26.240000   \n",
       "max       4.000000     1.000000     1.000000     3.000000    39.360000   \n",
       "\n",
       "             atemp     humidity    windspeed        count         hour  \\\n",
       "count  2164.000000  2164.000000  2164.000000  1297.000000  2164.000000   \n",
       "mean     24.011026    68.345656    10.655831   153.057055    10.020795   \n",
       "std       7.592057    18.773604     4.040597   157.052887     7.150973   \n",
       "min       5.305000    16.000000     6.037991     1.000000     0.000000   \n",
       "25%      17.425000    55.000000     7.352589    21.000000     4.000000   \n",
       "50%      24.240000    71.000000     9.631735   104.000000     9.000000   \n",
       "75%      30.305000    83.000000    12.774916   235.000000    16.000000   \n",
       "max      45.455000   100.000000    32.518844   731.000000    23.000000   \n",
       "\n",
       "              year        month      weekday  \n",
       "count  2164.000000  2164.000000  2164.000000  \n",
       "mean   2011.488447     7.274030     2.971811  \n",
       "std       0.499982     3.418187     1.990184  \n",
       "min    2011.000000     1.000000     0.000000  \n",
       "25%    2011.000000     5.000000     1.000000  \n",
       "50%    2011.000000     8.000000     3.000000  \n",
       "75%    2012.000000    10.000000     5.000000  \n",
       "max    2012.000000    12.000000     6.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_winspeed_0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f78fc906",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_corect = data_winspeed_not0.append(data_winspeed_0)\n",
    "data_corect.reset_index(inplace=True)\n",
    "data_corect.drop('index',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "43dffa2a",
   "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>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>10739.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "      <td>17232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.498085</td>\n",
       "      <td>0.029016</td>\n",
       "      <td>0.680188</td>\n",
       "      <td>1.426764</td>\n",
       "      <td>20.315176</td>\n",
       "      <td>23.723688</td>\n",
       "      <td>62.831708</td>\n",
       "      <td>14.066881</td>\n",
       "      <td>183.003818</td>\n",
       "      <td>11.507950</td>\n",
       "      <td>2011.498317</td>\n",
       "      <td>6.527855</td>\n",
       "      <td>3.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.108630</td>\n",
       "      <td>0.167856</td>\n",
       "      <td>0.466417</td>\n",
       "      <td>0.640080</td>\n",
       "      <td>7.888360</td>\n",
       "      <td>8.587677</td>\n",
       "      <td>19.286670</td>\n",
       "      <td>6.907108</td>\n",
       "      <td>166.681429</td>\n",
       "      <td>6.924261</td>\n",
       "      <td>0.500012</td>\n",
       "      <td>3.446353</td>\n",
       "      <td>2.004701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.003200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>8.998100</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.060000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>19.001200</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season       holiday    workingday       weather          temp  \\\n",
       "count  17232.000000  17232.000000  17232.000000  17232.000000  17232.000000   \n",
       "mean       2.498085      0.029016      0.680188      1.426764     20.315176   \n",
       "std        1.108630      0.167856      0.466417      0.640080      7.888360   \n",
       "min        1.000000      0.000000      0.000000      1.000000      0.820000   \n",
       "25%        2.000000      0.000000      0.000000      1.000000     13.940000   \n",
       "50%        3.000000      0.000000      1.000000      1.000000     20.500000   \n",
       "75%        3.000000      0.000000      1.000000      2.000000     27.060000   \n",
       "max        4.000000      1.000000      1.000000      4.000000     41.000000   \n",
       "\n",
       "              atemp      humidity     windspeed         count          hour  \\\n",
       "count  17232.000000  17232.000000  17232.000000  10739.000000  17232.000000   \n",
       "mean      23.723688     62.831708     14.066881    183.003818     11.507950   \n",
       "std        8.587677     19.286670      6.907108    166.681429      6.924261   \n",
       "min        0.000000      0.000000      6.003200      1.000000      0.000000   \n",
       "25%       16.665000     48.000000      8.998100     41.000000      6.000000   \n",
       "50%       24.240000     63.000000     12.998000    141.000000     11.500000   \n",
       "75%       31.060000     79.000000     19.001200    276.000000     18.000000   \n",
       "max       50.000000    100.000000     56.996900    734.000000     23.000000   \n",
       "\n",
       "               year         month       weekday  \n",
       "count  17232.000000  17232.000000  17232.000000  \n",
       "mean    2011.498317      6.527855      3.019557  \n",
       "std        0.500012      3.446353      2.004701  \n",
       "min     2011.000000      1.000000      0.000000  \n",
       "25%     2011.000000      4.000000      1.000000  \n",
       "50%     2011.000000      7.000000      3.000000  \n",
       "75%     2012.000000     10.000000      5.000000  \n",
       "max     2012.000000     12.000000      6.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corect.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0f413f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='windspeed', ylabel='Density'>"
      ]
     },
     "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": [
    "sns.distplot(data_corect['windspeed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22d5e4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data_corect[pd.notnull(data_corect['count'])].sort_values(by=['datetime'])\n",
    "test = data_corect[~pd.notnull(data_corect['count'])].sort_values(by=['datetime'])\n",
    "test_date = test['datetime']\n",
    "train_y = train['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a5ecd46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_feature = ['datetime','time','date','count']\n",
    "train_x =train.drop(drop_feature,axis=1)\n",
    "test = test.drop(drop_feature,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6099e0f",
   "metadata": {},
   "source": [
    "# Train and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "17697103",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kojun\\Anaconda3\\lib\\site-packages\\cupy\\_environment.py:213: UserWarning: CUDA path could not be detected. Set CUDA_PATH environment variable if CuPy fails to load.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBRegressor(n_estimators =10000)\n",
    "xgb.fit(train_x,np.log(train_y))\n",
    "predict = xgb.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7659b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame({'datetime':test_date,'count':[max(0,x) for x in np.exp(predict)]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3b23117",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}