Group0_Notebook_Assignment2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b94a9a63-5405-4776-9796-9c190b503e2f",
   "metadata": {},
   "source": [
    "# **Group Assignment** - Bike Sharing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85ed5e5-6e8e-4848-800d-d5c9c4675fcd",
   "metadata": {},
   "source": [
    "- `instant`: record index\n",
    "- `dteday` : date\n",
    "- `season` : season 1:winter, 2:spring, 3:summer, 4:fall\n",
    "- `yr` : year (0: 2011, 1:2012)\n",
    "- `mnth` : month ( 1 to 12)\n",
    "- `hr` : hour (0 to 23)\n",
    "- `holiday` : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "- `weekday` : day of the week\n",
    "- `workingday` : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ `weathersit` : \n",
    "\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- `temp` : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- `atemp`: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- `hum`: Normalized humidity. The values are divided to 100 (max)\n",
    "- `windspeed`: Normalized wind speed. The values are divided to 67 (max)\n",
    "- `casual`: count of casual users\n",
    "- `registered`: count of registered users\n",
    "- `cnt`: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98fd15ee-3dab-4626-a3fe-f3dea434b71c",
   "metadata": {},
   "source": [
    "## PART I: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f456cffd-2d57-41ab-9809-b6da5f579bd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/constantinwiederin/Documents/IE/Python2/Group_Project/Data/bike-sharing-hourly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'hr', 'holiday', 'weekday',\n",
       "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
       "       'casual', 'registered', 'cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "hr            0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       17379\n",
       "dteday          731\n",
       "season            4\n",
       "yr                2\n",
       "mnth             12\n",
       "hr               24\n",
       "holiday           2\n",
       "weekday           7\n",
       "workingday        2\n",
       "weathersit        4\n",
       "temp             50\n",
       "atemp            65\n",
       "hum              89\n",
       "windspeed        30\n",
       "casual          322\n",
       "registered      776\n",
       "cnt             869\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the number of unique values in each column\n",
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['instant','dteday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I want to create a box plot of my data to see the distribution of the data\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.boxplot(data=data)\n",
    "plt.yscale('log')  # Set the y-axis scale to logarithmic\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season          0\n",
       "yr              0\n",
       "mnth            0\n",
       "hr              0\n",
       "holiday       500\n",
       "weekday         0\n",
       "workingday      0\n",
       "weathersit      3\n",
       "temp            0\n",
       "atemp           1\n",
       "hum            22\n",
       "windspeed     107\n",
       "casual        467\n",
       "registered    371\n",
       "cnt           244\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_scores = stats.zscore(data)\n",
    "\n",
    "# Print the z-scores\n",
    "z_scores\n",
    "\n",
    "# Convert the z-scores to absolute values\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "\n",
    "# count for each column the number of outliers and print for each column the number of outliers\n",
    "outliers = (abs_z_scores > 3).sum()\n",
    "outliers\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No need to clamp as it is very little number of outliers and with high probability we use a tree algorithm no need to take care fo these few outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I want to create a correlation matrix to see the relationship between the variables\n",
    "correlation_matrix = data.corr()\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.show()\n",
    "\n",
    "# I want to create a pairplot to see the relationship between the variables\n",
    "#sns.pairplot(data)\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.scatterplot(x='hum', y='cnt', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the total number of causal users vs if workingday\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(x='workingday', y='casual', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season          int64\n",
       "yr              int64\n",
       "mnth            int64\n",
       "hr              int64\n",
       "holiday         int64\n",
       "weekday         int64\n",
       "workingday      int64\n",
       "weathersit      int64\n",
       "temp          float64\n",
       "atemp         float64\n",
       "hum           float64\n",
       "windspeed     float64\n",
       "casual          int64\n",
       "registered      int64\n",
       "cnt             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummify holiday, weekday, weathersit, workingday\n",
    "data = pd.get_dummies(data, columns=['holiday', 'weekday', 'weathersit', 'workingday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'yr', 'mnth', 'hr', 'temp', 'atemp', 'hum', 'windspeed',\n",
       "       'casual', 'registered', 'cnt', 'holiday_0', 'holiday_1', 'weekday_0',\n",
       "       'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',\n",
       "       'weekday_6', 'weathersit_1', 'weathersit_2', 'weathersit_3',\n",
       "       'weathersit_4', 'workingday_0', 'workingday_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create 2 new features that are the lagged data of the cnt column by 1 and 2 hours\n",
    "#data['cnt_lag1'] = data['cnt'].shift(1)\n",
    "#data['cnt_lag2'] = data['cnt'].shift(2)\n",
    "\n",
    "#create 4 new features that are the lagged data of the casual and registered column by 1 and 2 hours\n",
    "#data['casual_lag1'] = data['casual'].shift(1)\n",
    "#data['casual_lag2'] = data['casual'].shift(2)\n",
    "#data['registered_lag1'] = data['registered'].shift(1)\n",
    "#data['registered_lag2'] = data['registered'].shift(2)\n",
    "\n",
    "#create new features that are the lagged data of the cnt,casual and registered column by 1 and 2 days\n",
    "data['cnt_lag24'] = data['cnt'].shift(24)\n",
    "data['casual_lag24'] = data['casual'].shift(24)\n",
    "data['registered_lag24'] = data['registered'].shift(24)\n",
    "data['cnt_lag48'] = data['cnt'].shift(48)\n",
    "data['casual_lag48'] = data['casual'].shift(48)\n",
    "data['registered_lag48'] = data['registered'].shift(48)\n",
    "\n",
    "#create feature that is the rolling average of the cnt column over the last 1,2,3,4,5,6,7,8,9,10,11,12,24,48 hours\n",
    "#data['cnt_rolling_avg_2'] = data['cnt'].rolling(window=2).mean()\n",
    "#data['cnt_rolling_avg_3'] = data['cnt'].rolling(window=3).mean()\n",
    "#data['cnt_rolling_avg_4'] = data['cnt'].rolling(window=4).mean()\n",
    "#data['cnt_rolling_avg_5'] = data['cnt'].rolling(window=5).mean()\n",
    "#data['cnt_rolling_avg_6'] = data['cnt'].rolling(window=6).mean()\n",
    "#data['cnt_rolling_avg_7'] = data['cnt'].rolling(window=7).mean()\n",
    "#data['cnt_rolling_avg_8'] = data['cnt'].rolling(window=8).mean()\n",
    "#data['cnt_rolling_avg_9'] = data['cnt'].rolling(window=9).mean()\n",
    "#data['cnt_rolling_avg_10'] = data['cnt'].rolling(window=10).mean()\n",
    "#data['cnt_rolling_avg_11'] = data['cnt'].rolling(window=11).mean()\n",
    "data['cnt_rolling_avg_12'] = data['cnt'].rolling(window=12).mean()\n",
    "data['cnt_rolling_avg_24'] = data['cnt'].rolling(window=24).mean()\n",
    "data['cnt_rolling_avg_48'] = data['cnt'].rolling(window=48).mean()\n",
    "\n",
    "#create random variable\n",
    "data['random'] = random.sample(range(1, 17380), data.shape[0])\n",
    "\n",
    "#Create feature that is called ridability which is a combination of the temperature, humidity, windspeed and weathersit\n",
    "data['ridability'] = data['temp'] * (1/(data['hum']+0.01)) * (1/(data['windspeed']+0.01)) * (data['weathersit_1'] * 100 +0.01) * (data['weathersit_2'] * 64 +0.01) * (data['weathersit_3'] * 32 +0.01) * (data['weathersit_4'] * 15 +0.01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbab68f-8197-4ea0-a8cd-56773e5772a7",
   "metadata": {},
   "source": [
    "## PART II: Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season                  int64\n",
       "yr                      int64\n",
       "mnth                    int64\n",
       "hr                      int64\n",
       "temp                  float64\n",
       "atemp                 float64\n",
       "hum                   float64\n",
       "windspeed             float64\n",
       "casual                  int64\n",
       "registered              int64\n",
       "cnt                     int64\n",
       "holiday_0               uint8\n",
       "holiday_1               uint8\n",
       "weekday_0               uint8\n",
       "weekday_1               uint8\n",
       "weekday_2               uint8\n",
       "weekday_3               uint8\n",
       "weekday_4               uint8\n",
       "weekday_5               uint8\n",
       "weekday_6               uint8\n",
       "weathersit_1            uint8\n",
       "weathersit_2            uint8\n",
       "weathersit_3            uint8\n",
       "weathersit_4            uint8\n",
       "workingday_0            uint8\n",
       "workingday_1            uint8\n",
       "cnt_lag24             float64\n",
       "casual_lag24          float64\n",
       "registered_lag24      float64\n",
       "cnt_lag48             float64\n",
       "casual_lag48          float64\n",
       "registered_lag48      float64\n",
       "cnt_rolling_avg_12    float64\n",
       "cnt_rolling_avg_24    float64\n",
       "cnt_rolling_avg_48    float64\n",
       "random                  int64\n",
       "ridability            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season                 0\n",
       "yr                     0\n",
       "mnth                   0\n",
       "hr                     0\n",
       "temp                   0\n",
       "atemp                  0\n",
       "hum                    0\n",
       "windspeed              0\n",
       "casual                 0\n",
       "registered             0\n",
       "cnt                    0\n",
       "holiday_0              0\n",
       "holiday_1              0\n",
       "weekday_0              0\n",
       "weekday_1              0\n",
       "weekday_2              0\n",
       "weekday_3              0\n",
       "weekday_4              0\n",
       "weekday_5              0\n",
       "weekday_6              0\n",
       "weathersit_1           0\n",
       "weathersit_2           0\n",
       "weathersit_3           0\n",
       "weathersit_4           0\n",
       "workingday_0           0\n",
       "workingday_1           0\n",
       "cnt_lag24             24\n",
       "casual_lag24          24\n",
       "registered_lag24      24\n",
       "cnt_lag48             48\n",
       "casual_lag48          48\n",
       "registered_lag48      48\n",
       "cnt_rolling_avg_12    11\n",
       "cnt_rolling_avg_24    23\n",
       "cnt_rolling_avg_48    47\n",
       "random                 0\n",
       "ridability             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the features and target variable\n",
    "X = data.drop(columns=['cnt', 'casual', 'registered'])\n",
    "y = data['cnt']\n",
    "Y = data[['casual', 'registered']]\n",
    "\n",
    "#split the data into training and testing set\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=False)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale the data using the robust scaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "scaler = RobustScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting registered and casual users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of the Decision Tree model on the training set: 0.0\n",
      "Mean Squared Error of the Linear Regression model on the training set: 3232.957168883708\n",
      "Mean Squared Error of the Decision Tree model on the testing set: 3445.6654167868473\n",
      "Mean Squared Error of the Linear Regression model on the testing set: 5912.358154247797\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.0742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.0112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.00931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.00588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.00526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.00525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.00436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.00278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.00198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.00159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.00128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.00115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.00105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.000506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.000482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.000222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "23           cnt_lag24      0.402\n",
       "3                   hr      0.161\n",
       "25    registered_lag24      0.143\n",
       "29  cnt_rolling_avg_12     0.0742\n",
       "22        workingday_1      0.054\n",
       "30  cnt_rolling_avg_24     0.0351\n",
       "11           weekday_1     0.0217\n",
       "33          ridability      0.013\n",
       "24        casual_lag24     0.0113\n",
       "31  cnt_rolling_avg_48     0.0112\n",
       "28    registered_lag48    0.00931\n",
       "5                atemp    0.00705\n",
       "19        weathersit_3    0.00588\n",
       "27        casual_lag48    0.00554\n",
       "15           weekday_5    0.00526\n",
       "16           weekday_6    0.00525\n",
       "26           cnt_lag48     0.0052\n",
       "6                  hum    0.00495\n",
       "4                 temp    0.00458\n",
       "32              random    0.00436\n",
       "2                 mnth    0.00322\n",
       "1                   yr    0.00278\n",
       "8            holiday_0    0.00198\n",
       "7            windspeed    0.00188\n",
       "10           weekday_0    0.00159\n",
       "21        workingday_0    0.00128\n",
       "12           weekday_2    0.00115\n",
       "0               season    0.00105\n",
       "17        weathersit_1   0.000506\n",
       "9            holiday_1   0.000482\n",
       "14           weekday_4   0.000284\n",
       "13           weekday_3   0.000222\n",
       "18        weathersit_2   0.000123\n",
       "20        weathersit_4        0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initiate decision tree regressor\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import joblib\n",
    "import numpy as np\n",
    "#import linear regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#initiate the decision tree regressor\n",
    "dt = DecisionTreeRegressor()\n",
    "#initiate the linear regression\n",
    "lr = LinearRegression()\n",
    "\n",
    "#Now train the model using the training sets\n",
    "dt.fit(X_train, Y_train)\n",
    "lr.fit(X_train, Y_train)\n",
    "\n",
    "#predict the response on the training set\n",
    "y_pred_dt = dt.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_train)\n",
    "\n",
    "#predict the response on the testing set\n",
    "y_pred_dt_test = dt.predict(X_test)\n",
    "y_pred_lr_test = lr.predict(X_test)\n",
    "\n",
    "#calculate the mean squared error of the model\n",
    "mse_dt = mean_squared_error(Y_train, y_pred_dt)\n",
    "mse_lr = mean_squared_error(Y_train, y_pred_lr)\n",
    "\n",
    "#calculate the mean squared error of the model\n",
    "mse_dt_test = mean_squared_error(Y_test, y_pred_dt_test)\n",
    "mse_lr_test = mean_squared_error(Y_test, y_pred_lr_test)\n",
    "\n",
    "print('Mean Squared Error of the Decision Tree model on the training set:', mse_dt)\n",
    "print('Mean Squared Error of the Linear Regression model on the training set:', mse_lr)\n",
    "\n",
    "print('Mean Squared Error of the Decision Tree model on the testing set:', mse_dt_test)\n",
    "print('Mean Squared Error of the Linear Regression model on the testing set:', mse_lr_test)\n",
    "\n",
    "#print feature importances\n",
    "df_importances = pd.DataFrame({'feature': X.columns, 'importance': dt.feature_importances_})\n",
    "\n",
    "df_importances.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances['importance'] = df_importances['importance'].map('{:.3}'.format)\n",
    "df_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of the Random Forest model on the training set: 93.80128973961371\n",
      "Mean Squared Error of the Random Forest model on the testing set: 1997.3922824055385\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.0312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.0129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.00852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.00676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.00603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.00568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.00384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.00383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.00274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.00178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.000868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.000637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.000608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.000334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.00031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>2.02e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "23           cnt_lag24      0.405\n",
       "3                   hr      0.158\n",
       "25    registered_lag24       0.14\n",
       "29  cnt_rolling_avg_12       0.07\n",
       "30  cnt_rolling_avg_24     0.0383\n",
       "21        workingday_0     0.0316\n",
       "22        workingday_1     0.0312\n",
       "11           weekday_1     0.0175\n",
       "24        casual_lag24     0.0146\n",
       "33          ridability     0.0129\n",
       "28    registered_lag48       0.01\n",
       "31  cnt_rolling_avg_48    0.00852\n",
       "16           weekday_6    0.00676\n",
       "6                  hum    0.00642\n",
       "26           cnt_lag48    0.00603\n",
       "27        casual_lag48    0.00573\n",
       "19        weathersit_3    0.00568\n",
       "5                atemp    0.00495\n",
       "4                 temp    0.00495\n",
       "32              random    0.00384\n",
       "15           weekday_5    0.00383\n",
       "2                 mnth    0.00276\n",
       "1                   yr    0.00274\n",
       "7            windspeed    0.00243\n",
       "10           weekday_0    0.00178\n",
       "0               season   0.000894\n",
       "8            holiday_0   0.000868\n",
       "12           weekday_2   0.000637\n",
       "9            holiday_1   0.000608\n",
       "14           weekday_4   0.000495\n",
       "17        weathersit_1   0.000463\n",
       "13           weekday_3   0.000334\n",
       "18        weathersit_2    0.00031\n",
       "20        weathersit_4   2.02e-06"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Initiate Random Forest Regressor\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_rf = model.predict(X_train)\n",
    "y_pred_rf_test = model.predict(X_test)\n",
    "\n",
    "mse_rf = mean_squared_error(Y_train, y_pred_rf)\n",
    "mse_rf_test = mean_squared_error(Y_test, y_pred_rf_test)\n",
    "\n",
    "print('Mean Squared Error of the Random Forest model on the training set:', mse_rf)\n",
    "print('Mean Squared Error of the Random Forest model on the testing set:', mse_rf_test)\n",
    "\n",
    "df_importances_rf = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_})\n",
    "\n",
    "df_importances_rf.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_rf['importance'] = df_importances_rf['importance'].map('{:.3}'.format)\n",
    "df_importances_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of the XGBoost model on the training set: 147.7273544446465\n",
      "Mean Squared Error of the XGBoost model on the testing set: 1777.2475821738299\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.0995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.0994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.0577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.0422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.0187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.0154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.0128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.00701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.00666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.0065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.00545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.00423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.00308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.00296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.00155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.00114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "25    registered_lag24      0.245\n",
       "23           cnt_lag24      0.125\n",
       "21        workingday_0      0.121\n",
       "19        weathersit_3     0.0995\n",
       "11           weekday_1     0.0994\n",
       "3                   hr     0.0577\n",
       "16           weekday_6     0.0422\n",
       "10           weekday_0      0.029\n",
       "24        casual_lag24     0.0277\n",
       "29  cnt_rolling_avg_12     0.0231\n",
       "12           weekday_2     0.0187\n",
       "30  cnt_rolling_avg_24     0.0186\n",
       "1                   yr     0.0154\n",
       "15           weekday_5     0.0128\n",
       "33          ridability    0.00701\n",
       "14           weekday_4    0.00666\n",
       "5                atemp    0.00656\n",
       "17        weathersit_1     0.0065\n",
       "8            holiday_0    0.00545\n",
       "27        casual_lag48    0.00497\n",
       "28    registered_lag48    0.00423\n",
       "6                  hum    0.00377\n",
       "26           cnt_lag48     0.0035\n",
       "4                 temp    0.00318\n",
       "13           weekday_3    0.00308\n",
       "31  cnt_rolling_avg_48    0.00296\n",
       "2                 mnth    0.00218\n",
       "18        weathersit_2    0.00155\n",
       "7            windspeed    0.00128\n",
       "0               season    0.00114\n",
       "32              random     0.0011\n",
       "20        weathersit_4   0.000414\n",
       "9            holiday_1        0.0\n",
       "22        workingday_1        0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initiate XGBoost Regressor\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "model = xgb.XGBRegressor()\n",
    "model.fit(X_train, Y_train)\n",
    "    \n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_xgb = model.predict(X_train)\n",
    "y_pred_xgb_test = model.predict(X_test)\n",
    "\n",
    "mse_xgb = mean_squared_error(Y_train, y_pred_xgb)\n",
    "mse_xgb_test = mean_squared_error(Y_test, y_pred_xgb_test)\n",
    "\n",
    "print('Mean Squared Error of the XGBoost model on the training set:', mse_xgb)\n",
    "print('Mean Squared Error of the XGBoost model on the testing set:', mse_xgb_test)\n",
    "\n",
    "df_importances_xgb = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_})\n",
    "df_importances_xgb.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_xgb['importance'] = df_importances_xgb['importance'].map('{:.3}'.format)\n",
    "df_importances_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 16 candidates, totalling 32 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=3, n_estimators=300; total time=  22.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=3, n_estimators=300; total time=  22.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  23.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  24.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=3, n_estimators=350; total time=  25.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=350; total time=  27.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=3, n_estimators=350; total time=  27.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=350; total time=  27.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  20.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  21.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=3, n_estimators=300; total time=  20.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=3, n_estimators=300; total time=  21.7s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=350; total time=  25.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=350; total time=  24.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=3, n_estimators=350; total time=  24.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=3, n_estimators=350; total time=  25.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  25.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  26.7s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=3, n_estimators=300; total time=  25.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=3, n_estimators=300; total time=  25.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=2, n_estimators=350; total time=  31.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=2, n_estimators=350; total time=  32.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=3, n_estimators=350; total time=  29.8s\n",
      "[CV] END max_depth=25, min_samples_leaf=1, min_samples_split=3, n_estimators=350; total time=  31.7s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  25.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  26.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=350; total time=  29.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=350; total time=  30.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=300; total time=  25.5s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=300; total time=  26.5s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=350; total time=  28.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=350; total time=  27.7s\n",
      "{'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "0.7917745412508168\n",
      "RandomForestRegressor(max_depth=20, min_samples_leaf=2, n_estimators=300)\n",
      "Mean Squared Error of the Random Forest model on the training set: 157.48323113538044\n",
      "Mean Squared Error of the Random Forest model on the testing set: 2013.0002262673574\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.0319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.0311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.0181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.0123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.00968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.00819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.0075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.00635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.00541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.00383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.00312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.00171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.000725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.000634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.000604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.000515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.000398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.00027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "23           cnt_lag24      0.409\n",
       "3                   hr      0.156\n",
       "25    registered_lag24      0.142\n",
       "29  cnt_rolling_avg_12     0.0695\n",
       "30  cnt_rolling_avg_24     0.0382\n",
       "21        workingday_0     0.0319\n",
       "22        workingday_1     0.0311\n",
       "11           weekday_1     0.0181\n",
       "24        casual_lag24     0.0138\n",
       "33          ridability     0.0123\n",
       "28    registered_lag48    0.00968\n",
       "31  cnt_rolling_avg_48    0.00819\n",
       "16           weekday_6     0.0075\n",
       "19        weathersit_3    0.00635\n",
       "6                  hum    0.00599\n",
       "26           cnt_lag48    0.00541\n",
       "27        casual_lag48    0.00521\n",
       "5                atemp    0.00475\n",
       "4                 temp     0.0045\n",
       "15           weekday_5    0.00383\n",
       "32              random    0.00312\n",
       "1                   yr     0.0025\n",
       "2                 mnth    0.00246\n",
       "7            windspeed    0.00209\n",
       "10           weekday_0    0.00171\n",
       "0               season   0.000725\n",
       "8            holiday_0   0.000634\n",
       "9            holiday_1   0.000604\n",
       "12           weekday_2   0.000515\n",
       "14           weekday_4   0.000447\n",
       "17        weathersit_1   0.000398\n",
       "13           weekday_3    0.00027\n",
       "18        weathersit_2   0.000246\n",
       "20        weathersit_4        0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GridsearchCV for random forest\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [300, 350],\n",
    "    'max_depth': [20, 25],\n",
    "    'min_samples_leaf': [1, 2],\n",
    "    'min_samples_split': [2, 3]\n",
    "}\n",
    "\n",
    "# Initiate the random forest regressor\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "# Initiate the grid search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=2, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the grid search\n",
    "grid_search.fit(X_train, Y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_rf = grid_search.best_estimator_.predict(X_train)\n",
    "y_pred_rf_test = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "mse_rf = mean_squared_error(Y_train, y_pred_rf)\n",
    "mse_rf_test = mean_squared_error(Y_test, y_pred_rf_test)\n",
    "\n",
    "print('Mean Squared Error of the Random Forest model on the training set:', mse_rf)\n",
    "print('Mean Squared Error of the Random Forest model on the testing set:', mse_rf_test)\n",
    "\n",
    "df_importances_rf = pd.DataFrame({'feature': X.columns, 'importance': grid_search.best_estimator_.feature_importances_})\n",
    "df_importances_rf.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_rf['importance'] = df_importances_rf['importance'].map('{:.3}'.format)\n",
    "df_importances_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time= 1.6min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time= 1.6min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time= 1.6min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time= 1.7min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time= 1.7min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time= 2.0min\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 2.3min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time= 2.4min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time= 2.4min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time= 2.5min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time= 2.4min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time= 2.5min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 1.9min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 2.0min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 1.9min\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 1.9min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time= 1.7min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time= 1.7min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time= 1.8min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 2.2min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 2.2min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 2.2min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 2.2min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 2.2min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time= 2.1min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time= 1.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=12.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=24.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=24.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=24.3min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=24.3min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=24.3min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=24.3min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=24.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=13.6min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time= 1.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time= 1.2min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time= 1.1min\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time= 1.1min\n",
      "{'learning_rate': 0.08, 'max_depth': 25, 'n_estimators': 320}\n",
      "0.8434073661103003\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.08, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=25, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=320, n_jobs=None, num_parallel_tree=None,\n",
      "             predictor=None, random_state=None, ...)\n",
      "Mean Squared Error of the XGBoost model on the training set: 20021.387211118537\n",
      "Mean Squared Error of the XGBoost model on the testing set: 39614.995291478015\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.0378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.0368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.0261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.0161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.00787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.00328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.00315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.00302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>0.00256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.00222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.00167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.000746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.000726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.000621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.000352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "25    registered_lag24      0.316\n",
       "21        workingday_0      0.218\n",
       "11           weekday_1      0.139\n",
       "16           weekday_6      0.125\n",
       "19        weathersit_3     0.0378\n",
       "3                   hr     0.0368\n",
       "8            holiday_0     0.0261\n",
       "24        casual_lag24     0.0202\n",
       "29  cnt_rolling_avg_12      0.017\n",
       "10           weekday_0     0.0161\n",
       "30  cnt_rolling_avg_24     0.0114\n",
       "15           weekday_5    0.00787\n",
       "28    registered_lag48    0.00328\n",
       "1                   yr    0.00315\n",
       "23           cnt_lag24    0.00302\n",
       "20        weathersit_4    0.00256\n",
       "33          ridability    0.00222\n",
       "6                  hum    0.00181\n",
       "31  cnt_rolling_avg_48    0.00167\n",
       "5                atemp    0.00165\n",
       "12           weekday_2     0.0016\n",
       "27        casual_lag48    0.00144\n",
       "4                 temp    0.00109\n",
       "26           cnt_lag48      0.001\n",
       "13           weekday_3   0.000746\n",
       "2                 mnth   0.000726\n",
       "14           weekday_4   0.000712\n",
       "17        weathersit_1   0.000621\n",
       "7            windspeed   0.000352\n",
       "32              random   0.000317\n",
       "0               season   0.000284\n",
       "18        weathersit_2   0.000243\n",
       "22        workingday_1        0.0\n",
       "9            holiday_1        0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GridsearchCV for XGBoost\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [320, 300],\n",
    "    'max_depth': [20, 25],\n",
    "    'learning_rate': [0.03, 0.05, 0.08]\n",
    "}\n",
    "\n",
    "# Initiate the random forest regressor\n",
    "model = xgb.XGBRegressor()\n",
    "\n",
    "# Initiate the grid search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "\n",
    "# Fit the grid search\n",
    "grid_search.fit(X, Y)\n",
    "\n",
    "# Print the best parameters\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_xgb = grid_search.best_estimator_.predict(X_train)\n",
    "y_pred_xgb_test = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "mse_xgb = mean_squared_error(Y_train, y_pred_xgb)\n",
    "mse_xgb_test = mean_squared_error(Y_test, y_pred_xgb_test)\n",
    "\n",
    "print('Mean Squared Error of the XGBoost model on the training set:', mse_xgb)\n",
    "print('Mean Squared Error of the XGBoost model on the testing set:', mse_xgb_test)\n",
    "\n",
    "df_importances_xgb = pd.DataFrame({'feature': X.columns, 'importance': grid_search.best_estimator_.feature_importances_})\n",
    "df_importances_xgb.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_xgb['importance'] = df_importances_xgb['importance'].map('{:.3}'.format)\n",
    "df_importances_xgb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Only predicting number of users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of the Decision Tree model on the training set: 0.0\n",
      "Mean Squared Error of the Linear Regression model on the training set: 8015.337665491084\n",
      "Mean Squared Error of the Decision Tree model on the testing set: 8046.734929333718\n",
      "Mean Squared Error of the Linear Regression model on the testing set: 14204.43245112702\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.0311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>workingday_1</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>0.0164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.0137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.0121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>0.00831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.00759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.00611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.00586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>workingday_0</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.00445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.00405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>random</td>\n",
       "      <td>0.00321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.00232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.00152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.000483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.00043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>holiday_1</td>\n",
       "      <td>0.00036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>0.000217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>holiday_0</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_4</td>\n",
       "      <td>1.34e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "23           cnt_lag24      0.545\n",
       "3                   hr      0.158\n",
       "29  cnt_rolling_avg_12      0.086\n",
       "30  cnt_rolling_avg_24     0.0351\n",
       "25    registered_lag24     0.0311\n",
       "22        workingday_1      0.019\n",
       "11           weekday_1     0.0164\n",
       "31  cnt_rolling_avg_48     0.0137\n",
       "16           weekday_6     0.0131\n",
       "19        weathersit_3     0.0121\n",
       "10           weekday_0    0.00831\n",
       "28    registered_lag48    0.00759\n",
       "24        casual_lag24    0.00611\n",
       "33          ridability    0.00586\n",
       "5                atemp    0.00523\n",
       "6                  hum    0.00505\n",
       "21        workingday_0     0.0045\n",
       "26           cnt_lag48    0.00445\n",
       "27        casual_lag48    0.00405\n",
       "4                 temp    0.00376\n",
       "32              random    0.00321\n",
       "2                 mnth    0.00259\n",
       "15           weekday_5    0.00232\n",
       "7            windspeed    0.00194\n",
       "1                   yr    0.00152\n",
       "0               season     0.0012\n",
       "13           weekday_3   0.000483\n",
       "14           weekday_4   0.000457\n",
       "18        weathersit_2   0.000446\n",
       "17        weathersit_1    0.00043\n",
       "9            holiday_1    0.00036\n",
       "12           weekday_2   0.000217\n",
       "8            holiday_0   0.000118\n",
       "20        weathersit_4   1.34e-07"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#initiate the decision tree regressor\n",
    "dt = DecisionTreeRegressor()\n",
    "#initiate the linear regression\n",
    "lr = LinearRegression()\n",
    "\n",
    "#Now train the model using the training sets\n",
    "dt.fit(X_train, y_train)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#predict the response on the training set\n",
    "y_pred_dt = dt.predict(x_train)\n",
    "y_pred_lr = lr.predict(x_train)\n",
    "\n",
    "#predict the response on the testing set\n",
    "y_pred_dt_test = dt.predict(x_test)\n",
    "y_pred_lr_test = lr.predict(x_test)\n",
    "\n",
    "#calculate the mean squared error of the model\n",
    "mse_dt = mean_squared_error(y_train, y_pred_dt)\n",
    "mse_lr = mean_squared_error(y_train, y_pred_lr)\n",
    "\n",
    "#calculate the mean squared error of the model\n",
    "mse_dt_test = mean_squared_error(y_test, y_pred_dt_test)\n",
    "mse_lr_test = mean_squared_error(y_test, y_pred_lr_test)\n",
    "\n",
    "print('Mean Squared Error of the Decision Tree model on the training set:', mse_dt)\n",
    "print('Mean Squared Error of the Linear Regression model on the training set:', mse_lr)\n",
    "\n",
    "print('Mean Squared Error of the Decision Tree model on the testing set:', mse_dt_test)\n",
    "print('Mean Squared Error of the Linear Regression model on the testing set:', mse_lr_test)\n",
    "\n",
    "#print feature importances\n",
    "df_importances = pd.DataFrame({'feature': X.columns, 'importance': dt.feature_importances_})\n",
    "\n",
    "df_importances.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances['importance'] = df_importances['importance'].map('{:.3}'.format)\n",
    "df_importances\n",
    "\n",
    "#joblib.dump(dt, 'decision_tree_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.5s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.8s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  22.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.7s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  22.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.5s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.7s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  24.7s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  24.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  24.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  24.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  23.4s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.5s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  23.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  22.6s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  21.3s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  21.0s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  23.9s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  23.9s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  24.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  26.8s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  27.2s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  26.1s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  26.2s\n",
      "[CV] END max_depth=25, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  25.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  22.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  22.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=180; total time=  21.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  22.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=180; total time=  21.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  24.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  23.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  20.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  23.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  23.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  19.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=3, n_estimators=200; total time=  23.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  19.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  19.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=180; total time=  19.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  22.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  20.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  22.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  21.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  22.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=2, n_estimators=200; total time=  22.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  19.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  19.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  19.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=180; total time=  19.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  21.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  21.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  21.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  22.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=3, min_samples_split=3, n_estimators=200; total time=  20.6s\n",
      "{'max_depth': 30, 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "0.8669406344971303\n",
      "RandomForestRegressor(max_depth=30, min_samples_leaf=2, min_samples_split=3,\n",
      "                      n_estimators=200)\n",
      "Mean Squared Error of the Random Forest model on the training set: 348.585572626957\n",
      "Mean Squared Error of the Random Forest model on the testing set: 4430.969846498873\n",
      "Mean Absolute Error of the Random Forest model on the training set: 10.740820316428138\n",
      "Mean Absolute Error of the Random Forest model on the testing set: 41.44032820576474\n"
     ]
    }
   ],
   "source": [
    "#GridsearchCV for random forest\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "#import MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [180, 200],\n",
    "    'max_depth': [25, 30],\n",
    "    'min_samples_leaf': [2, 3],\n",
    "    'min_samples_split': [2, 3]\n",
    "}\n",
    "\n",
    "# Initiate the random forest regressor\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "# Initiate the grid search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_rf = grid_search.best_estimator_.predict(X_train)\n",
    "y_pred_rf_test = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "mse_rf = mean_squared_error(y_train, y_pred_rf)\n",
    "mse_rf_test = mean_squared_error(y_test, y_pred_rf_test)\n",
    "\n",
    "mae_rf = mean_absolute_error(y_train, y_pred_rf)\n",
    "mae_rf_test = mean_absolute_error(y_test, y_pred_rf_test)\n",
    "\n",
    "print('Mean Squared Error of the Random Forest model on the training set:', mse_rf)\n",
    "print('Mean Squared Error of the Random Forest model on the testing set:', mse_rf_test)\n",
    "\n",
    "#calculate the mean absolute error of the model for both the training and testing set\n",
    "print('Mean Absolute Error of the Random Forest model on the training set:', mae_rf)\n",
    "print('Mean Absolute Error of the Random Forest model on the testing set:', mae_rf_test)\n",
    "\n",
    "\n",
    "#feature importance\n",
    "df_importances_rf = pd.DataFrame({'feature': X.columns, 'importance': grid_search.best_estimator_.feature_importances_})\n",
    "df_importances_rf.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_rf['importance'] = df_importances_rf['importance'].map('{:.3}'.format)\n",
    "df_importances_rf\n",
    "\n",
    "#select the 20 best features\n",
    "Selectkbest = SelectKBest(f_regression, k=20)\n",
    "X_new = Selectkbest.fit_transform(X_train, y_train)\n",
    "X_new_test = Selectkbest.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error of the Random Forest model on the training set: 608.5298843112427\n",
      "Mean Squared Error of the Random Forest model on the testing set: 6669.329588301871\n",
      "Mean Absolute Error of the Random Forest model on the training set: 15.024670746075978\n",
      "Mean Absolute Error of the Random Forest model on the testing set: 53.89340969666964\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cnt_lag24</td>\n",
       "      <td>0.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hr</td>\n",
       "      <td>0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cnt_rolling_avg_12</td>\n",
       "      <td>0.0945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>cnt_rolling_avg_24</td>\n",
       "      <td>0.0417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>registered_lag24</td>\n",
       "      <td>0.0376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ridability</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>registered_lag48</td>\n",
       "      <td>0.0162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>casual_lag48</td>\n",
       "      <td>0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>cnt_rolling_avg_48</td>\n",
       "      <td>0.0141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cnt_lag48</td>\n",
       "      <td>0.0136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>casual_lag24</td>\n",
       "      <td>0.0111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hum</td>\n",
       "      <td>0.00963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>0.00811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.00668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.00621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>0.00444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mnth</td>\n",
       "      <td>0.00353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.00188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.00147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.00111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature importance\n",
       "10           cnt_lag24      0.567\n",
       "3                   hr      0.129\n",
       "16  cnt_rolling_avg_12     0.0945\n",
       "17  cnt_rolling_avg_24     0.0417\n",
       "12    registered_lag24     0.0376\n",
       "19          ridability      0.017\n",
       "15    registered_lag48     0.0162\n",
       "14        casual_lag48     0.0151\n",
       "18  cnt_rolling_avg_48     0.0141\n",
       "13           cnt_lag48     0.0136\n",
       "11        casual_lag24     0.0111\n",
       "6                  hum    0.00963\n",
       "9         weathersit_3    0.00811\n",
       "5                atemp    0.00668\n",
       "4                 temp    0.00621\n",
       "7            windspeed    0.00444\n",
       "2                 mnth    0.00353\n",
       "1                   yr    0.00188\n",
       "8         weathersit_1    0.00147\n",
       "0               season    0.00111"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train final model with the 20 best features\n",
    "model = RandomForestRegressor(n_estimators=180, max_depth=30, min_samples_leaf=2, min_samples_split=3)\n",
    "model.fit(X_new, y_train)\n",
    "\n",
    "#predict the response on the training set\n",
    "y_pred_rf = model.predict(X_new)\n",
    "y_pred_rf_test = model.predict(X_new_test)\n",
    "\n",
    "#calculate the mean squared error of the model\n",
    "mse_rf = mean_squared_error(y_train, y_pred_rf)\n",
    "mse_rf_test = mean_squared_error(y_test, y_pred_rf_test)\n",
    "\n",
    "#calculate the mean absolute error of the model for both the training and testing set\n",
    "mae_rf = mean_absolute_error(y_train, y_pred_rf)\n",
    "mae_rf_test = mean_absolute_error(y_test, y_pred_rf_test)\n",
    "\n",
    "print('Mean Squared Error of the Random Forest model on the training set:', mse_rf)\n",
    "print('Mean Squared Error of the Random Forest model on the testing set:', mse_rf_test)\n",
    "\n",
    "print('Mean Absolute Error of the Random Forest model on the training set:', mae_rf)\n",
    "print('Mean Absolute Error of the Random Forest model on the testing set:', mae_rf_test)\n",
    "\n",
    "#feature importance\n",
    "df_importances_rf = pd.DataFrame({'feature': X.columns[Selectkbest.get_support()], 'importance': model.feature_importances_})\n",
    "df_importances_rf.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_rf['importance'] = df_importances_rf['importance'].map('{:.3}'.format)\n",
    "df_importances_rf\n",
    "\n",
    "#joblib.dump(model, 'random_forest_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time=  45.3s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time=  45.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time=  46.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time=  49.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time=  50.0s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time=  50.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time=  50.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=320; total time=  50.5s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time=  48.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=20, n_estimators=300; total time=  48.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time=  53.7s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time=  58.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time=  58.1s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time=  57.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time=  58.1s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=320; total time=  58.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time=  58.6s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time=  58.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time=  58.1s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  56.3s\n",
      "[CV] END .learning_rate=0.03, max_depth=25, n_estimators=300; total time= 1.0min\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  57.3s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  57.7s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  57.6s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time=  50.2s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  54.5s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time=  52.6s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time=  52.3s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time=  52.3s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=300; total time=  52.1s\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 1.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 1.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 1.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 1.1min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time=  58.6s\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time=  59.0s\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time=  58.8s\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=320; total time= 1.0min\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time=  56.9s\n",
      "[CV] END .learning_rate=0.05, max_depth=25, n_estimators=300; total time=  57.7s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time=  48.5s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time=  48.0s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time=  45.5s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time=  48.2s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=  47.4s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=320; total time=  50.0s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=  46.7s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=  47.7s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=  49.0s\n",
      "[CV] END .learning_rate=0.08, max_depth=20, n_estimators=300; total time=  47.3s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=  52.3s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=  54.4s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=  53.7s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=  53.4s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=320; total time=  51.2s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=  51.7s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=  42.6s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=  42.8s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=  37.8s\n",
      "[CV] END .learning_rate=0.08, max_depth=25, n_estimators=300; total time=  38.6s\n",
      "{'learning_rate': 0.05, 'max_depth': 20, 'n_estimators': 320}\n",
      "0.9001028642808006\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=20, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=320, n_jobs=None, num_parallel_tree=None,\n",
      "             predictor=None, random_state=None, ...)\n",
      "Mean Squared Error of the XGBoost model on the training set: 0.00023645082500562984\n",
      "Mean Squared Error of the XGBoost model on the testing set: 0.00029929078476634285\n",
      "Mean Absolute Error of the XGBoost model on the training set: 0.008149894821767648\n",
      "Mean Absolute Error of the XGBoost model on the testing set: 0.007695276059160873\n"
     ]
    }
   ],
   "source": [
    "#GridsearchCV for XGBoost\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [320, 300],\n",
    "    'max_depth': [20, 25],\n",
    "    'learning_rate': [0.03, 0.05, 0.08]\n",
    "}\n",
    "\n",
    "# Initiate the XGBoost regressor\n",
    "model = xgb.XGBRegressor()\n",
    "\n",
    "# Initiate the grid search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the grid search\n",
    "grid_search.fit(X, y)\n",
    "\n",
    "# Print the best parameters\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_xgb = grid_search.best_estimator_.predict(X_train)\n",
    "y_pred_xgb_test = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "mse_xgb = mean_squared_error(y_train, y_pred_xgb)\n",
    "mse_xgb_test = mean_squared_error(y_test, y_pred_xgb_test)\n",
    "\n",
    "mae_xgb = mean_absolute_error(y_train, y_pred_xgb)\n",
    "mae_xgb_test = mean_absolute_error(y_test, y_pred_xgb_test)\n",
    "\n",
    "print('Mean Squared Error of the XGBoost model on the training set:', mse_xgb)\n",
    "print('Mean Squared Error of the XGBoost model on the testing set:', mse_xgb_test)\n",
    "\n",
    "#print the mean absolute error of the model for both the training and testing set\n",
    "print('Mean Absolute Error of the XGBoost model on the training set:', mae_xgb)\n",
    "print('Mean Absolute Error of the XGBoost model on the testing set:', mae_xgb_test)\n",
    "\n",
    "# Feature importance\n",
    "df_importances_xgb = pd.DataFrame({'feature': X.columns, 'importance': grid_search.best_estimator_.feature_importances_})\n",
    "df_importances_xgb.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_xgb['importance'] = df_importances_xgb['importance'].map('{:.3}'.format)\n",
    "df_importances_xgb\n",
    "\n",
    "#remove all the features with importance below 0.000495\n",
    "df_importances_rf = df_importances_rf[df_importances_rf['importance'].astype(float) > 0.000495]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  35.7s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  36.2s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  36.5s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  36.7s\n",
      "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=320; total time=  36.8s\n",
      "{'learning_rate': 0.05, 'max_depth': 20, 'n_estimators': 320}\n",
      "0.9001028642808006\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=20, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=320, n_jobs=None, num_parallel_tree=None,\n",
      "             predictor=None, random_state=None, ...)\n",
      "Mean Squared Error of the XGBoost model on the training set: 0.00023645082500562984\n",
      "Mean Squared Error of the XGBoost model on the testing set: 0.00029929078476634285\n",
      "Mean Absolute Error of the XGBoost model on the training set: 0.008149894821767648\n",
      "Mean Absolute Error of the XGBoost model on the testing set: 0.007695276059160873\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['decision_tree_model.pkl']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create another XGBoost model with the best parameters and removing all the features with importance below the random variable\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [320],\n",
    "    'max_depth': [20],\n",
    "    'learning_rate': [0.05]\n",
    "}\n",
    "\n",
    "# Initiate the XGBoost regressor\n",
    "model = xgb.XGBRegressor()\n",
    "\n",
    "# Initiate the grid search\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the grid search\n",
    "grid_search.fit(X, y)\n",
    "\n",
    "# Print the best parameters\n",
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "# Calculate mean squared error of the train and test set and print feature importances\n",
    "y_pred_xgb = grid_search.best_estimator_.predict(X_train)\n",
    "y_pred_xgb_test = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "mse_xgb = mean_squared_error(y_train, y_pred_xgb)\n",
    "mse_xgb_test = mean_squared_error(y_test, y_pred_xgb_test)\n",
    "\n",
    "mae_xgb = mean_absolute_error(y_train, y_pred_xgb)\n",
    "mae_xgb_test = mean_absolute_error(y_test, y_pred_xgb_test)\n",
    "\n",
    "print('Mean Squared Error of the XGBoost model on the training set:', mse_xgb)\n",
    "print('Mean Squared Error of the XGBoost model on the testing set:', mse_xgb_test)\n",
    "\n",
    "#print the mean absolute error of the model for both the training and testing set\n",
    "print('Mean Absolute Error of the XGBoost model on the training set:', mae_xgb)\n",
    "print('Mean Absolute Error of the XGBoost model on the testing set:', mae_xgb_test)\n",
    "\n",
    "# Feature importance\n",
    "df_importances_xgb = pd.DataFrame({'feature': X.columns, 'importance': grid_search.best_estimator_.feature_importances_})\n",
    "df_importances_xgb.sort_values(by='importance', ascending=False, inplace=True)\n",
    "df_importances_xgb['importance'] = df_importances_xgb['importance'].map('{:.3}'.format)\n",
    "df_importances_xgb\n",
    "\n",
    "joblib.dump(grid_search.best_estimator_, 'decision_tree_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69e4af7-0cc1-4488-83ad-33662479edad",
   "metadata": {},
   "source": [
    "## PART III: Streamlit dashboard"
   ]
  }
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