Linear Regression Assignment-bikes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b316739",
   "metadata": {},
   "source": [
    "Step1: Reading and understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "id": "87ac8afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "id": "62d6908c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>18.18125</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  01-01-2018       1   0     1        0        6           0   \n",
       "1        2  02-01-2018       1   0     1        0        0           0   \n",
       "2        3  03-01-2018       1   0     1        0        1           1   \n",
       "3        4  04-01-2018       1   0     1        0        2           1   \n",
       "4        5  05-01-2018       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit       temp     atemp      hum  windspeed  casual  registered  \\\n",
       "0           2  14.110847  18.18125  80.5833  10.749882     331         654   \n",
       "1           2  14.902598  17.68695  69.6087  16.652113     131         670   \n",
       "2           1   8.050924   9.47025  43.7273  16.636703     120        1229   \n",
       "3           1   8.200000  10.60610  59.0435  10.739832     108        1454   \n",
       "4           1   9.305237  11.46350  43.6957  12.522300      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike = pd.read_csv('day.csv')\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "id": "d3f631d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730, 16)"
      ]
     },
     "execution_count": 1048,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "id": "4b689f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     730 non-null    int64  \n",
      " 1   dteday      730 non-null    object \n",
      " 2   season      730 non-null    int64  \n",
      " 3   yr          730 non-null    int64  \n",
      " 4   mnth        730 non-null    int64  \n",
      " 5   holiday     730 non-null    int64  \n",
      " 6   weekday     730 non-null    int64  \n",
      " 7   workingday  730 non-null    int64  \n",
      " 8   weathersit  730 non-null    int64  \n",
      " 9   temp        730 non-null    float64\n",
      " 10  atemp       730 non-null    float64\n",
      " 11  hum         730 non-null    float64\n",
      " 12  windspeed   730 non-null    float64\n",
      " 13  casual      730 non-null    int64  \n",
      " 14  registered  730 non-null    int64  \n",
      " 15  cnt         730 non-null    int64  \n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "id": "62a89410",
   "metadata": {},
   "outputs": [
    {
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       "  <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>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",
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       "      <th>cnt</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>365.500000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.028767</td>\n",
       "      <td>2.997260</td>\n",
       "      <td>0.683562</td>\n",
       "      <td>1.394521</td>\n",
       "      <td>20.319259</td>\n",
       "      <td>23.726322</td>\n",
       "      <td>62.765175</td>\n",
       "      <td>12.763620</td>\n",
       "      <td>849.249315</td>\n",
       "      <td>3658.757534</td>\n",
       "      <td>4508.006849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>210.877136</td>\n",
       "      <td>1.110184</td>\n",
       "      <td>0.500343</td>\n",
       "      <td>3.450215</td>\n",
       "      <td>0.167266</td>\n",
       "      <td>2.006161</td>\n",
       "      <td>0.465405</td>\n",
       "      <td>0.544807</td>\n",
       "      <td>7.506729</td>\n",
       "      <td>8.150308</td>\n",
       "      <td>14.237589</td>\n",
       "      <td>5.195841</td>\n",
       "      <td>686.479875</td>\n",
       "      <td>1559.758728</td>\n",
       "      <td>1936.011647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>2.424346</td>\n",
       "      <td>3.953480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500244</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.811885</td>\n",
       "      <td>16.889713</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>9.041650</td>\n",
       "      <td>316.250000</td>\n",
       "      <td>2502.250000</td>\n",
       "      <td>3169.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>365.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.465826</td>\n",
       "      <td>24.368225</td>\n",
       "      <td>62.625000</td>\n",
       "      <td>12.125325</td>\n",
       "      <td>717.000000</td>\n",
       "      <td>3664.500000</td>\n",
       "      <td>4548.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>547.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.880615</td>\n",
       "      <td>30.445775</td>\n",
       "      <td>72.989575</td>\n",
       "      <td>15.625589</td>\n",
       "      <td>1096.500000</td>\n",
       "      <td>4783.250000</td>\n",
       "      <td>5966.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>730.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.328347</td>\n",
       "      <td>42.044800</td>\n",
       "      <td>97.250000</td>\n",
       "      <td>34.000021</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  730.000000  730.000000  730.000000  730.000000  730.000000  730.000000   \n",
       "mean   365.500000    2.498630    0.500000    6.526027    0.028767    2.997260   \n",
       "std    210.877136    1.110184    0.500343    3.450215    0.167266    2.006161   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.250000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    365.500000    3.000000    0.500000    7.000000    0.000000    3.000000   \n",
       "75%    547.750000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    730.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  730.000000  730.000000  730.000000  730.000000  730.000000  730.000000   \n",
       "mean     0.683562    1.394521   20.319259   23.726322   62.765175   12.763620   \n",
       "std      0.465405    0.544807    7.506729    8.150308   14.237589    5.195841   \n",
       "min      0.000000    1.000000    2.424346    3.953480    0.000000    1.500244   \n",
       "25%      0.000000    1.000000   13.811885   16.889713   52.000000    9.041650   \n",
       "50%      1.000000    1.000000   20.465826   24.368225   62.625000   12.125325   \n",
       "75%      1.000000    2.000000   26.880615   30.445775   72.989575   15.625589   \n",
       "max      1.000000    3.000000   35.328347   42.044800   97.250000   34.000021   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   730.000000   730.000000   730.000000  \n",
       "mean    849.249315  3658.757534  4508.006849  \n",
       "std     686.479875  1559.758728  1936.011647  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     316.250000  2502.250000  3169.750000  \n",
       "50%     717.000000  3664.500000  4548.500000  \n",
       "75%    1096.500000  4783.250000  5966.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 1050,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "id": "495b8516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
      "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
      "       'casual', 'registered', 'cnt'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bike.columns)  #checking all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "id": "dcb9a429",
   "metadata": {},
   "outputs": [],
   "source": [
    "bike.rename(columns={'season':'Season','yr':'Year','mnth':'Month','hum':'Humidity',\n",
    "                     'weathersit':'Weather','cnt':'Count'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "id": "93148290",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
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       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>Count</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>18.18125</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  Season  Year  Month  holiday  weekday  workingday  \\\n",
       "0        1  01-01-2018       1     0      1        0        6           0   \n",
       "1        2  02-01-2018       1     0      1        0        0           0   \n",
       "2        3  03-01-2018       1     0      1        0        1           1   \n",
       "3        4  04-01-2018       1     0      1        0        2           1   \n",
       "4        5  05-01-2018       1     0      1        0        3           1   \n",
       "\n",
       "   Weather       temp     atemp  Humidity  windspeed  casual  registered  \\\n",
       "0        2  14.110847  18.18125   80.5833  10.749882     331         654   \n",
       "1        2  14.902598  17.68695   69.6087  16.652113     131         670   \n",
       "2        1   8.050924   9.47025   43.7273  16.636703     120        1229   \n",
       "3        1   8.200000  10.60610   59.0435  10.739832     108        1454   \n",
       "4        1   9.305237  11.46350   43.6957  12.522300      82        1518   \n",
       "\n",
       "   Count  \n",
       "0    985  \n",
       "1    801  \n",
       "2   1349  \n",
       "3   1562  \n",
       "4   1600  "
      ]
     },
     "execution_count": 1053,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "id": "ce63e4ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instant       0\n",
      "dteday        0\n",
      "Season        0\n",
      "Year          0\n",
      "Month         0\n",
      "holiday       0\n",
      "weekday       0\n",
      "workingday    0\n",
      "Weather       0\n",
      "temp          0\n",
      "atemp         0\n",
      "Humidity      0\n",
      "windspeed     0\n",
      "casual        0\n",
      "registered    0\n",
      "Count         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "count = bike.isnull().sum()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11863ce1",
   "metadata": {},
   "source": [
    "Step2: Exploratory Data Anlysis\n",
    "Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "id": "f9100494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     730 non-null    int64  \n",
      " 1   dteday      730 non-null    object \n",
      " 2   Season      730 non-null    int64  \n",
      " 3   Year        730 non-null    int64  \n",
      " 4   Month       730 non-null    int64  \n",
      " 5   holiday     730 non-null    int64  \n",
      " 6   weekday     730 non-null    int64  \n",
      " 7   workingday  730 non-null    int64  \n",
      " 8   Weather     730 non-null    int64  \n",
      " 9   temp        730 non-null    float64\n",
      " 10  atemp       730 non-null    float64\n",
      " 11  Humidity    730 non-null    float64\n",
      " 12  windspeed   730 non-null    float64\n",
      " 13  casual      730 non-null    int64  \n",
      " 14  registered  730 non-null    int64  \n",
      " 15  Count       730 non-null    int64  \n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "id": "c510bf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
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       "      <td>01-01-2018</td>\n",
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       "      <td>801</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
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       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  Season  Year  Month  holiday  weekday  workingday  Weather  \\\n",
       "0  01-01-2018       1     0      1        0        6           0        2   \n",
       "1  02-01-2018       1     0      1        0        0           0        2   \n",
       "2  03-01-2018       1     0      1        0        1           1        1   \n",
       "3  04-01-2018       1     0      1        0        2           1        1   \n",
       "4  05-01-2018       1     0      1        0        3           1        1   \n",
       "\n",
       "        temp     atemp  Humidity  windspeed  casual  registered  Count  \n",
       "0  14.110847  18.18125   80.5833  10.749882     331         654    985  \n",
       "1  14.902598  17.68695   69.6087  16.652113     131         670    801  \n",
       "2   8.050924   9.47025   43.7273  16.636703     120        1229   1349  \n",
       "3   8.200000  10.60610   59.0435  10.739832     108        1454   1562  \n",
       "4   9.305237  11.46350   43.6957  12.522300      82        1518   1600  "
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.drop(['instant'],axis=1,inplace=True)\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "id": "61028808",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday  weekday  workingday  Weather       temp  \\\n",
       "0       1     0      1        0        6           0        2  14.110847   \n",
       "1       1     0      1        0        0           0        2  14.902598   \n",
       "2       1     0      1        0        1           1        1   8.050924   \n",
       "3       1     0      1        0        2           1        1   8.200000   \n",
       "4       1     0      1        0        3           1        1   9.305237   \n",
       "\n",
       "      atemp  Humidity  windspeed  casual  registered  Count  \n",
       "0  18.18125   80.5833  10.749882     331         654    985  \n",
       "1  17.68695   69.6087  16.652113     131         670    801  \n",
       "2   9.47025   43.7273  16.636703     120        1229   1349  \n",
       "3  10.60610   59.0435  10.739832     108        1454   1562  \n",
       "4  11.46350   43.6957  12.522300      82        1518   1600  "
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.drop(['dteday'],axis=1,inplace=True)\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "id": "ae75a7af",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday  weekday  workingday  Weather       temp  \\\n",
       "0       1     0      1        0        6           0        2  14.110847   \n",
       "1       1     0      1        0        0           0        2  14.902598   \n",
       "2       1     0      1        0        1           1        1   8.050924   \n",
       "3       1     0      1        0        2           1        1   8.200000   \n",
       "4       1     0      1        0        3           1        1   9.305237   \n",
       "\n",
       "      atemp  Humidity  windspeed  Count  \n",
       "0  18.18125   80.5833  10.749882    985  \n",
       "1  17.68695   69.6087  16.652113    801  \n",
       "2   9.47025   43.7273  16.636703   1349  \n",
       "3  10.60610   59.0435  10.739832   1562  \n",
       "4  11.46350   43.6957  12.522300   1600  "
      ]
     },
     "execution_count": 1058,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.drop(['casual','registered'],axis=1,inplace=True)\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "id": "67e9b9d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Season      730 non-null    int64  \n",
      " 1   Year        730 non-null    int64  \n",
      " 2   Month       730 non-null    int64  \n",
      " 3   holiday     730 non-null    int64  \n",
      " 4   weekday     730 non-null    int64  \n",
      " 5   workingday  730 non-null    int64  \n",
      " 6   Weather     730 non-null    int64  \n",
      " 7   temp        730 non-null    float64\n",
      " 8   atemp       730 non-null    float64\n",
      " 9   Humidity    730 non-null    float64\n",
      " 10  windspeed   730 non-null    float64\n",
      " 11  Count       730 non-null    int64  \n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 68.6 KB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "id": "3f80b919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>Weather</th>\n",
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       "      <td>0</td>\n",
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       "      <td>14.902598</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.378268</td>\n",
       "      <td>11.66045</td>\n",
       "      <td>51.8261</td>\n",
       "      <td>6.000868</td>\n",
       "      <td>1606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.057402</td>\n",
       "      <td>10.44195</td>\n",
       "      <td>49.8696</td>\n",
       "      <td>11.304642</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.765000</td>\n",
       "      <td>8.11270</td>\n",
       "      <td>53.5833</td>\n",
       "      <td>17.875868</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.671653</td>\n",
       "      <td>5.80875</td>\n",
       "      <td>43.4167</td>\n",
       "      <td>24.250650</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.184153</td>\n",
       "      <td>7.54440</td>\n",
       "      <td>48.2917</td>\n",
       "      <td>14.958889</td>\n",
       "      <td>1321</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday  weekday  workingday  Weather       temp  \\\n",
       "0  spring     0      1        0        6           0        2  14.110847   \n",
       "1  spring     0      1        0        0           0        2  14.902598   \n",
       "2  spring     0      1        0        1           1        1   8.050924   \n",
       "3  spring     0      1        0        2           1        1   8.200000   \n",
       "4  spring     0      1        0        3           1        1   9.305237   \n",
       "5  spring     0      1        0        4           1        1   8.378268   \n",
       "6  spring     0      1        0        5           1        2   8.057402   \n",
       "7  spring     0      1        0        6           0        2   6.765000   \n",
       "8  spring     0      1        0        0           0        1   5.671653   \n",
       "9  spring     0      1        0        1           1        1   6.184153   \n",
       "\n",
       "      atemp  Humidity  windspeed  Count  \n",
       "0  18.18125   80.5833  10.749882    985  \n",
       "1  17.68695   69.6087  16.652113    801  \n",
       "2   9.47025   43.7273  16.636703   1349  \n",
       "3  10.60610   59.0435  10.739832   1562  \n",
       "4  11.46350   43.6957  12.522300   1600  \n",
       "5  11.66045   51.8261   6.000868   1606  \n",
       "6  10.44195   49.8696  11.304642   1510  \n",
       "7   8.11270   53.5833  17.875868    959  \n",
       "8   5.80875   43.4167  24.250650    822  \n",
       "9   7.54440   48.2917  14.958889   1321  "
      ]
     },
     "execution_count": 1060,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike['Season'].replace({1:\"spring\",2:\"summer\",3:\"fall\",4:\"winter\"},inplace=True)\n",
    "bike.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "id": "28bb277d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>Humidity</th>\n",
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       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
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       "      <td>16.636703</td>\n",
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       "    <tr>\n",
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       "      <td>Clear_Few Clouds</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
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       "    <tr>\n",
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       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>11.66045</td>\n",
       "      <td>51.8261</td>\n",
       "      <td>6.000868</td>\n",
       "      <td>1606</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1</td>\n",
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       "      <td>11.304642</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>6.765000</td>\n",
       "      <td>8.11270</td>\n",
       "      <td>53.5833</td>\n",
       "      <td>17.875868</td>\n",
       "      <td>959</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>5.671653</td>\n",
       "      <td>5.80875</td>\n",
       "      <td>43.4167</td>\n",
       "      <td>24.250650</td>\n",
       "      <td>822</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
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       "      <td>7.54440</td>\n",
       "      <td>48.2917</td>\n",
       "      <td>14.958889</td>\n",
       "      <td>1321</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday  weekday  workingday           Weather  \\\n",
       "0  spring     0      1        0        6           0       Mist_cloudy   \n",
       "1  spring     0      1        0        0           0       Mist_cloudy   \n",
       "2  spring     0      1        0        1           1  Clear_Few Clouds   \n",
       "3  spring     0      1        0        2           1  Clear_Few Clouds   \n",
       "4  spring     0      1        0        3           1  Clear_Few Clouds   \n",
       "5  spring     0      1        0        4           1  Clear_Few Clouds   \n",
       "6  spring     0      1        0        5           1       Mist_cloudy   \n",
       "7  spring     0      1        0        6           0       Mist_cloudy   \n",
       "8  spring     0      1        0        0           0  Clear_Few Clouds   \n",
       "9  spring     0      1        0        1           1  Clear_Few Clouds   \n",
       "\n",
       "        temp     atemp  Humidity  windspeed  Count  \n",
       "0  14.110847  18.18125   80.5833  10.749882    985  \n",
       "1  14.902598  17.68695   69.6087  16.652113    801  \n",
       "2   8.050924   9.47025   43.7273  16.636703   1349  \n",
       "3   8.200000  10.60610   59.0435  10.739832   1562  \n",
       "4   9.305237  11.46350   43.6957  12.522300   1600  \n",
       "5   8.378268  11.66045   51.8261   6.000868   1606  \n",
       "6   8.057402  10.44195   49.8696  11.304642   1510  \n",
       "7   6.765000   8.11270   53.5833  17.875868    959  \n",
       "8   5.671653   5.80875   43.4167  24.250650    822  \n",
       "9   6.184153   7.54440   48.2917  14.958889   1321  "
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike['Weather'].replace({1:\"Clear_Few Clouds\",2:\"Mist_cloudy\",3:\"Light rain_Light snow_Thunderstorm\",4:'Heavy Rain_Ice Pallets_Thunderstorm_Mist'},inplace=True)\n",
    "bike.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "id": "dea48d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>Weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>18.18125</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>8.378268</td>\n",
       "      <td>11.66045</td>\n",
       "      <td>51.8261</td>\n",
       "      <td>6.000868</td>\n",
       "      <td>1606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>8.057402</td>\n",
       "      <td>10.44195</td>\n",
       "      <td>49.8696</td>\n",
       "      <td>11.304642</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>6.765000</td>\n",
       "      <td>8.11270</td>\n",
       "      <td>53.5833</td>\n",
       "      <td>17.875868</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>5.671653</td>\n",
       "      <td>5.80875</td>\n",
       "      <td>43.4167</td>\n",
       "      <td>24.250650</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>6.184153</td>\n",
       "      <td>7.54440</td>\n",
       "      <td>48.2917</td>\n",
       "      <td>14.958889</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday    weekday  workingday           Weather  \\\n",
       "0  spring     0      1        0   Saturday           0       Mist_cloudy   \n",
       "1  spring     0      1        0     Sunday           0       Mist_cloudy   \n",
       "2  spring     0      1        0     Monday           1  Clear_Few Clouds   \n",
       "3  spring     0      1        0    Tuesday           1  Clear_Few Clouds   \n",
       "4  spring     0      1        0  Wednesday           1  Clear_Few Clouds   \n",
       "5  spring     0      1        0   Thursday           1  Clear_Few Clouds   \n",
       "6  spring     0      1        0     Friday           1       Mist_cloudy   \n",
       "7  spring     0      1        0   Saturday           0       Mist_cloudy   \n",
       "8  spring     0      1        0     Sunday           0  Clear_Few Clouds   \n",
       "9  spring     0      1        0     Monday           1  Clear_Few Clouds   \n",
       "\n",
       "        temp     atemp  Humidity  windspeed  Count  \n",
       "0  14.110847  18.18125   80.5833  10.749882    985  \n",
       "1  14.902598  17.68695   69.6087  16.652113    801  \n",
       "2   8.050924   9.47025   43.7273  16.636703   1349  \n",
       "3   8.200000  10.60610   59.0435  10.739832   1562  \n",
       "4   9.305237  11.46350   43.6957  12.522300   1600  \n",
       "5   8.378268  11.66045   51.8261   6.000868   1606  \n",
       "6   8.057402  10.44195   49.8696  11.304642   1510  \n",
       "7   6.765000   8.11270   53.5833  17.875868    959  \n",
       "8   5.671653   5.80875   43.4167  24.250650    822  \n",
       "9   6.184153   7.54440   48.2917  14.958889   1321  "
      ]
     },
     "execution_count": 1062,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike['weekday'].replace({0:\"Sunday\",1:\"Monday\",2:\"Tuesday\",3:\"Wednesday\",4:\"Thursday\",5:\"Friday\",6:\"Saturday\"},inplace=True)\n",
    "bike.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "id": "4ff55a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Season      730 non-null    object \n",
      " 1   Year        730 non-null    int64  \n",
      " 2   Month       730 non-null    int64  \n",
      " 3   holiday     730 non-null    int64  \n",
      " 4   weekday     730 non-null    object \n",
      " 5   workingday  730 non-null    int64  \n",
      " 6   Weather     730 non-null    object \n",
      " 7   temp        730 non-null    float64\n",
      " 8   atemp       730 non-null    float64\n",
      " 9   Humidity    730 non-null    float64\n",
      " 10  windspeed   730 non-null    float64\n",
      " 11  Count       730 non-null    int64  \n",
      "dtypes: float64(4), int64(5), object(3)\n",
      "memory usage: 68.6+ KB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "id": "ef923435",
   "metadata": {},
   "outputs": [],
   "source": [
    "bike[['temp','atemp','Humidity','windspeed','Count']]=bike[['temp','atemp','Humidity','windspeed','Count']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "id": "17dc0d55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Season      730 non-null    object \n",
      " 1   Year        730 non-null    int64  \n",
      " 2   Month       730 non-null    int64  \n",
      " 3   holiday     730 non-null    int64  \n",
      " 4   weekday     730 non-null    object \n",
      " 5   workingday  730 non-null    int64  \n",
      " 6   Weather     730 non-null    object \n",
      " 7   temp        730 non-null    float64\n",
      " 8   atemp       730 non-null    float64\n",
      " 9   Humidity    730 non-null    float64\n",
      " 10  windspeed   730 non-null    float64\n",
      " 11  Count       730 non-null    int64  \n",
      "dtypes: float64(4), int64(5), object(3)\n",
      "memory usage: 68.6+ KB\n"
     ]
    }
   ],
   "source": [
    "bike.head()\n",
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "id": "cfe3f2a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Performing EDA and understand numirical variables\n",
    "sns.pairplot(bike, x_vars=['temp','atemp','Humidity','windspeed',\"Count\"], y_vars='Count', size=4, aspect=1, kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "id": "bb888a8b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'spring'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1067], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(bike\u001b[38;5;241m.\u001b[39mcorr(), annot \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYlGnBu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32mE:\\anaconda\\Lib\\site-packages\\pandas\\core\\frame.py:10054\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  10052\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  10053\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 10054\u001b[0m mat \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m, na_value\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnan, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m  10056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  10057\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32mE:\\anaconda\\Lib\\site-packages\\pandas\\core\\frame.py:1838\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1837\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1838\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mas_array(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1840\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(result, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mE:\\anaconda\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1732\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1730\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1732\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interleave(dtype\u001b[38;5;241m=\u001b[39mdtype, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1736\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32mE:\\anaconda\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1794\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1792\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1793\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1794\u001b[0m     result[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1795\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1797\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'spring'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 10))\n",
    "sns.heatmap(bike.corr(), annot = True, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "id": "2ef5ca12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>Weather</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>18.18125</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>17.68695</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>9.47025</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>10.60610</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear_Few Clouds</td>\n",
       "      <td>11.46350</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  Year  Month  holiday    weekday  workingday           Weather  \\\n",
       "0  spring     0      1        0   Saturday           0       Mist_cloudy   \n",
       "1  spring     0      1        0     Sunday           0       Mist_cloudy   \n",
       "2  spring     0      1        0     Monday           1  Clear_Few Clouds   \n",
       "3  spring     0      1        0    Tuesday           1  Clear_Few Clouds   \n",
       "4  spring     0      1        0  Wednesday           1  Clear_Few Clouds   \n",
       "\n",
       "      atemp  Humidity  windspeed  Count  \n",
       "0  18.18125   80.5833  10.749882    985  \n",
       "1  17.68695   69.6087  16.652113    801  \n",
       "2   9.47025   43.7273  16.636703   1349  \n",
       "3  10.60610   59.0435  10.739832   1562  \n",
       "4  11.46350   43.6957  12.522300   1600  "
      ]
     },
     "execution_count": 1068,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.drop(['temp'],axis=1,inplace=True)\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "id": "e6cc5f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising categorical Variables to understand data better\n",
    "plt.figure(figsize=(30, 15))\n",
    "plt.subplot(3,3,1)\n",
    "sns.boxplot(x = 'Season', y = 'Count', data = bike)#yr','mnth','workingday','weathersit','weekday'\n",
    "plt.subplot(3,3,2)\n",
    "sns.boxplot(x = 'Year', y = 'Count', data = bike)\n",
    "plt.subplot(3,3,3)\n",
    "sns.boxplot(x = 'Month', y = 'Count', data = bike)\n",
    "plt.subplot(3,3,4)\n",
    "sns.boxplot(x = 'workingday', y = 'Count', data = bike)\n",
    "plt.subplot(3,3,5)\n",
    "sns.boxplot(x = 'Weather', y = 'Count', data = bike)\n",
    "plt.subplot(3,3,6)\n",
    "sns.boxplot(x = 'weekday', y = 'Count', data = bike)\n",
    "plt.subplot(3,3,7)\n",
    "sns.boxplot(x = 'holiday', y = 'Count', data = bike)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2eb41ea",
   "metadata": {},
   "source": [
    "Step3: Creating dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "id": "cb64fac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Season      730 non-null    object \n",
      " 1   Year        730 non-null    int64  \n",
      " 2   Month       730 non-null    object \n",
      " 3   holiday     730 non-null    int64  \n",
      " 4   weekday     730 non-null    object \n",
      " 5   workingday  730 non-null    int64  \n",
      " 6   Weather     730 non-null    object \n",
      " 7   atemp       730 non-null    float64\n",
      " 8   Humidity    730 non-null    float64\n",
      " 9   windspeed   730 non-null    float64\n",
      " 10  Count       730 non-null    int64  \n",
      "dtypes: float64(3), int64(4), object(4)\n",
      "memory usage: 62.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#Convert variables to object type\n",
    "bike['Month']=bike['Month'].astype(object)\n",
    "bike['Season']=bike['Season'].astype(object)\n",
    "bike['Weather']=bike['Weather'].astype(object)\n",
    "bike['weekday']=bike['weekday'].astype(object)\n",
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "id": "52570ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATING DUMMY VARIABLES FOR CATEGORICAL DATA \n",
    "\n",
    "Season_condition=pd.get_dummies(bike['Season'],drop_first=True)\n",
    "Weather_condition=pd.get_dummies(bike['Weather'],drop_first=True)\n",
    "Day_of_week=pd.get_dummies(bike['weekday'],drop_first=True)\n",
    "Month=pd.get_dummies(bike['Month'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "id": "309081ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 730 entries, 0 to 729\n",
      "Data columns (total 33 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   Season                              730 non-null    object \n",
      " 1   Year                                730 non-null    int64  \n",
      " 2   Month                               730 non-null    object \n",
      " 3   holiday                             730 non-null    int64  \n",
      " 4   weekday                             730 non-null    object \n",
      " 5   workingday                          730 non-null    int64  \n",
      " 6   Weather                             730 non-null    object \n",
      " 7   atemp                               730 non-null    float64\n",
      " 8   Humidity                            730 non-null    float64\n",
      " 9   windspeed                           730 non-null    float64\n",
      " 10  Count                               730 non-null    int64  \n",
      " 11  spring                              730 non-null    bool   \n",
      " 12  summer                              730 non-null    bool   \n",
      " 13  winter                              730 non-null    bool   \n",
      " 14  Light rain_Light snow_Thunderstorm  730 non-null    bool   \n",
      " 15  Mist_cloudy                         730 non-null    bool   \n",
      " 16  Monday                              730 non-null    bool   \n",
      " 17  Saturday                            730 non-null    bool   \n",
      " 18  Sunday                              730 non-null    bool   \n",
      " 19  Thursday                            730 non-null    bool   \n",
      " 20  Tuesday                             730 non-null    bool   \n",
      " 21  Wednesday                           730 non-null    bool   \n",
      " 22  2                                   730 non-null    bool   \n",
      " 23  3                                   730 non-null    bool   \n",
      " 24  4                                   730 non-null    bool   \n",
      " 25  5                                   730 non-null    bool   \n",
      " 26  6                                   730 non-null    bool   \n",
      " 27  7                                   730 non-null    bool   \n",
      " 28  8                                   730 non-null    bool   \n",
      " 29  9                                   730 non-null    bool   \n",
      " 30  10                                  730 non-null    bool   \n",
      " 31  11                                  730 non-null    bool   \n",
      " 32  12                                  730 non-null    bool   \n",
      "dtypes: bool(22), float64(3), int64(4), object(4)\n",
      "memory usage: 78.5+ KB\n"
     ]
    }
   ],
   "source": [
    "bike=pd.concat([bike,Season_condition],axis=1)\n",
    "bike=pd.concat([bike,Weather_condition],axis=1)\n",
    "bike=pd.concat([bike,Day_of_week],axis=1)\n",
    "bike=pd.concat([bike,Month],axis=1)\n",
    "bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "id": "d6ec23d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>Count</th>\n",
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       "      <th>winter</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>1562</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>12.522300</td>\n",
       "      <td>1600</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  holiday  workingday     atemp  Humidity  windspeed  Count  spring  \\\n",
       "0     0        0           0  18.18125   80.5833  10.749882    985    True   \n",
       "1     0        0           0  17.68695   69.6087  16.652113    801    True   \n",
       "2     0        0           1   9.47025   43.7273  16.636703   1349    True   \n",
       "3     0        0           1  10.60610   59.0435  10.739832   1562    True   \n",
       "4     0        0           1  11.46350   43.6957  12.522300   1600    True   \n",
       "\n",
       "   summer  winter  ...      3      4      5      6      7      8      9  \\\n",
       "0   False   False  ...  False  False  False  False  False  False  False   \n",
       "1   False   False  ...  False  False  False  False  False  False  False   \n",
       "2   False   False  ...  False  False  False  False  False  False  False   \n",
       "3   False   False  ...  False  False  False  False  False  False  False   \n",
       "4   False   False  ...  False  False  False  False  False  False  False   \n",
       "\n",
       "      10     11     12  \n",
       "0  False  False  False  \n",
       "1  False  False  False  \n",
       "2  False  False  False  \n",
       "3  False  False  False  \n",
       "4  False  False  False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 1073,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting the orginal columns season.weathersit,weekday,mnth\n",
    "bike.drop(['Season'],axis=1,inplace=True)\n",
    "bike.drop(['Weather'],axis=1,inplace=True)\n",
    "\n",
    "bike.drop(['weekday'],axis=1,inplace=True)\n",
    "\n",
    "bike.drop(['Month'],axis=1,inplace=True)\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4617cd9",
   "metadata": {},
   "source": [
    "# Train - Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "id": "3bb541f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "np.random.seed(0)\n",
    "bike_train, bike_test = train_test_split(bike, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "id": "18313487",
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "     Year  holiday  workingday    atemp  Humidity  windspeed  Count  spring  \\\n",
       "653     1        0           1  23.0423   55.8333  12.208807   7534   False   \n",
       "576     1        0           1  33.1448   70.4167  11.083475   7216   False   \n",
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       "482     1        0           0  18.8752   48.9583   8.708325   4220   False   \n",
       "\n",
       "     summer  winter  ...      3      4      5      6      7      8      9  \\\n",
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       "576   False   False  ...  False  False  False  False   True  False  False   \n",
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       "\n",
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       "426  False  False  False  \n",
       "728  False  False   True  \n",
       "482  False  False  False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 1075,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "id": "168761c3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>33.27085</td>\n",
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       "      <td>5.459106</td>\n",
       "      <td>6043</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36.04875</td>\n",
       "      <td>59.2083</td>\n",
       "      <td>7.625404</td>\n",
       "      <td>6211</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.85230</td>\n",
       "      <td>81.2917</td>\n",
       "      <td>13.250121</td>\n",
       "      <td>2659</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34.24915</td>\n",
       "      <td>42.4167</td>\n",
       "      <td>13.417286</td>\n",
       "      <td>4780</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.19710</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>19.583229</td>\n",
       "      <td>4968</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  workingday     atemp  Humidity  windspeed  Count  spring  \\\n",
       "184     0        1           0  33.27085   63.7917   5.459106   6043   False   \n",
       "535     1        0           1  36.04875   59.2083   7.625404   6211   False   \n",
       "299     0        0           1  22.85230   81.2917  13.250121   2659   False   \n",
       "221     0        0           1  34.24915   42.4167  13.417286   4780   False   \n",
       "152     0        0           1  32.19710   30.5000  19.583229   4968   False   \n",
       "\n",
       "     summer  winter  ...      3      4      5      6      7      8      9  \\\n",
       "184   False   False  ...  False  False  False  False   True  False  False   \n",
       "535    True   False  ...  False  False  False   True  False  False  False   \n",
       "299   False    True  ...  False  False  False  False  False  False  False   \n",
       "221   False   False  ...  False  False  False  False  False   True  False   \n",
       "152    True   False  ...  False  False  False   True  False  False  False   \n",
       "\n",
       "        10     11     12  \n",
       "184  False  False  False  \n",
       "535  False  False  False  \n",
       "299   True  False  False  \n",
       "221  False  False  False  \n",
       "152  False  False  False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 1076,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "id": "a0797ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                              'Year',\n",
       "                                  'holiday',\n",
       "                               'workingday',\n",
       "                                    'atemp',\n",
       "                                 'Humidity',\n",
       "                                'windspeed',\n",
       "                                    'Count',\n",
       "                                   'spring',\n",
       "                                   'summer',\n",
       "                                   'winter',\n",
       "       'Light rain_Light snow_Thunderstorm',\n",
       "                              'Mist_cloudy',\n",
       "                                   'Monday',\n",
       "                                 'Saturday',\n",
       "                                   'Sunday',\n",
       "                                 'Thursday',\n",
       "                                  'Tuesday',\n",
       "                                'Wednesday',\n",
       "                                          2,\n",
       "                                          3,\n",
       "                                          4,\n",
       "                                          5,\n",
       "                                          6,\n",
       "                                          7,\n",
       "                                          8,\n",
       "                                          9,\n",
       "                                         10,\n",
       "                                         11,\n",
       "                                         12],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "id": "666106e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SCALING THE NUMERICAL DATA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "id": "370a6075",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_vars=['atemp','Humidity','windspeed','Count']\n",
    "bike_train[num_vars] = scaler.fit_transform(bike_train[num_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "id": "e59f0a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
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       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
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       "      <th>3</th>\n",
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       "      <td>False</td>\n",
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       "      <th>728</th>\n",
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       "      <td>0.498067</td>\n",
       "      <td>0.663106</td>\n",
       "      <td>0.204096</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  workingday     atemp  Humidity  windspeed     Count  \\\n",
       "653     1        0           1  0.501133  0.575354   0.300794  0.864243   \n",
       "576     1        0           1  0.766351  0.725633   0.264686  0.827658   \n",
       "426     1        0           0  0.438975  0.640189   0.255342  0.465255   \n",
       "728     1        0           0  0.200348  0.498067   0.663106  0.204096   \n",
       "482     1        0           0  0.391735  0.504508   0.188475  0.482973   \n",
       "\n",
       "     spring  summer  winter  ...      3      4      5      6      7      8  \\\n",
       "653   False   False    True  ...  False  False  False  False  False  False   \n",
       "576   False   False   False  ...  False  False  False  False   True  False   \n",
       "426    True   False   False  ...   True  False  False  False  False  False   \n",
       "728    True   False   False  ...  False  False  False  False  False  False   \n",
       "482   False    True   False  ...  False   True  False  False  False  False   \n",
       "\n",
       "         9     10     11     12  \n",
       "653  False   True  False  False  \n",
       "576  False  False  False  False  \n",
       "426  False  False  False  False  \n",
       "728  False  False  False   True  \n",
       "482  False  False  False  False  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 1080,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "id": "910d1cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>510.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.507843</td>\n",
       "      <td>0.025490</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.512989</td>\n",
       "      <td>0.650369</td>\n",
       "      <td>0.320768</td>\n",
       "      <td>0.513620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500429</td>\n",
       "      <td>0.157763</td>\n",
       "      <td>0.468282</td>\n",
       "      <td>0.212385</td>\n",
       "      <td>0.145882</td>\n",
       "      <td>0.169797</td>\n",
       "      <td>0.224593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.332086</td>\n",
       "      <td>0.538643</td>\n",
       "      <td>0.199179</td>\n",
       "      <td>0.356420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526811</td>\n",
       "      <td>0.653714</td>\n",
       "      <td>0.296763</td>\n",
       "      <td>0.518638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688457</td>\n",
       "      <td>0.754830</td>\n",
       "      <td>0.414447</td>\n",
       "      <td>0.684710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Year     holiday  workingday       atemp    Humidity   windspeed  \\\n",
       "count  510.000000  510.000000  510.000000  510.000000  510.000000  510.000000   \n",
       "mean     0.507843    0.025490    0.676471    0.512989    0.650369    0.320768   \n",
       "std      0.500429    0.157763    0.468282    0.212385    0.145882    0.169797   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.332086    0.538643    0.199179   \n",
       "50%      1.000000    0.000000    1.000000    0.526811    0.653714    0.296763   \n",
       "75%      1.000000    0.000000    1.000000    0.688457    0.754830    0.414447   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "            Count  \n",
       "count  510.000000  \n",
       "mean     0.513620  \n",
       "std      0.224593  \n",
       "min      0.000000  \n",
       "25%      0.356420  \n",
       "50%      0.518638  \n",
       "75%      0.684710  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 1081,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "id": "52a3ed7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATING X AND Y\n",
    "y_train = bike_train.pop('Count')\n",
    "X_train = bike_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "id": "d292f6ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>...</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.501133</td>\n",
       "      <td>0.575354</td>\n",
       "      <td>0.300794</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766351</td>\n",
       "      <td>0.725633</td>\n",
       "      <td>0.264686</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.438975</td>\n",
       "      <td>0.640189</td>\n",
       "      <td>0.255342</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.200348</td>\n",
       "      <td>0.498067</td>\n",
       "      <td>0.663106</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.391735</td>\n",
       "      <td>0.504508</td>\n",
       "      <td>0.188475</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  workingday     atemp  Humidity  windspeed  spring  summer  \\\n",
       "653     1        0           1  0.501133  0.575354   0.300794   False   False   \n",
       "576     1        0           1  0.766351  0.725633   0.264686   False   False   \n",
       "426     1        0           0  0.438975  0.640189   0.255342    True   False   \n",
       "728     1        0           0  0.200348  0.498067   0.663106    True   False   \n",
       "482     1        0           0  0.391735  0.504508   0.188475   False    True   \n",
       "\n",
       "     winter  Light rain_Light snow_Thunderstorm  ...      3      4      5  \\\n",
       "653    True                               False  ...  False  False  False   \n",
       "576   False                               False  ...  False  False  False   \n",
       "426   False                               False  ...   True  False  False   \n",
       "728   False                               False  ...  False  False  False   \n",
       "482   False                               False  ...  False   True  False   \n",
       "\n",
       "         6      7      8      9     10     11     12  \n",
       "653  False  False  False  False   True  False  False  \n",
       "576  False   True  False  False  False  False  False  \n",
       "426  False  False  False  False  False  False  False  \n",
       "728  False  False  False  False  False  False   True  \n",
       "482  False  False  False  False  False  False  False  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 1083,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "671c5052",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "id": "53762877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "653    0.864243\n",
       "576    0.827658\n",
       "426    0.465255\n",
       "728    0.204096\n",
       "482    0.482973\n",
       "Name: Count, dtype: float64"
      ]
     },
     "execution_count": 1084,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5123e4f",
   "metadata": {},
   "source": [
    "# model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "id": "8c871995",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "id": "1a79d87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train.values, y_train)\n",
    "rfe = RFE(estimator=lm, n_features_to_select=15)            \n",
    "rfe = rfe.fit(X_train.values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "id": "f38f8c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Year', True, 1),\n",
       " ('holiday', True, 1),\n",
       " ('workingday', False, 4),\n",
       " ('atemp', True, 1),\n",
       " ('Humidity', True, 1),\n",
       " ('windspeed', True, 1),\n",
       " ('spring', True, 1),\n",
       " ('summer', False, 7),\n",
       " ('winter', True, 1),\n",
       " ('Light rain_Light snow_Thunderstorm', True, 1),\n",
       " ('Mist_cloudy', True, 1),\n",
       " ('Monday', False, 11),\n",
       " ('Saturday', False, 3),\n",
       " ('Sunday', False, 10),\n",
       " ('Thursday', False, 13),\n",
       " ('Tuesday', False, 12),\n",
       " ('Wednesday', False, 14),\n",
       " (2, False, 5),\n",
       " (3, True, 1),\n",
       " (4, True, 1),\n",
       " (5, True, 1),\n",
       " (6, True, 1),\n",
       " (7, False, 6),\n",
       " (8, True, 1),\n",
       " (9, True, 1),\n",
       " (10, False, 2),\n",
       " (11, False, 8),\n",
       " (12, False, 9)]"
      ]
     },
     "execution_count": 1087,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfe.support_,rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "id": "0fc7c00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                              'Year',\n",
       "                                  'holiday',\n",
       "                                    'atemp',\n",
       "                                 'Humidity',\n",
       "                                'windspeed',\n",
       "                                   'spring',\n",
       "                                   'winter',\n",
       "       'Light rain_Light snow_Thunderstorm',\n",
       "                              'Mist_cloudy',\n",
       "                                          3,\n",
       "                                          4,\n",
       "                                          5,\n",
       "                                          6,\n",
       "                                          8,\n",
       "                                          9],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1088,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = X_train.columns[rfe.support_]\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "id": "99687637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['workingday',     'summer',     'Monday',   'Saturday',     'Sunday',\n",
       "         'Thursday',    'Tuesday',  'Wednesday',            2,            7,\n",
       "                 10,           11,           12],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1089,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[~rfe.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1090,
   "id": "7c493da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe = X_train[col].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "id": "410cc6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sms  \n",
    "X_train_rfe1 = sms.add_constant(X_train_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "id": "35212bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = sms.OLS(y_train,X_train_rfe1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "id": "a6d7f748",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.841\n",
      "Model:                            OLS   Adj. R-squared:                  0.836\n",
      "Method:                 Least Squares   F-statistic:                     173.6\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          8.06e-186\n",
      "Time:                        06:27:36   Log-Likelihood:                 506.64\n",
      "No. Observations:                 510   AIC:                            -981.3\n",
      "Df Residuals:                     494   BIC:                            -913.5\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.2995      0.034      8.909      0.000       0.233       0.366\n",
      "Year                                   0.2320      0.008     28.268      0.000       0.216       0.248\n",
      "holiday                               -0.0845      0.026     -3.266      0.001      -0.135      -0.034\n",
      "atemp                                  0.4597      0.033     13.757      0.000       0.394       0.525\n",
      "Humidity                              -0.1599      0.038     -4.156      0.000      -0.236      -0.084\n",
      "windspeed                             -0.1613      0.026     -6.170      0.000      -0.213      -0.110\n",
      "spring                                -0.0757      0.019     -4.061      0.000      -0.112      -0.039\n",
      "winter                                 0.1024      0.016      6.301      0.000       0.070       0.134\n",
      "Light rain_Light snow_Thunderstorm    -0.2359      0.027     -8.833      0.000      -0.288      -0.183\n",
      "Mist_cloudy                           -0.0568      0.011     -5.347      0.000      -0.078      -0.036\n",
      "3                                      0.0585      0.015      3.852      0.000       0.029       0.088\n",
      "4                                      0.0591      0.020      2.948      0.003       0.020       0.099\n",
      "5                                      0.0922      0.019      4.906      0.000       0.055       0.129\n",
      "6                                      0.0583      0.019      3.017      0.003       0.020       0.096\n",
      "8                                      0.0608      0.018      3.362      0.001       0.025       0.096\n",
      "9                                      0.1175      0.017      6.719      0.000       0.083       0.152\n",
      "==============================================================================\n",
      "Omnibus:                       60.719   Durbin-Watson:                   2.015\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              162.007\n",
      "Skew:                          -0.591   Prob(JB):                     6.62e-36\n",
      "Kurtosis:                       5.496   Cond. No.                         19.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a92e06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "id": "67dac4d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Humidity</td>\n",
       "      <td>25.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>atemp</td>\n",
       "      <td>15.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spring</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>winter</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features    VIF\n",
       "3                             Humidity  25.72\n",
       "2                                atemp  15.52\n",
       "4                            windspeed   4.56\n",
       "5                               spring   3.52\n",
       "6                               winter   3.21\n",
       "8                          Mist_cloudy   2.26\n",
       "0                                 Year   2.08\n",
       "13                                   8   1.90\n",
       "11                                   5   1.77\n",
       "10                                   4   1.68\n",
       "12                                   6   1.65\n",
       "14                                   9   1.50\n",
       "9                                    3   1.36\n",
       "7   Light rain_Light snow_Thunderstorm   1.24\n",
       "1                              holiday   1.05"
      ]
     },
     "execution_count": 1094,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "id": "daec7999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>atemp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>spring</th>\n",
       "      <th>winter</th>\n",
       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>Mist_cloudy</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.501133</td>\n",
       "      <td>0.575354</td>\n",
       "      <td>0.300794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.766351</td>\n",
       "      <td>0.725633</td>\n",
       "      <td>0.264686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.438975</td>\n",
       "      <td>0.640189</td>\n",
       "      <td>0.255342</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200348</td>\n",
       "      <td>0.498067</td>\n",
       "      <td>0.663106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391735</td>\n",
       "      <td>0.504508</td>\n",
       "      <td>0.188475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     const  Year  holiday     atemp  Humidity  windspeed  spring  winter  \\\n",
       "653    1.0   1.0      0.0  0.501133  0.575354   0.300794     0.0     1.0   \n",
       "576    1.0   1.0      0.0  0.766351  0.725633   0.264686     0.0     0.0   \n",
       "426    1.0   1.0      0.0  0.438975  0.640189   0.255342     1.0     0.0   \n",
       "728    1.0   1.0      0.0  0.200348  0.498067   0.663106     1.0     0.0   \n",
       "482    1.0   1.0      0.0  0.391735  0.504508   0.188475     0.0     0.0   \n",
       "\n",
       "     Light rain_Light snow_Thunderstorm  Mist_cloudy    3    4    5    6    8  \\\n",
       "653                                 0.0          0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "576                                 0.0          0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "426                                 0.0          1.0  1.0  0.0  0.0  0.0  0.0   \n",
       "728                                 0.0          0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "482                                 0.0          1.0  0.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "       9  \n",
       "653  0.0  \n",
       "576  0.0  \n",
       "426  0.0  \n",
       "728  0.0  \n",
       "482  0.0  "
      ]
     },
     "execution_count": 1095,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "id": "3acdd2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop(['Humidity'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "id": "1da7ff64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm  \n",
    "X_train_rfe1 = sm.add_constant(X_train_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "id": "f447cf98",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm1 = sm.OLS(y_train,X_train_rfe1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1099,
   "id": "001dc5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.835\n",
      "Model:                            OLS   Adj. R-squared:                  0.830\n",
      "Method:                 Least Squares   F-statistic:                     178.8\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.75e-183\n",
      "Time:                        06:27:46   Log-Likelihood:                 497.88\n",
      "No. Observations:                 510   AIC:                            -965.8\n",
      "Df Residuals:                     495   BIC:                            -902.2\n",
      "Df Model:                          14                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.2151      0.027      7.900      0.000       0.162       0.269\n",
      "Year                                   0.2355      0.008     28.377      0.000       0.219       0.252\n",
      "holiday                               -0.0854      0.026     -3.248      0.001      -0.137      -0.034\n",
      "atemp                                  0.4298      0.033     12.960      0.000       0.365       0.495\n",
      "windspeed                             -0.1332      0.026     -5.189      0.000      -0.184      -0.083\n",
      "spring                                -0.0815      0.019     -4.310      0.000      -0.119      -0.044\n",
      "winter                                 0.0911      0.016      5.597      0.000       0.059       0.123\n",
      "Light rain_Light snow_Thunderstorm    -0.2799      0.025    -11.234      0.000      -0.329      -0.231\n",
      "Mist_cloudy                           -0.0820      0.009     -9.252      0.000      -0.099      -0.065\n",
      "3                                      0.0597      0.015      3.871      0.000       0.029       0.090\n",
      "4                                      0.0597      0.020      2.930      0.004       0.020       0.100\n",
      "5                                      0.0817      0.019      4.316      0.000       0.045       0.119\n",
      "6                                      0.0628      0.020      3.199      0.001       0.024       0.101\n",
      "8                                      0.0594      0.018      3.235      0.001       0.023       0.096\n",
      "9                                      0.1118      0.018      6.311      0.000       0.077       0.147\n",
      "==============================================================================\n",
      "Omnibus:                       61.552   Durbin-Watson:                   2.019\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              155.196\n",
      "Skew:                          -0.618   Prob(JB):                     1.99e-34\n",
      "Kurtosis:                       5.404   Cond. No.                         16.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "id": "3092fe89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>atemp</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spring</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>winter</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "2                                atemp  6.26\n",
       "3                            windspeed  4.56\n",
       "4                               spring  2.10\n",
       "0                                 Year  2.08\n",
       "5                               winter  1.97\n",
       "12                                   8  1.86\n",
       "11                                   6  1.64\n",
       "10                                   5  1.60\n",
       "7                          Mist_cloudy  1.55\n",
       "9                                    4  1.54\n",
       "13                                   9  1.45\n",
       "8                                    3  1.35\n",
       "6   Light rain_Light snow_Thunderstorm  1.08\n",
       "1                              holiday  1.05"
      ]
     },
     "execution_count": 1100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "id": "3a401411",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop(['atemp'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "id": "cca85cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.779\n",
      "Model:                            OLS   Adj. R-squared:                  0.773\n",
      "Method:                 Least Squares   F-statistic:                     134.4\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          3.90e-153\n",
      "Time:                        06:27:50   Log-Likelihood:                 423.38\n",
      "No. Observations:                 510   AIC:                            -818.8\n",
      "Df Residuals:                     496   BIC:                            -759.5\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.5082      0.018     28.983      0.000       0.474       0.543\n",
      "Year                                   0.2479      0.010     26.013      0.000       0.229       0.267\n",
      "holiday                               -0.0886      0.030     -2.916      0.004      -0.148      -0.029\n",
      "windspeed                             -0.1645      0.030     -5.566      0.000      -0.223      -0.106\n",
      "spring                                -0.2392      0.017    -14.308      0.000      -0.272      -0.206\n",
      "winter                                -0.0080      0.017     -0.482      0.630      -0.041       0.025\n",
      "Light rain_Light snow_Thunderstorm    -0.2896      0.029    -10.056      0.000      -0.346      -0.233\n",
      "Mist_cloudy                           -0.0943      0.010     -9.257      0.000      -0.114      -0.074\n",
      "3                                      0.0494      0.018      2.772      0.006       0.014       0.084\n",
      "4                                     -0.0153      0.023     -0.676      0.499      -0.060       0.029\n",
      "5                                      0.0671      0.022      3.072      0.002       0.024       0.110\n",
      "6                                      0.0901      0.023      3.995      0.000       0.046       0.134\n",
      "8                                      0.0929      0.021      4.418      0.000       0.052       0.134\n",
      "9                                      0.1318      0.020      6.456      0.000       0.092       0.172\n",
      "==============================================================================\n",
      "Omnibus:                       35.107   Durbin-Watson:                   1.975\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               78.924\n",
      "Skew:                          -0.372   Prob(JB):                     7.28e-18\n",
      "Kurtosis:                       4.777   Cond. No.                         11.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe2 = sm.add_constant(X_train_rfe)\n",
    "lm2 = sm.OLS(y_train,X_train_rfe2).fit()\n",
    "print(lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "id": "542596a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spring</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>winter</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "2                            windspeed  3.95\n",
       "3                               spring  2.10\n",
       "0                                 Year  1.90\n",
       "4                               winter  1.80\n",
       "6                          Mist_cloudy  1.54\n",
       "8                                    4  1.48\n",
       "9                                    5  1.32\n",
       "7                                    3  1.31\n",
       "11                                   8  1.31\n",
       "10                                   6  1.24\n",
       "12                                   9  1.17\n",
       "5   Light rain_Light snow_Thunderstorm  1.08\n",
       "1                              holiday  1.05"
      ]
     },
     "execution_count": 1103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1104,
   "id": "79662d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                              'Year',\n",
       "                                  'holiday',\n",
       "                                'windspeed',\n",
       "                                   'spring',\n",
       "                                   'winter',\n",
       "       'Light rain_Light snow_Thunderstorm',\n",
       "                              'Mist_cloudy',\n",
       "                                          3,\n",
       "                                          4,\n",
       "                                          5,\n",
       "                                          6,\n",
       "                                          8,\n",
       "                                          9],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "id": "e7985c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop(['windspeed'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "id": "49b2aba1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.765\n",
      "Model:                            OLS   Adj. R-squared:                  0.759\n",
      "Method:                 Least Squares   F-statistic:                     134.9\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          9.96e-148\n",
      "Time:                        06:27:54   Log-Likelihood:                 407.93\n",
      "No. Observations:                 510   AIC:                            -789.9\n",
      "Df Residuals:                     497   BIC:                            -734.8\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4616      0.016     29.102      0.000       0.430       0.493\n",
      "Year                                   0.2478      0.010     25.245      0.000       0.228       0.267\n",
      "holiday                               -0.0918      0.031     -2.935      0.003      -0.153      -0.030\n",
      "spring                                -0.2516      0.017    -14.740      0.000      -0.285      -0.218\n",
      "winter                                -0.0093      0.017     -0.544      0.587      -0.043       0.024\n",
      "Light rain_Light snow_Thunderstorm    -0.3052      0.030    -10.342      0.000      -0.363      -0.247\n",
      "Mist_cloudy                           -0.0934      0.010     -8.907      0.000      -0.114      -0.073\n",
      "3                                      0.0395      0.018      2.166      0.031       0.004       0.075\n",
      "4                                     -0.0386      0.023     -1.691      0.092      -0.084       0.006\n",
      "5                                      0.0638      0.022      2.838      0.005       0.020       0.108\n",
      "6                                      0.0874      0.023      3.763      0.000       0.042       0.133\n",
      "8                                      0.0961      0.022      4.439      0.000       0.054       0.139\n",
      "9                                      0.1381      0.021      6.580      0.000       0.097       0.179\n",
      "==============================================================================\n",
      "Omnibus:                       43.939   Durbin-Watson:                   1.998\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              121.742\n",
      "Skew:                          -0.401   Prob(JB):                     3.67e-27\n",
      "Kurtosis:                       5.255   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe3 = sm.add_constant(X_train_rfe)\n",
    "lm3 = sm.OLS(y_train,X_train_rfe3).fit()\n",
    "print(lm3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "id": "3a32e9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>winter</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.85\n",
       "5                          Mist_cloudy  1.53\n",
       "2                               spring  1.48\n",
       "3                               winter  1.39\n",
       "6                                    3  1.23\n",
       "10                                   8  1.17\n",
       "7                                    4  1.14\n",
       "8                                    5  1.14\n",
       "11                                   9  1.12\n",
       "9                                    6  1.08\n",
       "4   Light rain_Light snow_Thunderstorm  1.06\n",
       "1                              holiday  1.05"
      ]
     },
     "execution_count": 1107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "id": "05b7ba2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Winter has A VERY HIGH p-value WHUCH MEANS IT IS insignificant SO WE DROP IT\n",
    "X_train_rfe=X_train_rfe.drop(['winter'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "id": "bea899d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.765\n",
      "Model:                            OLS   Adj. R-squared:                  0.760\n",
      "Method:                 Least Squares   F-statistic:                     147.4\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          9.28e-149\n",
      "Time:                        06:27:56   Log-Likelihood:                 407.78\n",
      "No. Observations:                 510   AIC:                            -791.6\n",
      "Df Residuals:                     498   BIC:                            -740.7\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4552      0.011     42.608      0.000       0.434       0.476\n",
      "Year                                   0.2478      0.010     25.263      0.000       0.228       0.267\n",
      "holiday                               -0.0924      0.031     -2.956      0.003      -0.154      -0.031\n",
      "spring                                -0.2457      0.013    -18.510      0.000      -0.272      -0.220\n",
      "Light rain_Light snow_Thunderstorm    -0.3060      0.029    -10.389      0.000      -0.364      -0.248\n",
      "Mist_cloudy                           -0.0938      0.010     -8.976      0.000      -0.114      -0.073\n",
      "3                                      0.0423      0.017      2.421      0.016       0.008       0.077\n",
      "4                                     -0.0321      0.019     -1.653      0.099      -0.070       0.006\n",
      "5                                      0.0704      0.019      3.711      0.000       0.033       0.108\n",
      "6                                      0.0938      0.020      4.703      0.000       0.055       0.133\n",
      "8                                      0.1026      0.018      5.698      0.000       0.067       0.138\n",
      "9                                      0.1426      0.019      7.422      0.000       0.105       0.180\n",
      "==============================================================================\n",
      "Omnibus:                       44.016   Durbin-Watson:                   1.991\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              121.952\n",
      "Skew:                          -0.402   Prob(JB):                     3.30e-27\n",
      "Kurtosis:                       5.257   Cond. No.                         8.20\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe4 = sm.add_constant(X_train_rfe)\n",
    "lm4 = sm.OLS(y_train,X_train_rfe4).fit()\n",
    "print(lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "id": "a3f7e479",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cOLUMN 4 HAS A VERY HIGH p-value WHUCH MEANS IT IS insignificant SO WE DROP IT\n",
    "X_train_rfe=X_train_rfe.drop([4],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "id": "83efb93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.764\n",
      "Model:                            OLS   Adj. R-squared:                  0.759\n",
      "Method:                 Least Squares   F-statistic:                     161.3\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.76e-149\n",
      "Time:                        06:27:58   Log-Likelihood:                 406.38\n",
      "No. Observations:                 510   AIC:                            -790.8\n",
      "Df Residuals:                     499   BIC:                            -744.2\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4493      0.010     44.546      0.000       0.429       0.469\n",
      "Year                                   0.2469      0.010     25.167      0.000       0.228       0.266\n",
      "holiday                               -0.0916      0.031     -2.928      0.004      -0.153      -0.030\n",
      "spring                                -0.2402      0.013    -18.666      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3039      0.029    -10.308      0.000      -0.362      -0.246\n",
      "Mist_cloudy                           -0.0938      0.010     -8.956      0.000      -0.114      -0.073\n",
      "3                                      0.0450      0.017      2.581      0.010       0.011       0.079\n",
      "5                                      0.0767      0.019      4.119      0.000       0.040       0.113\n",
      "6                                      0.1002      0.020      5.108      0.000       0.062       0.139\n",
      "8                                      0.1090      0.018      6.186      0.000       0.074       0.144\n",
      "9                                      0.1488      0.019      7.881      0.000       0.112       0.186\n",
      "==============================================================================\n",
      "Omnibus:                       40.875   Durbin-Watson:                   1.990\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              110.374\n",
      "Skew:                          -0.375   Prob(JB):                     1.08e-24\n",
      "Kurtosis:                       5.152   Cond. No.                         8.12\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe5 = sm.add_constant(X_train_rfe)\n",
    "lm5 = sm.OLS(y_train,X_train_rfe5).fit()\n",
    "print(lm5.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "id": "7a3b2dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.22</td>\n",
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       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
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      ],
      "text/plain": [
       "                             Features   VIF\n",
       "0                                Year  1.51\n",
       "2                              spring  1.39\n",
       "4                         Mist_cloudy  1.36\n",
       "5                                   3  1.22\n",
       "8                                   8  1.12\n",
       "6                                   5  1.10\n",
       "9                                   9  1.10\n",
       "7                                   6  1.06\n",
       "1                             holiday  1.03\n",
       "3  Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "id": "76ce9845",
   "metadata": {},
   "outputs": [
    {
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       "      <th>653</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Saturday  \n",
       "653  0.0  0.0  0.0  0.0  0.0     False  \n",
       "576  0.0  0.0  0.0  0.0  0.0     False  \n",
       "426  1.0  0.0  0.0  0.0  0.0      True  \n",
       "728  0.0  0.0  0.0  0.0  0.0     False  \n",
       "482  0.0  0.0  0.0  0.0  0.0      True  "
      ]
     },
     "execution_count": 1113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe['Saturday']=X_train['Saturday']\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "id": "7ff5fef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.764\n",
      "Model:                            OLS   Adj. R-squared:                  0.759\n",
      "Method:                 Least Squares   F-statistic:                     146.6\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.47e-148\n",
      "Time:                        06:31:12   Log-Likelihood:                 406.77\n",
      "No. Observations:                 510   AIC:                            -789.5\n",
      "Df Residuals:                     498   BIC:                            -738.7\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4473      0.010     43.229      0.000       0.427       0.468\n",
      "Year                                   0.2472      0.010     25.176      0.000       0.228       0.266\n",
      "holiday                               -0.0897      0.031     -2.858      0.004      -0.151      -0.028\n",
      "spring                                -0.2403      0.013    -18.670      0.000      -0.266      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3043      0.029    -10.319      0.000      -0.362      -0.246\n",
      "Mist_cloudy                           -0.0939      0.010     -8.964      0.000      -0.114      -0.073\n",
      "3                                      0.0449      0.017      2.573      0.010       0.011       0.079\n",
      "5                                      0.0775      0.019      4.156      0.000       0.041       0.114\n",
      "6                                      0.1002      0.020      5.106      0.000       0.062       0.139\n",
      "8                                      0.1090      0.018      6.182      0.000       0.074       0.144\n",
      "9                                      0.1489      0.019      7.884      0.000       0.112       0.186\n",
      "Saturday                               0.0118      0.014      0.867      0.386      -0.015       0.038\n",
      "==============================================================================\n",
      "Omnibus:                       42.791   Durbin-Watson:                   1.991\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              112.225\n",
      "Skew:                          -0.408   Prob(JB):                     4.27e-25\n",
      "Kurtosis:                       5.148   Cond. No.                         8.20\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe6 = sm.add_constant(X_train_rfe)\n",
    "lm6 = sm.OLS(y_train,X_train_rfe6.astype(float)).fit()\n",
    "print(lm6.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "id": "9c05f4b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saturday has high p-value so we drop it\n",
    "X_train_rfe=X_train_rfe.drop(['Saturday'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "id": "86b385ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.764\n",
      "Method:                 Least Squares   F-statistic:                     150.5\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          1.81e-150\n",
      "Time:                        06:32:57   Log-Likelihood:                 411.83\n",
      "No. Observations:                 510   AIC:                            -799.7\n",
      "Df Residuals:                     498   BIC:                            -748.8\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4579      0.010     44.346      0.000       0.438       0.478\n",
      "Year                                   0.2468      0.010     25.401      0.000       0.228       0.266\n",
      "holiday                               -0.0993      0.031     -3.194      0.001      -0.160      -0.038\n",
      "spring                                -0.2403      0.013    -18.852      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3086      0.029    -10.557      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0956      0.010     -9.204      0.000      -0.116      -0.075\n",
      "3                                      0.0419      0.017      2.423      0.016       0.008       0.076\n",
      "5                                      0.0754      0.018      4.087      0.000       0.039       0.112\n",
      "6                                      0.0992      0.019      5.107      0.000       0.061       0.137\n",
      "8                                      0.1058      0.017      6.055      0.000       0.071       0.140\n",
      "9                                      0.1461      0.019      7.806      0.000       0.109       0.183\n",
      "Sunday                                -0.0456      0.014     -3.280      0.001      -0.073      -0.018\n",
      "==============================================================================\n",
      "Omnibus:                       42.945   Durbin-Watson:                   2.000\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              126.971\n",
      "Skew:                          -0.365   Prob(JB):                     2.68e-28\n",
      "Kurtosis:                       5.333   Cond. No.                         8.22\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe7 = sm.add_constant(X_train_rfe)\n",
    "lm7 = sm.OLS(y_train,X_train_rfe7.astype(float)).fit()\n",
    "print(lm7.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "id": "0f12a8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.54\n",
       "2                               spring  1.41\n",
       "4                          Mist_cloudy  1.37\n",
       "5                                    3  1.23\n",
       "8                                    8  1.12\n",
       "9                                    9  1.11\n",
       "10                              Sunday  1.11\n",
       "6                                    5  1.10\n",
       "7                                    6  1.07\n",
       "1                              holiday  1.03\n",
       "3   Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "id": "277ba66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\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>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>spring</th>\n",
       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>Mist_cloudy</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False  "
      ]
     },
     "execution_count": 1126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ADDING SUNDAY TO CHECK IF MODEL IMPROVES\n",
    "X_train_rfe['Sunday']=X_train['Sunday']\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "id": "40ba4702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.764\n",
      "Method:                 Least Squares   F-statistic:                     150.5\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          1.81e-150\n",
      "Time:                        06:36:10   Log-Likelihood:                 411.83\n",
      "No. Observations:                 510   AIC:                            -799.7\n",
      "Df Residuals:                     498   BIC:                            -748.8\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4579      0.010     44.346      0.000       0.438       0.478\n",
      "Year                                   0.2468      0.010     25.401      0.000       0.228       0.266\n",
      "holiday                               -0.0993      0.031     -3.194      0.001      -0.160      -0.038\n",
      "spring                                -0.2403      0.013    -18.852      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3086      0.029    -10.557      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0956      0.010     -9.204      0.000      -0.116      -0.075\n",
      "3                                      0.0419      0.017      2.423      0.016       0.008       0.076\n",
      "5                                      0.0754      0.018      4.087      0.000       0.039       0.112\n",
      "6                                      0.0992      0.019      5.107      0.000       0.061       0.137\n",
      "8                                      0.1058      0.017      6.055      0.000       0.071       0.140\n",
      "9                                      0.1461      0.019      7.806      0.000       0.109       0.183\n",
      "Sunday                                -0.0456      0.014     -3.280      0.001      -0.073      -0.018\n",
      "==============================================================================\n",
      "Omnibus:                       42.945   Durbin-Watson:                   2.000\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              126.971\n",
      "Skew:                          -0.365   Prob(JB):                     2.68e-28\n",
      "Kurtosis:                       5.333   Cond. No.                         8.22\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe8 = sm.add_constant(X_train_rfe)\n",
    "lm8 = sm.OLS(y_train,X_train_rfe8.astype(float)).fit()\n",
    "print(lm8.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "id": "4c232908",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Year</td>\n",
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       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.37</td>\n",
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       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.23</td>\n",
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       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.02</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.54\n",
       "2                               spring  1.41\n",
       "4                          Mist_cloudy  1.37\n",
       "5                                    3  1.23\n",
       "8                                    8  1.12\n",
       "9                                    9  1.11\n",
       "10                              Sunday  1.11\n",
       "6                                    5  1.10\n",
       "7                                    6  1.07\n",
       "1                              holiday  1.03\n",
       "3   Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "id": "04685cca",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
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       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>Mist_cloudy</th>\n",
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      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday  workingday  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False           1  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False           1  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False           0  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True           0  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False           0  "
      ]
     },
     "execution_count": 1131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ADDING WORKING DAY TO CHECK IF MODEL IMPROVES\n",
    "X_train_rfe['workingday']=X_train['workingday']\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "id": "1bb481fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.763\n",
      "Method:                 Least Squares   F-statistic:                     137.7\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.18e-149\n",
      "Time:                        06:37:31   Log-Likelihood:                 411.87\n",
      "No. Observations:                 510   AIC:                            -797.7\n",
      "Df Residuals:                     497   BIC:                            -742.7\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4610      0.015     30.733      0.000       0.432       0.490\n",
      "Year                                   0.2469      0.010     25.372      0.000       0.228       0.266\n",
      "holiday                               -0.1025      0.033     -3.105      0.002      -0.167      -0.038\n",
      "spring                                -0.2403      0.013    -18.837      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3086      0.029    -10.549      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0956      0.010     -9.197      0.000      -0.116      -0.075\n",
      "3                                      0.0419      0.017      2.421      0.016       0.008       0.076\n",
      "5                                      0.0757      0.018      4.093      0.000       0.039       0.112\n",
      "6                                      0.0992      0.019      5.103      0.000       0.061       0.137\n",
      "8                                      0.1059      0.017      6.051      0.000       0.071       0.140\n",
      "9                                      0.1462      0.019      7.802      0.000       0.109       0.183\n",
      "Sunday                                -0.0488      0.018     -2.741      0.006      -0.084      -0.014\n",
      "workingday                            -0.0039      0.014     -0.288      0.774      -0.031       0.023\n",
      "==============================================================================\n",
      "Omnibus:                       43.589   Durbin-Watson:                   2.000\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              127.486\n",
      "Skew:                          -0.377   Prob(JB):                     2.07e-28\n",
      "Kurtosis:                       5.330   Cond. No.                         10.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe9 = sm.add_constant(X_train_rfe)\n",
    "lm9 = sm.OLS(y_train,X_train_rfe9.astype(float)).fit()\n",
    "print(lm9.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "id": "a9264015",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop(['workingday'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "id": "0875161f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.764\n",
      "Method:                 Least Squares   F-statistic:                     150.5\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          1.81e-150\n",
      "Time:                        06:38:12   Log-Likelihood:                 411.83\n",
      "No. Observations:                 510   AIC:                            -799.7\n",
      "Df Residuals:                     498   BIC:                            -748.8\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4579      0.010     44.346      0.000       0.438       0.478\n",
      "Year                                   0.2468      0.010     25.401      0.000       0.228       0.266\n",
      "holiday                               -0.0993      0.031     -3.194      0.001      -0.160      -0.038\n",
      "spring                                -0.2403      0.013    -18.852      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3086      0.029    -10.557      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0956      0.010     -9.204      0.000      -0.116      -0.075\n",
      "3                                      0.0419      0.017      2.423      0.016       0.008       0.076\n",
      "5                                      0.0754      0.018      4.087      0.000       0.039       0.112\n",
      "6                                      0.0992      0.019      5.107      0.000       0.061       0.137\n",
      "8                                      0.1058      0.017      6.055      0.000       0.071       0.140\n",
      "9                                      0.1461      0.019      7.806      0.000       0.109       0.183\n",
      "Sunday                                -0.0456      0.014     -3.280      0.001      -0.073      -0.018\n",
      "==============================================================================\n",
      "Omnibus:                       42.945   Durbin-Watson:                   2.000\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              126.971\n",
      "Skew:                          -0.365   Prob(JB):                     2.68e-28\n",
      "Kurtosis:                       5.333   Cond. No.                         8.22\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe10 = sm.add_constant(X_train_rfe)\n",
    "lm10 = sm.OLS(y_train,X_train_rfe10.astype(float)).fit()\n",
    "print(lm10.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "id": "6e6e1356",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.02</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.54\n",
       "2                               spring  1.41\n",
       "4                          Mist_cloudy  1.37\n",
       "5                                    3  1.23\n",
       "8                                    8  1.12\n",
       "9                                    9  1.11\n",
       "10                              Sunday  1.11\n",
       "6                                    5  1.10\n",
       "7                                    6  1.07\n",
       "1                              holiday  1.03\n",
       "3   Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e4daa7",
   "metadata": {},
   "source": [
    "# Add month model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1139,
   "id": "c011876c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday      2  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False  False  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False  False  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False  False  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True  False  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False  False  "
      ]
     },
     "execution_count": 1139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ADDING MONTH 2 TO CHECK IF MODEL IMPROVES\n",
    "X_train_rfe[2]=X_train[2]\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1141,
   "id": "4e5b8b94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.763\n",
      "Method:                 Least Squares   F-statistic:                     137.7\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.06e-149\n",
      "Time:                        06:40:27   Log-Likelihood:                 411.93\n",
      "No. Observations:                 510   AIC:                            -797.9\n",
      "Df Residuals:                     497   BIC:                            -742.8\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4577      0.010     44.265      0.000       0.437       0.478\n",
      "Year                                   0.2468      0.010     25.380      0.000       0.228       0.266\n",
      "holiday                               -0.0995      0.031     -3.198      0.001      -0.161      -0.038\n",
      "spring                                -0.2435      0.015    -16.630      0.000      -0.272      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3090      0.029    -10.557      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0955      0.010     -9.188      0.000      -0.116      -0.075\n",
      "3                                      0.0441      0.018      2.450      0.015       0.009       0.080\n",
      "5                                      0.0755      0.018      4.091      0.000       0.039       0.112\n",
      "6                                      0.0994      0.019      5.110      0.000       0.061       0.138\n",
      "8                                      0.1060      0.017      6.058      0.000       0.072       0.140\n",
      "9                                      0.1463      0.019      7.807      0.000       0.109       0.183\n",
      "Sunday                                -0.0457      0.014     -3.284      0.001      -0.073      -0.018\n",
      "2                                      0.0102      0.023      0.447      0.655      -0.035       0.055\n",
      "==============================================================================\n",
      "Omnibus:                       43.016   Durbin-Watson:                   1.998\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              128.611\n",
      "Skew:                          -0.362   Prob(JB):                     1.18e-28\n",
      "Kurtosis:                       5.351   Cond. No.                         8.24\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe11 = sm.add_constant(X_train_rfe)\n",
    "lm11 = sm.OLS(y_train,X_train_rfe11.astype(float)).fit()\n",
    "print(lm11.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1142,
   "id": "a7892cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop([2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1144,
   "id": "31061959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.769\n",
      "Model:                            OLS   Adj. R-squared:                  0.764\n",
      "Method:                 Least Squares   F-statistic:                     150.5\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          1.81e-150\n",
      "Time:                        06:41:12   Log-Likelihood:                 411.83\n",
      "No. Observations:                 510   AIC:                            -799.7\n",
      "Df Residuals:                     498   BIC:                            -748.8\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4579      0.010     44.346      0.000       0.438       0.478\n",
      "Year                                   0.2468      0.010     25.401      0.000       0.228       0.266\n",
      "holiday                               -0.0993      0.031     -3.194      0.001      -0.160      -0.038\n",
      "spring                                -0.2403      0.013    -18.852      0.000      -0.265      -0.215\n",
      "Light rain_Light snow_Thunderstorm    -0.3086      0.029    -10.557      0.000      -0.366      -0.251\n",
      "Mist_cloudy                           -0.0956      0.010     -9.204      0.000      -0.116      -0.075\n",
      "3                                      0.0419      0.017      2.423      0.016       0.008       0.076\n",
      "5                                      0.0754      0.018      4.087      0.000       0.039       0.112\n",
      "6                                      0.0992      0.019      5.107      0.000       0.061       0.137\n",
      "8                                      0.1058      0.017      6.055      0.000       0.071       0.140\n",
      "9                                      0.1461      0.019      7.806      0.000       0.109       0.183\n",
      "Sunday                                -0.0456      0.014     -3.280      0.001      -0.073      -0.018\n",
      "==============================================================================\n",
      "Omnibus:                       42.945   Durbin-Watson:                   2.000\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              126.971\n",
      "Skew:                          -0.365   Prob(JB):                     2.68e-28\n",
      "Kurtosis:                       5.333   Cond. No.                         8.22\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe12 = sm.add_constant(X_train_rfe)\n",
    "lm12 = sm.OLS(y_train,X_train_rfe12.astype(float)).fit()\n",
    "print(lm12.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1145,
   "id": "f8074c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.37</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>1.11</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.54\n",
       "2                               spring  1.41\n",
       "4                          Mist_cloudy  1.37\n",
       "5                                    3  1.23\n",
       "8                                    8  1.12\n",
       "9                                    9  1.11\n",
       "10                              Sunday  1.11\n",
       "6                                    5  1.10\n",
       "7                                    6  1.07\n",
       "1                              holiday  1.03\n",
       "3   Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1146,
   "id": "c6ab29e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>spring</th>\n",
       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>Mist_cloudy</th>\n",
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       "      <th>7</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday      7  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False  False  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False   True  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False  False  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True  False  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False  False  "
      ]
     },
     "execution_count": 1146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe[7]=X_train[7]\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1147,
   "id": "459058c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.780\n",
      "Model:                            OLS   Adj. R-squared:                  0.775\n",
      "Method:                 Least Squares   F-statistic:                     146.8\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          1.03e-154\n",
      "Time:                        06:42:22   Log-Likelihood:                 424.54\n",
      "No. Observations:                 510   AIC:                            -823.1\n",
      "Df Residuals:                     497   BIC:                            -768.0\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4373      0.011     40.197      0.000       0.416       0.459\n",
      "Year                                   0.2463      0.009     25.958      0.000       0.228       0.265\n",
      "holiday                               -0.0887      0.030     -2.915      0.004      -0.148      -0.029\n",
      "spring                                -0.2242      0.013    -17.460      0.000      -0.249      -0.199\n",
      "Light rain_Light snow_Thunderstorm    -0.2998      0.029    -10.484      0.000      -0.356      -0.244\n",
      "Mist_cloudy                           -0.0895      0.010     -8.763      0.000      -0.110      -0.069\n",
      "3                                      0.0501      0.017      2.953      0.003       0.017       0.083\n",
      "5                                      0.0934      0.018      5.087      0.000       0.057       0.129\n",
      "6                                      0.1190      0.019      6.143      0.000       0.081       0.157\n",
      "8                                      0.1245      0.017      7.130      0.000       0.090       0.159\n",
      "9                                      0.1635      0.019      8.791      0.000       0.127       0.200\n",
      "Sunday                                -0.0458      0.014     -3.367      0.001      -0.072      -0.019\n",
      "7                                      0.0965      0.019      5.039      0.000       0.059       0.134\n",
      "==============================================================================\n",
      "Omnibus:                       42.644   Durbin-Watson:                   2.047\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              137.373\n",
      "Skew:                          -0.327   Prob(JB):                     1.48e-30\n",
      "Kurtosis:                       5.457   Cond. No.                         8.42\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe13 = sm.add_constant(X_train_rfe)\n",
    "lm13 = sm.OLS(y_train,X_train_rfe13.astype(float)).fit()\n",
    "print(lm13.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1149,
   "id": "fc76b2c1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Features</th>\n",
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       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.37</td>\n",
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       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.02</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.62\n",
       "2                               spring  1.42\n",
       "4                          Mist_cloudy  1.37\n",
       "5                                    3  1.23\n",
       "8                                    8  1.13\n",
       "10                              Sunday  1.12\n",
       "6                                    5  1.11\n",
       "9                                    9  1.11\n",
       "11                                   7  1.08\n",
       "7                                    6  1.07\n",
       "1                              holiday  1.03\n",
       "3   Light rain_Light snow_Thunderstorm  1.02"
      ]
     },
     "execution_count": 1149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13271e54",
   "metadata": {},
   "source": [
    "It hass improved  in month 7 so we can keep in month 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1151,
   "id": "e05a0c3e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
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       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday      7   10  \n",
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      ]
     },
     "execution_count": 1151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ADDING MONTH 10 TO CHECK IF MODEL IMPROVES\n",
    "X_train_rfe[10]=X_train[10].astype(float)\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1152,
   "id": "9d63e822",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.797\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     149.7\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          3.24e-162\n",
      "Time:                        06:45:14   Log-Likelihood:                 445.00\n",
      "No. Observations:                 510   AIC:                            -862.0\n",
      "Df Residuals:                     496   BIC:                            -802.7\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4085      0.011     35.898      0.000       0.386       0.431\n",
      "Year                                   0.2469      0.009     27.063      0.000       0.229       0.265\n",
      "holiday                               -0.0836      0.029     -2.857      0.004      -0.141      -0.026\n",
      "spring                                -0.1980      0.013    -15.229      0.000      -0.224      -0.172\n",
      "Light rain_Light snow_Thunderstorm    -0.3212      0.028    -11.596      0.000      -0.376      -0.267\n",
      "Mist_cloudy                           -0.0907      0.010     -9.235      0.000      -0.110      -0.071\n",
      "3                                      0.0635      0.016      3.860      0.000       0.031       0.096\n",
      "5                                      0.1230      0.018      6.744      0.000       0.087       0.159\n",
      "6                                      0.1483      0.019      7.734      0.000       0.111       0.186\n",
      "8                                      0.1538      0.017      8.840      0.000       0.120       0.188\n",
      "9                                      0.1937      0.018     10.475      0.000       0.157       0.230\n",
      "Sunday                                -0.0498      0.013     -3.806      0.000      -0.075      -0.024\n",
      "7                                      0.1264      0.019      6.656      0.000       0.089       0.164\n",
      "10                                     0.1168      0.018      6.438      0.000       0.081       0.153\n",
      "==============================================================================\n",
      "Omnibus:                       81.612   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              333.309\n",
      "Skew:                          -0.652   Prob(JB):                     4.20e-73\n",
      "Kurtosis:                       6.740   Cond. No.                         8.68\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe14 = sm.add_constant(X_train_rfe)\n",
    "lm14 = sm.OLS(y_train,X_train_rfe14.astype(float)).fit()\n",
    "print(lm14.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1153,
   "id": "28277e93",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.68\n",
       "2                               spring  1.45\n",
       "4                          Mist_cloudy  1.41\n",
       "5                                    3  1.23\n",
       "12                                  10  1.17\n",
       "8                                    8  1.14\n",
       "10                              Sunday  1.14\n",
       "9                                    9  1.13\n",
       "6                                    5  1.12\n",
       "11                                   7  1.09\n",
       "7                                    6  1.08\n",
       "3   Light rain_Light snow_Thunderstorm  1.06\n",
       "1                              holiday  1.03"
      ]
     },
     "execution_count": 1153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1154,
   "id": "2ca40bc6",
   "metadata": {},
   "outputs": [
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       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday      7   10     11  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False  False  1.0  False  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False   True  0.0  False  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False  False  0.0  False  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True  False  0.0  False  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False  False  0.0  False  "
      ]
     },
     "execution_count": 1154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ADDING MONTH 11 TO CHECK IF MODEL IMPROVES\n",
    "X_train_rfe[11]=X_train[11]\n",
    "X_train_rfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1155,
   "id": "1771fa18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.797\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     139.0\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          2.70e-161\n",
      "Time:                        06:46:32   Log-Likelihood:                 445.42\n",
      "No. Observations:                 510   AIC:                            -860.8\n",
      "Df Residuals:                     495   BIC:                            -797.3\n",
      "Df Model:                          14                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4023      0.013     30.292      0.000       0.376       0.428\n",
      "Year                                   0.2471      0.009     27.072      0.000       0.229       0.265\n",
      "holiday                               -0.0865      0.029     -2.938      0.003      -0.144      -0.029\n",
      "spring                                -0.1925      0.014    -13.391      0.000      -0.221      -0.164\n",
      "Light rain_Light snow_Thunderstorm    -0.3217      0.028    -11.612      0.000      -0.376      -0.267\n",
      "Mist_cloudy                           -0.0905      0.010     -9.210      0.000      -0.110      -0.071\n",
      "3                                      0.0660      0.017      3.956      0.000       0.033       0.099\n",
      "5                                      0.1291      0.019      6.642      0.000       0.091       0.167\n",
      "6                                      0.1544      0.020      7.596      0.000       0.114       0.194\n",
      "8                                      0.1598      0.019      8.577      0.000       0.123       0.196\n",
      "9                                      0.1999      0.020     10.134      0.000       0.161       0.239\n",
      "Sunday                                -0.0501      0.013     -3.825      0.000      -0.076      -0.024\n",
      "7                                      0.1325      0.020      6.573      0.000       0.093       0.172\n",
      "10                                     0.1231      0.019      6.340      0.000       0.085       0.161\n",
      "11                                     0.0175      0.019      0.904      0.366      -0.021       0.056\n",
      "==============================================================================\n",
      "Omnibus:                       83.073   Durbin-Watson:                   2.014\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              344.709\n",
      "Skew:                          -0.660   Prob(JB):                     1.40e-75\n",
      "Kurtosis:                       6.805   Cond. No.                         10.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe15 = sm.add_constant(X_train_rfe)\n",
    "lm15 = sm.OLS(y_train,X_train_rfe15.astype(float)).fit()\n",
    "print(lm15.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1156,
   "id": "684b1d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe=X_train_rfe.drop([11],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1158,
   "id": "0bf0d7cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.797\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     149.7\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          3.24e-162\n",
      "Time:                        06:47:10   Log-Likelihood:                 445.00\n",
      "No. Observations:                 510   AIC:                            -862.0\n",
      "Df Residuals:                     496   BIC:                            -802.7\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4085      0.011     35.898      0.000       0.386       0.431\n",
      "Year                                   0.2469      0.009     27.063      0.000       0.229       0.265\n",
      "holiday                               -0.0836      0.029     -2.857      0.004      -0.141      -0.026\n",
      "spring                                -0.1980      0.013    -15.229      0.000      -0.224      -0.172\n",
      "Light rain_Light snow_Thunderstorm    -0.3212      0.028    -11.596      0.000      -0.376      -0.267\n",
      "Mist_cloudy                           -0.0907      0.010     -9.235      0.000      -0.110      -0.071\n",
      "3                                      0.0635      0.016      3.860      0.000       0.031       0.096\n",
      "5                                      0.1230      0.018      6.744      0.000       0.087       0.159\n",
      "6                                      0.1483      0.019      7.734      0.000       0.111       0.186\n",
      "8                                      0.1538      0.017      8.840      0.000       0.120       0.188\n",
      "9                                      0.1937      0.018     10.475      0.000       0.157       0.230\n",
      "Sunday                                -0.0498      0.013     -3.806      0.000      -0.075      -0.024\n",
      "7                                      0.1264      0.019      6.656      0.000       0.089       0.164\n",
      "10                                     0.1168      0.018      6.438      0.000       0.081       0.153\n",
      "==============================================================================\n",
      "Omnibus:                       81.612   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              333.309\n",
      "Skew:                          -0.652   Prob(JB):                     4.20e-73\n",
      "Kurtosis:                       6.740   Cond. No.                         8.68\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe16= sm.add_constant(X_train_rfe)\n",
    "lm16 = sm.OLS(y_train,X_train_rfe16.astype(float)).fit()\n",
    "print(lm16.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1159,
   "id": "409e95db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Year</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spring</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mist_cloudy</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light rain_Light snow_Thunderstorm</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>holiday</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Features   VIF\n",
       "0                                 Year  1.68\n",
       "2                               spring  1.45\n",
       "4                          Mist_cloudy  1.41\n",
       "5                                    3  1.23\n",
       "12                                  10  1.17\n",
       "8                                    8  1.14\n",
       "10                              Sunday  1.14\n",
       "9                                    9  1.13\n",
       "6                                    5  1.12\n",
       "11                                   7  1.09\n",
       "7                                    6  1.08\n",
       "3   Light rain_Light snow_Thunderstorm  1.06\n",
       "1                              holiday  1.03"
      ]
     },
     "execution_count": 1159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe.astype(float)\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73644f4a",
   "metadata": {},
   "source": [
    "We have considered all columns and checked.Now can we choose\n",
    "Out all the models model lm14 seems to give good result so we choose it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486f7fb6",
   "metadata": {},
   "source": [
    "# Error terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1160,
   "id": "5e0abac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_cnt = lm14.predict(X_train_rfe14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1161,
   "id": "f7a24048",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1162,
   "id": "e94d709f",
   "metadata": {},
   "outputs": [],
   "source": [
    "res=y_train - y_train_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1163,
   "id": "ff974492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Errors')"
      ]
     },
     "execution_count": 1163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot((res), bins = 20)\n",
    "fig.suptitle('Error Terms', fontsize = 20)                  # Plot heading \n",
    "plt.xlabel('Errors', fontsize = 18)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1164,
   "id": "02f7787b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                             'const',\n",
       "                                     'Year',\n",
       "                                  'holiday',\n",
       "                                   'spring',\n",
       "       'Light rain_Light snow_Thunderstorm',\n",
       "                              'Mist_cloudy',\n",
       "                                          3,\n",
       "                                          5,\n",
       "                                          6,\n",
       "                                          8,\n",
       "                                          9,\n",
       "                                   'Sunday',\n",
       "                                          7,\n",
       "                                         10],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe14.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1168,
   "id": "97abed24",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_vars=['atemp','Humidity','windspeed','Count']\n",
    "bike_test[num_vars] = scaler.fit_transform(bike_test[num_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1169,
   "id": "ebc6b76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_test = bike_test.pop('Count')\n",
    "X_test = bike_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1170,
   "id": "36422396",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new=X_train_rfe14.drop(['const'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1171,
   "id": "a8fcf0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_new = X_test[X_train_new.columns]\n",
    "\n",
    "# Adding a constant variable \n",
    "X_test_new = sm.add_constant(X_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1172,
   "id": "09a39570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                             'const',\n",
       "                                     'Year',\n",
       "                                  'holiday',\n",
       "                                   'spring',\n",
       "       'Light rain_Light snow_Thunderstorm',\n",
       "                              'Mist_cloudy',\n",
       "                                          3,\n",
       "                                          5,\n",
       "                                          6,\n",
       "                                          8,\n",
       "                                          9,\n",
       "                                   'Sunday',\n",
       "                                          7,\n",
       "                                         10],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe14.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1173,
   "id": "6e2bf417",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lm14.predict(X_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1174,
   "id": "72848774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_pred')"
      ]
     },
     "execution_count": 1174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(y_test,y_pred)\n",
    "fig.suptitle('y_test vs y_pred', fontsize=20)              # Plot heading \n",
    "plt.xlabel('y_test', fontsize=18)                          # X-label\n",
    "plt.ylabel('y_pred', fontsize=16)           "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d9c77f",
   "metadata": {},
   "source": [
    "We have a model that seems good enough to predict demand of bikes. The actual and predicted cnt i.e demand significantly overlapped, thus indicating that the model is able to explain the change in demand very well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1175,
   "id": "be2b241f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1176,
   "id": "d936ae55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10573120032920406"
      ]
     },
     "execution_count": 1176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1177,
   "id": "ffe419c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8029017439604782"
      ]
     },
     "execution_count": 1177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_squared = r2_score(y_test, y_pred)\n",
    "r_squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1178,
   "id": "98c21164",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_t=X_train_new.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1179,
   "id": "64eb2743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(X_t,res)\n",
    "fig.suptitle('Independent vars vs res', fontsize=20)              # Plot heading \n",
    "plt.xlabel('Independent variables', fontsize=18)                          # X-label\n",
    "plt.ylabel('Residuals', fontsize=16)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a175622",
   "metadata": {},
   "source": [
    "the regression is likely to suffer from autocorrelation because demand next year will certainly be dependent on demand this year. Hence, error terms in different observations will surely be correlated with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1180,
   "id": "39465fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>spring</th>\n",
       "      <th>Light rain_Light snow_Thunderstorm</th>\n",
       "      <th>Mist_cloudy</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  holiday  spring  Light rain_Light snow_Thunderstorm  Mist_cloudy  \\\n",
       "653   1.0      0.0     0.0                                 0.0          0.0   \n",
       "576   1.0      0.0     0.0                                 0.0          0.0   \n",
       "426   1.0      0.0     1.0                                 0.0          1.0   \n",
       "728   1.0      0.0     1.0                                 0.0          0.0   \n",
       "482   1.0      0.0     0.0                                 0.0          1.0   \n",
       "\n",
       "       3    5    6    8    9  Sunday      7   10  \n",
       "653  0.0  0.0  0.0  0.0  0.0   False  False  1.0  \n",
       "576  0.0  0.0  0.0  0.0  0.0   False   True  0.0  \n",
       "426  1.0  0.0  0.0  0.0  0.0   False  False  0.0  \n",
       "728  0.0  0.0  0.0  0.0  0.0    True  False  0.0  \n",
       "482  0.0  0.0  0.0  0.0  0.0   False  False  0.0  "
      ]
     },
     "execution_count": 1180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1181,
   "id": "922580ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([                             'const',\n",
      "                                     'Year',\n",
      "                                  'holiday',\n",
      "                                   'spring',\n",
      "       'Light rain_Light snow_Thunderstorm',\n",
      "                              'Mist_cloudy',\n",
      "                                          3,\n",
      "                                          5,\n",
      "                                          6,\n",
      "                                          8,\n",
      "                                          9,\n",
      "                                   'Sunday',\n",
      "                                          7,\n",
      "                                         10],\n",
      "      dtype='object')\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Count   R-squared:                       0.797\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     149.7\n",
      "Date:                Wed, 14 Feb 2024   Prob (F-statistic):          3.24e-162\n",
      "Time:                        06:56:27   Log-Likelihood:                 445.00\n",
      "No. Observations:                 510   AIC:                            -862.0\n",
      "Df Residuals:                     496   BIC:                            -802.7\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================================\n",
      "                                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------------\n",
      "const                                  0.4085      0.011     35.898      0.000       0.386       0.431\n",
      "Year                                   0.2469      0.009     27.063      0.000       0.229       0.265\n",
      "holiday                               -0.0836      0.029     -2.857      0.004      -0.141      -0.026\n",
      "spring                                -0.1980      0.013    -15.229      0.000      -0.224      -0.172\n",
      "Light rain_Light snow_Thunderstorm    -0.3212      0.028    -11.596      0.000      -0.376      -0.267\n",
      "Mist_cloudy                           -0.0907      0.010     -9.235      0.000      -0.110      -0.071\n",
      "3                                      0.0635      0.016      3.860      0.000       0.031       0.096\n",
      "5                                      0.1230      0.018      6.744      0.000       0.087       0.159\n",
      "6                                      0.1483      0.019      7.734      0.000       0.111       0.186\n",
      "8                                      0.1538      0.017      8.840      0.000       0.120       0.188\n",
      "9                                      0.1937      0.018     10.475      0.000       0.157       0.230\n",
      "Sunday                                -0.0498      0.013     -3.806      0.000      -0.075      -0.024\n",
      "7                                      0.1264      0.019      6.656      0.000       0.089       0.164\n",
      "10                                     0.1168      0.018      6.438      0.000       0.081       0.153\n",
      "==============================================================================\n",
      "Omnibus:                       81.612   Durbin-Watson:                   2.020\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              333.309\n",
      "Skew:                          -0.652   Prob(JB):                     4.20e-73\n",
      "Kurtosis:                       6.740   Cond. No.                         8.68\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(X_train_rfe14.columns)\n",
    "print(lm14.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d1bc85",
   "metadata": {},
   "source": [
    "Final recommendations for the company: Demand is higher in month of 3, 5 , 6, 8, 9 ,7 and 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9155b4b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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