DayBike Rental prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff9e0d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154caa6f",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afdd3803",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>801</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</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>0.196364</td>\n",
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       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</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>2011-01-05</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>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</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  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4e4827",
   "metadata": {},
   "source": [
    "## Basic checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d9ddb57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dteday</th>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
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       "      <td>1600</td>\n",
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      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7331c5ac",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
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       "  </thead>\n",
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       "      <th>726</th>\n",
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       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
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       "      <td>2451</td>\n",
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       "      <td>2012-12-29</td>\n",
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       "      <td>2012-12-30</td>\n",
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       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
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       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
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      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
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    "data.tail()"
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   "cell_type": "code",
   "execution_count": 5,
   "id": "02fd179f",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     731 non-null    int64  \n",
      " 1   dteday      731 non-null    object \n",
      " 2   season      731 non-null    int64  \n",
      " 3   yr          731 non-null    int64  \n",
      " 4   mnth        731 non-null    int64  \n",
      " 5   holiday     731 non-null    int64  \n",
      " 6   weekday     731 non-null    int64  \n",
      " 7   workingday  731 non-null    int64  \n",
      " 8   weathersit  731 non-null    int64  \n",
      " 9   temp        731 non-null    float64\n",
      " 10  atemp       731 non-null    float64\n",
      " 11  hum         731 non-null    float64\n",
      " 12  windspeed   731 non-null    float64\n",
      " 13  casual      731 non-null    int64  \n",
      " 14  registered  731 non-null    int64  \n",
      " 15  cnt         731 non-null    int64  \n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</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>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</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>183.500000</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>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</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>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</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>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</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>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b84dd0a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=731, step=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bf6d14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2dbf91",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f61b0fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0711310a",
   "metadata": {},
   "source": [
    "## Checking outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b5a407e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1800 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box=data[['instant', 'season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday',\n",
    "       'weathersit', 'temp', 'hum', 'windspeed', 'casual', 'cnt']]\n",
    "plt.figure(figsize=(25,25),facecolor='white')\n",
    "plotnum=1\n",
    "for c in box:\n",
    "    if(plotnum<14):\n",
    "        ax=plt.subplot(4,4,plotnum)\n",
    "        sns.boxplot(box[c])\n",
    "    plotnum+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680ad015",
   "metadata": {},
   "source": [
    "* IT CAN BE SEEN THAT THE GIVEN BELOW COLUMNS HAVE OUTLIERS\n",
    "\n",
    "* hum\n",
    "* windspeed\n",
    "* casual"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6e5e86",
   "metadata": {},
   "source": [
    "## MAKING SEPARATE VISUALIZATIONS FOR FEATURES CONTAINING OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0445533f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b63h+Ye3LZw3KZw3KZw1ysA7lK3MN+nqO0a+AcSg1NjZGc3Nz2cMY0axB+axB+axB+axB+cpaA2tfPmtQPmtQPmuQg3UoX8Y18BESAAAAID0BAwAAAEgvTcCYO3du2UMY8axB+axB+axB+axB+cpaA2tfPmtQPmtQPmuQg3UoX8Y1SPMdGAAAAAB9SfMODAAAAIC+CBgAAABAeoMeMFatWhUnnXRS1NbWxmc+85ke24uiiIULF0ZtbW3U19fHL37xi8Ee4rB3oDX45je/GfX19VFfXx9nnXVW3H///SWMcng70Bp0uPfee2P06NGxfPnyQRzdyFDJGtxxxx3R0NAQdXV1ce655w7yCIe/A63BE088ERdffHFMmzYt6urq4oYbbihhlMPX5ZdfHhMmTIhTTjml1+2H8vHYc4HyHWgNNmzYEDNmzIjDDjssrrnmmhJGOPx5Pla+A63BzTffHPX19dHQ0BCNjY1x1113lTDK4c1z4vIdaA3uuOOOeMlLXhINDQ3R0NAQn/zkJ0sYZRfFINqzZ08xefLkYtOmTcWzzz5b1NfXF2vXru22zy233FLMmjWr2LdvX3HPPfcUp59++mAOcdirZA3uvvvuYseOHUVRFMWKFSuswQCrZA069nvDG95QzJ49u/jOd75TwkiHr0rW4I9//GMxZcqU4tFHHy2Koigee+yxMoY6bFWyBp/61KeKD3/4w0VRFMUf/vCH4qUvfWnx7LPPljHcYeknP/lJsWbNmqKurq7X7Yfq8dhzgfJVsgaPPfZYsXr16uJf/uVfis9//vMljXT48nysfJWswVNPPVXs27evKIqiuP/++4uTTjqpjKEOW54Tl6+SNbj99tuLiy66qKQR9jSo78BYvXp11NbWxuTJk+MFL3hBXHrppXHzzTd32+fmm2+Oyy67LKqqquLMM8+Mxx9/PLZu3TqYwxzWKlmDs846K1760pdGRMSZZ54ZmzdvLmOow1YlaxARsWTJknjb294WEyZMKGGUw1sla/Ctb30r3vrWt8YrXvGKiAjrMMAqWYOqqqp46qmnoiiK2LlzZxx99NFRXV1d0oiHn3POOSeOPvroPrcfqsdjzwXKV8kaTJgwIaZPnx5jxowpaZTDm+dj5atkDY444oioqqqKiIinn366888MDM+Jy1fpGmQyqAFjy5Ytcfzxx3f+fdKkSbFly5Z+78PB6+/8fuMb34jZs2cPxtBGjEr/HXzve9+L97///YM9vBGhkjV46KGH4o9//GO8/vWvj9NOOy2WLVs22MMc1ipZgyuvvDLWr18fxx13XEydOjWuvfbaGDXKVzcNlkP1eOy5QPnMb/k8HytfpWvwve99L04++eS46KKL4j/+4z8Gc4jDnufE5av038E999wT06ZNi9mzZ8fatWsHc4g9DOqvsope/sfW/UtmJftw8Pozv7fffnt84xvf8Hm/AVbJGvzDP/xDfPazn43Ro0cP1rBGlErWYM+ePbFmzZr48Y9/HLt27YoZM2bEmWeeGa961asGa5jDWiVr8MMf/jAaGhritttui02bNsXMmTPj7LPPjiOPPHKwhjmiHarHY88Fymd+y+f5WPkqXYO3vOUt8Za3vCV++tOfxsc//vG49dZbB2N4I4LnxOWrZA1e85rXxKOPPhpHHHFErFixIt785jfHxo0bB2uIPQxqwJg0aVL89re/7fz75s2b47jjjuv3Phy8Suf3V7/6Vbzvfe+LlStXxrhx4wZziMNeJWvQ3Nwcl156aUREbN++PVasWBHV1dXx5je/eTCHOmxV+rNo/Pjx8aIXvShe9KIXxTnnnBP333+/gDFAKlmDG264IT760Y9GVVVV1NbWxgknnBAbNmyI008/fbCHOyIdqsdjzwXKZ37L5/lY+fr77+Ccc86JTZs2xfbt22P8+PGDMcRhz3Pi8lWyBl1/cXThhRfG/Pnzy/13MJhfuNHe3l6ccMIJxcMPP9z5JSEPPvhgt31+8IMfdPvirunTpw/mEIe9Stbg0UcfLf7iL/6iuPvuu0sa5fBWyRp0NWfOHF9YNMAqWYN169YV5513XtHe3l48/fTTRV1dXfHAAw+UNOLhp5I1eP/7319cddVVRVEUxe9///viuOOOK7Zt21bCaIevRx55pM8v8TxUj8eeC5SvP49DV111lS/xPAQ8HytfJWuwcePGzi/xXLNmTXHcccd1/p3nz3Pi8lWyBlu3bu283//f//1fcfzxx5f672BQ34FRXV0dX/rSl+KCCy6IvXv3xuWXXx51dXXx1a9+NSIi3v/+98eFF14YK1asiNra2njhC1/ov80bYJWswSc/+cloa2uL+fPnd16nubm5zGEPK5WsAYdWJWswZcqUmDVrVtTX18eoUaPife97X5//3ST9V8kafPzjH4/3vOc9MXXq1CiKIj772c/6rdcAeuc73xl33HFHbN++PSZNmhSf+MQnor29PSIO7eOx5wLlq2QNfv/730djY2M8+eSTMWrUqPjiF78Y69at8xGuAeL5WPkqWYPvfve7sWzZshgzZkyMHTs2vv3tb/u41QDynLh8lazB8uXL4ytf+UpUV1fH2LFj46abbir130FVUfTywRcAAACARHydOwAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAF0am1t9V+FAgD9cuGFF8bjjz9e8f5lPt844ogjSrldYGBUlz0AAABg6FqxYkXZQwBGCO/AALrZu3dvXHHFFVFXVxdvfOMbY9euXfH6178+mpubIyJi+/btUVNTExERN954Y7z5zW+Oiy++OE444YT40pe+FF/4whfi1FNPjTPPPDN27NhR4pkAAAPhc5/7XFx33XUREfHBD34wzjvvvIiI+PGPfxzvete7oqamJrZv3x6tra0xZcqUHs8jIiLWrFkT06ZNixkzZsT111/feey1a9fG6aefHg0NDVFfXx8bN26M1tbWOPnkk2POnDlRX18fb3/72+OZZ57pPM65554bp512WlxwwQWxdevWiIjYtGlTzJo1K0477bQ4++yzY8OGDRER8cgjj8SMGTNi+vTp8fGPf3zQ5gw4NAQMoJuNGzfGBz7wgVi7dm0cddRR8d3vfvc593/wwQfjW9/6VqxevTo+9rGPxQtf+ML45S9/GTNmzIhly5YN0qgBgEPlnHPOiTvvvDMiIpqbm2Pnzp3R3t4ed911V5x99tnd9u3recR73/veuO666+Kee+7ptv9Xv/rVWLRoUdx3333R3NwckyZNioiIX//61zF37tz41a9+FUceeWR8+ctfjvb29liwYEEsX7481qxZE5dffnl87GMfi4iIuXPnxpIlS2LNmjVxzTXXxPz58yMiYtGiRTFv3ry499574+Uvf/khnSfg0BMwgG5OOOGEaGhoiIiI0047LVpbW59z/ze84Q3x4he/OF72spfFS17ykrj44osjImLq1KkHvC4AkN9pp50Wa9asiaeeeioOO+ywmDFjRjQ3N8edd97ZI2D09jziiSeeiMcffzzOPffciIh497vf3bn/jBkz4l//9V/js5/9bDz66KMxduzYiIg4/vjj47WvfW1ERLzrXe+Ku+66K37961/Hgw8+GDNnzoyGhoZoamqKzZs3x86dO+NnP/tZvOMd74iGhob4+7//+853Ztx9993xzne+s8ftAkOT78AAujnssMM6/zx69OjYtWtXVFdXx759+yIiYvfu3X3uP2rUqM6/jxo1Kvbs2TMIIwYADqUxY8ZETU1N3HDDDXHWWWdFfX193H777bFp06aYMmVKt317ex5RFEVUVVX1euy/+Zu/iTPOOCNuueWWuOCCC+LrX/96TJ48ucf+VVVVURRF1NXV9XgXx5NPPhlHHXVU3Hfffb3eRl+3DQw93oEBHFBNTU2sWbMmIiKWL19e8mgAgMF2zjnnxDXXXBPnnHNOnH322fHVr341GhoaKooDRx11VLzkJS+Ju+66KyIivvnNb3Zue/jhh2Py5MmxcOHCuOSSS+JXv/pVRET85je/6QwV//mf/xmve93r4qSTTopt27Z1Xt7e3h5r166NI488Mk444YT4zne+ExERRVHE/fffHxERr33ta+Omm27qcbvA0CRgAAf0T//0T/GVr3wlzjrrrNi+fXvZwwEABtnZZ58dW7dujRkzZsQxxxwThx9+eI+PjzyXG264IT7wgQ/EjBkzOj8mEhHx7W9/O0455ZRoaGiIDRs2xGWXXRYREVOmTImlS5dGfX197NixI+bNmxcveMELYvny5fGRj3wkpk2bFg0NDfGzn/0sIv4cJ77xjW/EtGnToq6uLm6++eaIiLj22mvj+uuvj+nTp8cTTzwxgDMClKGqKIqi7EEAAABERLS2tsab3vSmePDBB8seCpCMd2AAAAAA6XkHBgAAAJCed2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6VX3Z+fx48dHTU3NIRoKADCUtLa2xvbt28seBgAwQvQrYNTU1ERzc/OhGgsAMIQ0NjaWPQQAYATxERIAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMAAAAID0qssewFC0ZMmSaGlpKXsYh8SWLVsiImLixIklj2Rg1dbWxoIFC8oeBgAAAAdJwDgILS0tcd+D62PvC48ueygDbvQzT0RExO+fHT53jdHP7Ch7CAAAADxPw+dV6iDb+8KjY9fJF5Y9jAE3dsOKiIhhdW4d5wQAAMDQ5TswAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0hMwAAAAgPQEDAAAACA9AQMAAABIT8AAAAAA0ksRMJYsWRJLliwpexgAA87PNwAAGBjVZQ8gIqKlpaXsIQAcEn6+AQDAwEjxDgwAAACA5yJgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAyitra2mDdvXsydOzfmzZsXbW1tPbZfccUVccEFF8SsWbOipaWlx/aFCxdGS0tLLFy4sNv1+9rW0tISF110UTQ3N3fb3tLS0jmW9773vTFr1qyYO3dutLW1dR6r65879p8/f360tLTE/Pnzez2Hruc5f/78Xrf3pqWlJWbPnt3nGPp7vN7m7bmu29s+lVxv/2MczDj3v53+3m6lxwUAGMoEDIBBtHTp0li/fn089NBDsX79+li2bFmP7Rs3boxnn302du/eHU1NTT22P/DAA9HU1BQPPPBAt+v3ta2pqSmefvrpuPrqq7ttb2pq6hzLI488Ert3746HHnooli1b1nmsrn/u2H/dunXR1NQU69at6/Ucup7nunXret3em6ampti1a1efY+jv8Xqbt+e6bm/7VHK9/Y9xMOPc/3b6e7uVHhcAYCgTMAAGSVtbW6xcubLbZStXruz2W/f9t7e2tna+C6OtrS1WrVoVRVFEa2trFEURq1at6nyHQm/bmpubo7W1NSIidu7c2W17x+X7u+WWWzqPtXLlyli5cmWP/bv+ues59HYe+2/vTUtLS7djrlixovN2V65cGStWrOjX8brqOjcd81XJPpVcb/9j9Pe8e7vtlpaWft1upcf1LgwAYKirLnsAERFbtmyJXbt2xaJFi8oeSkVaWlpi1J+KsodBhUbtfjJaWp4aMvcvhpeWlpYYO3ZsRPz5t+F79uzptr29vT2WLVsWH/zgB2Pp0qXR3t7e4xhNTU1x4403xtKlS2Pfvn3dtu3duzeWLVsWRVH0uu3qq6/u95j37NkTVVVVneM7kK7nENHzPPff3pv932nS3t7ebQxFUXTbdqDjddV13jrma//r9rZP1znt63r7H6O/593bbTc1NfXrdis97sEeBwAgiwO+A+NrX/taNDY2RmNjY2zbtm0wxgQwLN16663dXohHRBRFET/60Y86t/em450Jt956a48AsmfPnvjRj37U57adO3ce1Fg7xlkURY8x97Zvxzl0jLPrdfbf3pve3g3SdQzPdXsH0nVuOuarkn0qud7+x+jvefd2262trf263UqPe7DHAQDI4oDvwJg7d27MnTs3IiIaGxsPySAmTpwYERHXXnvtITn+QFu0aFGsefixsodBhfYdfmTUTj5myNy/GF66vvPn/PPPj+9///vdXuRWVVXFzJkzO7f/z//8T49j1NTUdG5fsWJFt1BRXV0dM2fOjKIoet12+OGHH1TEqKqqiqIoOt8F8VwRo+s59Hae+2/vTU1NTY+I0XUMfc1ZJbrOW8d8VbJP1znt63r7H6O/593bbU+aNCk2b95c8e0+n/MGABhKfAcGwCCZM2dOVFd378ZjxoyJyy67rHP7mDFjelxv8eLFndtHjer+Y3v06NFx2WWX9bntYD5CUl1d3TmOMWPG9Bjz/rqeQ8c4u15n/+296TjHrtfpOMb+Y6jkeF11nZuO+apkn0qut/8xDmac+9/O4sWL+3W7lR73YI8DAJCFgAEwSMaNGxezZ8/udtns2bNj3LhxfW6vqamJ2trazu2zZs2KqqqqqKmpiaqqqpg1a1aMGzeuz22NjY2d7+A44ogjum3vuHx/F110UeexZs+eHbNnz+6xf9c/dz2H3s5j/+29qa2t7XbMCy+8sPN2Z8+eHRdeeGG/jtdV17npmK9K9qnkevsfo7/n3dtt19bW9ut2Kz3uwR4HACCLFF/iCTBSzJkzJzZu3Bh79+7t9bfic+bMifXr18dvfvObqKqq6vHOhDlz5kRra2ssXLgwrrvuuh7vfOht2+LFi2PRokVx9dVXx7Jlyzq3L1y4MP7t3/4t9u7dG+3t7bF169Z4xSte0Xm91tbWbn/u2L+qqio+9KEPxRe+8IUoiqLPdzRs3LgxqqqqKv7N/+LFi2PBggVx/PHH9zqG/h6vt3l7ruv2tk8l19v/GAczzv1vp7+3W+lxAQCGsqriQN/O1kVjY2M0NzcP+CA6PiM+VL6joOM7MHadfOGBdx5ixm74839VOJzObeyGFXGa78CgJEPt5xv0x6F6XgAA0BsfIQEAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSqy57ABERtbW1ZQ8B4JDw8w0AAAZGioCxYMGCsocAcEj4+QYAAAPDR0gAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSEzAAAACA9AQMAAAAID0BAwAAAEhPwAAAAADSqy57AEPV6Gd2xNgNK8oexoAb/UxbRMSwOrfRz+yIiGPKHgYAAADPg4BxEGpra8sewiGzZcueiIiYOHE4veA/ZlivGQAAwEggYByEBQsWlD0EAAAAGFF8BwYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQnoABAAAApCdgAAAAAOkJGAAAAEB6AgYAAACQXlVRFEWlO48fPz5qamoOyUC2bdsWL3vZyw7JsYcqc9KTOenJnPTOvPRkTnoyJz31Z05aW1tj+/bth3hEAAB/1q+AcSg1NjZGc3Nz2cNIxZz0ZE56Mie9My89mZOezElP5gQAyMpHSAAAAID0BAwAAAAgvTQBY+7cuWUPIR1z0pM56cmc9M689GROejInPZkTACCrNN+BAQAAANCXNO/AAAAAAOiLgAEAAACkV3rAWLVqVZx00klRW1sbn/nMZ8oezqCqqamJqVOnRkNDQzQ2NkZExI4dO2LmzJlx4oknxsyZM+OPf/xj5/6f/vSno7a2Nk466aT44Q9/WNawB9zll18eEyZMiFNOOaXzsoOZhzVr1sTUqVOjtrY2Fi5cGEP501G9zcnVV18dEydOjIaGhmhoaIgVK1Z0bhsJc/Lb3/423vCGN8SUKVOirq4urr322ogY2feVvuZkJN9Xdu/eHaeffnpMmzYt6urq4qqrroqIkX0/6WtORvL9BAAYoooS7dmzp5g8eXKxadOm4tlnny3q6+uLtWvXljmkQfXKV76y2LZtW7fL/vmf/7n49Kc/XRRFUXz6058uPvzhDxdFURRr164t6uvri927dxcPP/xwMXny5GLPnj2DPuZD4Sc/+UmxZs2aoq6urvOyg5mH6dOnFz/72c+Kffv2FbNmzSpWrFgx+CczQHqbk6uuuqr4/Oc/32PfkTInv/vd74o1a9YURVEUTz75ZHHiiScWa9euHdH3lb7mZCTfV/bt21c89dRTRVEUxZ/+9Kfi9NNPL+65554RfT/pa05G8v0EABiaSn0HxurVq6O2tjYmT54cL3jBC+LSSy+Nm2++ucwhle7mm2+OOXPmRETEnDlz4r//+787L7/00kvjsMMOixNOOCFqa2tj9erVJY504Jxzzjlx9NFHd7usv/OwdevWePLJJ2PGjBlRVVUVl112Wed1hqLe5qQvI2VOjj322HjNa14TEREvfvGLY8qUKbFly5YRfV/pa076MhLmpKqqKo444oiIiGhvb4/29vaoqqoa0feTvuakLyNhTgCAoanUgLFly5Y4/vjjO/8+adKk53zyPdxUVVXFG9/4xjjttNPia1/7WkREPPbYY3HsscdGxJ9fnPzhD3+IiJE3V/2dhy1btsSkSZN6XD7cfOlLX4r6+vq4/PLLO98CPxLnpLW1NX75y1/GGWec4b7y/+k6JxEj+76yd+/eaGhoiAkTJsTMmTPdT6L3OYkY2fcTAGDoKTVgFL18dva5fis03Nx9993xi1/8IlauXBnXX399/PSnP+1z35E+Vx36moeRMD/z5s2LTZs2xX333RfHHnts/OM//mNEjLw52blzZ7ztbW+LL37xi3HkkUf2ud9Impf952Sk31dGjx4d9913X2zevDlWr14dDz74YJ/7juQ5Gen3EwBg6Ck1YEyaNCl++9vfdv598+bNcdxxx5U4osHVca4TJkyIt7zlLbF69eo45phjYuvWrRERsXXr1pgwYUJEjLy56u88TJo0KTZv3tzj8uHkmGOOidGjR8eoUaPiiiuu6PwI0Uiak/b29njb294Wf/u3fxtvfetbI8J9pa85Gen3lYiIo446Kl7/+tfHqlWrRvz9pMP+c+J+AgAMJaUGjOnTp8fGjRvjkUceiT/96U9x0003xSWXXFLmkAbN008/HU899VTnn//3f/83TjnllLjkkkti6dKlERGxdOnS+Ku/+quIiLjkkkvipptuimeffTYeeeSR2LhxY5x++umljf9Q6+88HHvssfHiF784fv7zn0dRFLFs2bLO6wwXHS++IiK+973vdf4PJSNlToqiiL/7u7+LKVOmxIc+9KHOy0fyfaWvORnJ95Vt27bF448/HhERu3btiltvvTVOPvnkEX0/6WtORvL9BAAYogbzG0N7c8sttxQnnnhiMXny5KKpqans4QyaTZs2FfX19UV9fX3x6le/uvPct2/fXpx33nlFbW1tcd555xVtbW2d12lqaiomT55cvOpVrxpW3/x+6aWXFi9/+cuL6urqYuLEicXXv/71g5qHe++9t6irqysmT55cfOADHyj27dtXxukMiN7m5F3veldxyimnFFOnTi0uvvji4ne/+13n/iNhTu68884iIoqpU6cW06ZNK6ZNm1bccsstI/q+0tecjOT7yv333180NDQUU6dOLerq6opPfOITRVEc3M/W4T4nI/l+AgAMTVVF4T9xBwAAAHIr9SMkAAAAAJUQMAAAAID0BAwAAAAgPQEDAAAASE/AAAAAANITMIBB8Z73vCeWL19e9jAAAIAhSsAAAAAA0hMwYIRatmxZ1NfXx7Rp0+Ld7353fP/7348zzjgjTj311Dj//PPjsccei4iIn/zkJ9HQ0BANDQ1x6qmnxlNPPRV33HFHvOlNb+o81pVXXhk33nhjRER88pOfjOnTp8cpp5wSc+fOjaIoyjg9AABgmBEwYARau3ZtfOpTn4rbbrst7r///rj22mvjda97Xfz85z+PX/7yl3HppZfG5z73uYiIuOaaa+L666+P++67L+68884YO3bscx77yiuvjHvvvTcefPDB2LVrV/zgBz8YjFMCAACGOQEDRqDbbrst3v72t8f48eMjIuLoo4+OzZs3xwUXXBBTp06Nz3/+87F27dqIiHjta18bH/rQh+K6666Lxx9/PKqrq5/z2LfffnucccYZMXXq1Ljttts6jwMAAPB8CBgwAhVFEVVVVd0uW7BgQVx55ZXxwAMPxL//+7/H7t27IyLiox/9aHz961+PXbt2xZlnnhkbNmyI6urq2LdvX+d1O/bdvXt3zJ8/P5YvXx4PPPBAXHHFFZ3bAAAAng8BA0agv/zLv4z/+q//ira2toiI2LFjRzzxxBMxceLEiIhYunRp576bNm2KqVOnxkc+8pFobGyMDRs2xCtf+cpYt25dPPvss/HEE0/Ej3/844j4/0PG+PHjY+fOnf7XEQAAYMA893vBgWGprq4uPvaxj8W5554bo0ePjlNPPTWuvvrqeMc73hETJ06MM888Mx555JGIiPjiF78Yt99+e4wePTpe/epXx+zZs+Owww6Lv/7rv476+vo48cQT49RTT42IiKOOOiquuOKKmDp1atTU1MT06dPLPE0AAGAYqSr8FwEAAABAcj5CAgAAAKQnYAAAAADpCRgAAABAegIGAAAAkJ6AAQAAAKQnYAAAAADpCRgAAABAev8PSPF+fOBEif8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box=data[[   'hum', 'windspeed', 'casual']]\n",
    "plt.figure(figsize=(15,10),facecolor='white')\n",
    "plotnum=1\n",
    "for c in box:\n",
    "    if(plotnum<14):\n",
    "        ax=plt.subplot(2,2,plotnum)\n",
    "        sns.boxplot(box[c])\n",
    "    plotnum+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b57432d",
   "metadata": {},
   "source": [
    "## HANDLING OUTLIERS\n",
    "## WINDSPEED FEATURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b834cbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.04200740041710149\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4229798236729151"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit=data.windspeed.mean() - 3*data.windspeed.std()##calculating lower limit\n",
    "print(lower_limit)\n",
    "\n",
    "upper_limit=data.windspeed.mean() + 3*data.windspeed.std()#calculating upper limit\n",
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb5d0eaf",
   "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>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>49</th>\n",
       "      <td>50</td>\n",
       "      <td>2011-02-19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.399167</td>\n",
       "      <td>0.391404</td>\n",
       "      <td>0.187917</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>532</td>\n",
       "      <td>1103</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>2012-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.527500</td>\n",
       "      <td>0.524604</td>\n",
       "      <td>0.567500</td>\n",
       "      <td>0.441563</td>\n",
       "      <td>486</td>\n",
       "      <td>4896</td>\n",
       "      <td>5382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "49        50  2011-02-19       1   0     2        0        6           0   \n",
       "432      433  2012-03-08       1   1     3        0        4           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "49            1  0.399167  0.391404  0.187917   0.507463     532        1103   \n",
       "432           1  0.527500  0.524604  0.567500   0.441563     486        4896   \n",
       "\n",
       "      cnt  \n",
       "49   1635  \n",
       "432  5382  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['windspeed']>upper_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54b42052",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['windspeed']>upper_limit,\"windspeed\"]=np.median(data.windspeed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a7af64",
   "metadata": {},
   "source": [
    "### Humidity Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed50f998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20060677751243433\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.055181348342558"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit=data.hum.mean() - 3*data.hum.std()##calculating lower limit\n",
    "print(lower_limit)\n",
    "\n",
    "upper_limit=data.hum.mean() + 3*data.hum.std()#calculating upper limit\n",
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b36f52a",
   "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>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>49</th>\n",
       "      <td>50</td>\n",
       "      <td>2011-02-19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.399167</td>\n",
       "      <td>0.391404</td>\n",
       "      <td>0.187917</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>532</td>\n",
       "      <td>1103</td>\n",
       "      <td>1635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>2011-03-10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.389091</td>\n",
       "      <td>0.385668</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261877</td>\n",
       "      <td>46</td>\n",
       "      <td>577</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "49       50  2011-02-19       1   0     2        0        6           0   \n",
       "68       69  2011-03-10       1   0     3        0        4           1   \n",
       "\n",
       "    weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "49           1  0.399167  0.391404  0.187917   0.180975     532        1103   \n",
       "68           3  0.389091  0.385668  0.000000   0.261877      46         577   \n",
       "\n",
       "     cnt  \n",
       "49  1635  \n",
       "68   623  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['hum']<lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "71988853",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['hum']<lower_limit,\"hum\"]=np.median(data.hum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e738472e",
   "metadata": {},
   "source": [
    "### Casual Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dbfa397e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1211.6909942657294\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2908.0439354421997"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit=data.casual.mean() - 3*data.casual.std()##calculating lower limit\n",
    "print(lower_limit)\n",
    "\n",
    "upper_limit=data.casual.mean() + 3*data.casual.std()#calculating upper limit\n",
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70aaac27",
   "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>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>184</th>\n",
       "      <td>185</td>\n",
       "      <td>2011-07-04</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.726667</td>\n",
       "      <td>0.665417</td>\n",
       "      <td>0.637917</td>\n",
       "      <td>0.081479</td>\n",
       "      <td>3065</td>\n",
       "      <td>2978</td>\n",
       "      <td>6043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>442</td>\n",
       "      <td>2012-03-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.514167</td>\n",
       "      <td>0.505046</td>\n",
       "      <td>0.755833</td>\n",
       "      <td>0.110704</td>\n",
       "      <td>3155</td>\n",
       "      <td>4681</td>\n",
       "      <td>7836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>463</td>\n",
       "      <td>2012-04-07</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.426129</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.274871</td>\n",
       "      <td>3252</td>\n",
       "      <td>3605</td>\n",
       "      <td>6857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>505</td>\n",
       "      <td>2012-05-19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.566908</td>\n",
       "      <td>0.456250</td>\n",
       "      <td>0.083975</td>\n",
       "      <td>3410</td>\n",
       "      <td>4884</td>\n",
       "      <td>8294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>513</td>\n",
       "      <td>2012-05-27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.641425</td>\n",
       "      <td>0.697083</td>\n",
       "      <td>0.215171</td>\n",
       "      <td>3283</td>\n",
       "      <td>3308</td>\n",
       "      <td>6591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>533</td>\n",
       "      <td>2012-06-16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.631667</td>\n",
       "      <td>0.594708</td>\n",
       "      <td>0.504167</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2963</td>\n",
       "      <td>4739</td>\n",
       "      <td>7702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>624</td>\n",
       "      <td>2012-09-15</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.608333</td>\n",
       "      <td>0.585867</td>\n",
       "      <td>0.501667</td>\n",
       "      <td>0.247521</td>\n",
       "      <td>3160</td>\n",
       "      <td>5554</td>\n",
       "      <td>8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>2012-10-06</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.554167</td>\n",
       "      <td>0.538521</td>\n",
       "      <td>0.664167</td>\n",
       "      <td>0.268025</td>\n",
       "      <td>3031</td>\n",
       "      <td>4934</td>\n",
       "      <td>7965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "184      185  2011-07-04       3   0     7        1        1           0   \n",
       "441      442  2012-03-17       1   1     3        0        6           0   \n",
       "462      463  2012-04-07       2   1     4        0        6           0   \n",
       "504      505  2012-05-19       2   1     5        0        6           0   \n",
       "512      513  2012-05-27       2   1     5        0        0           0   \n",
       "532      533  2012-06-16       2   1     6        0        6           0   \n",
       "623      624  2012-09-15       3   1     9        0        6           0   \n",
       "644      645  2012-10-06       4   1    10        0        6           0   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "184           2  0.726667  0.665417  0.637917   0.081479    3065        2978   \n",
       "441           2  0.514167  0.505046  0.755833   0.110704    3155        4681   \n",
       "462           1  0.437500  0.426129  0.254167   0.274871    3252        3605   \n",
       "504           1  0.600000  0.566908  0.456250   0.083975    3410        4884   \n",
       "512           1  0.690000  0.641425  0.697083   0.215171    3283        3308   \n",
       "532           1  0.631667  0.594708  0.504167   0.166667    2963        4739   \n",
       "623           1  0.608333  0.585867  0.501667   0.247521    3160        5554   \n",
       "644           1  0.554167  0.538521  0.664167   0.268025    3031        4934   \n",
       "\n",
       "      cnt  \n",
       "184  6043  \n",
       "441  7836  \n",
       "462  6857  \n",
       "504  8294  \n",
       "512  6591  \n",
       "532  7702  \n",
       "623  8714  \n",
       "644  7965  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['casual']>upper_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b1cf952",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['casual']>upper_limit,\"casual\"]=np.median(data.casual)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786fff52",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b199582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "040433fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"dteday\",\"registered\",\"atemp\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254d41ce",
   "metadata": {},
   "source": [
    "## Train-Test-Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bde459c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.drop([\"cnt\"],axis=1)\n",
    "y=data[\"cnt\"]\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=42,test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2c8cc0",
   "metadata": {},
   "source": [
    "## Using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "47fb00b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "362a7915",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9070a752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6083c931",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "94af2949",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b44876a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.892135009991626\n",
      "mean_absolute_error:    467.68026859028396\n",
      "mean_squared_error:     415704.55634532456\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,y_pred))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,y_pred))\n",
    "print(\"mean_squared_error:    \",mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10070e0",
   "metadata": {},
   "source": [
    "## Applying Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6670fe3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "bg=BaggingRegressor(base_estimator=lr,n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e255bb4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingRegressor(base_estimator=LinearRegression(), n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingRegressor</label><div class=\"sk-toggleable__content\"><pre>BaggingRegressor(base_estimator=LinearRegression(), n_estimators=200)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingRegressor(base_estimator=LinearRegression(), n_estimators=200)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de101a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction=bg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bf634abe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.891957654518037\n",
      "mean_absolute_error:    467.18135973130643\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,y_prediction))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,y_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f812980f",
   "metadata": {},
   "source": [
    "## Training Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c437b0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.8554052345271269\n",
      "mean_absolute_error:    517.904276394901\n"
     ]
    }
   ],
   "source": [
    "y_pred_train=lr.predict(x_train)\n",
    "print(\"r2_score:              \",r2_score(y_train,y_pred_train))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_train,y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcec39fa",
   "metadata": {},
   "source": [
    "### CONCLUSION OF LINEAR REGRESSION WITH BAGGING\n",
    "* It can be seen that with linear regression we are geting 89% of r2 score , it means our 89% of data lies on best fit line.\n",
    "* After applying boosting technique there is not even single percentage improvement to r2_score as well as mean_absolute error.\n",
    "* Now lets have a try with another algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd143e9a",
   "metadata": {},
   "source": [
    "### Knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7fcec4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn=KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "13909176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "15e5f000",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pdct=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5898ee34",
   "metadata": {},
   "source": [
    "### Testing Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de263f10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.7783929206642453\n",
      "mean_absolute_error:    648.6907103825138\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,y_pdct))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,y_pdct))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550e628f",
   "metadata": {},
   "source": [
    "### CONCLUSION FROM KNN\n",
    "* It can be seen that knn doesn't able to give good r2 score as Linear regression did.\n",
    "* Error rate is also high comparitevly to Linear regression.\n",
    "* Hence , we can conclude that knn model is not much suitable for this data.\n",
    "### USING DECISION TREE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cd825e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt=DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9f1aed2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba1f3ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pd=dt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8456acfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.8750522212624804\n",
      "mean_absolute_error:    485.43715846994536\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,y_pd))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,y_pd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "add3992f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "54a4abca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr=RandomForestRegressor(n_estimators=200,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f511149c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=200, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=200, random_state=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=200, random_state=10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3f77379c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat=rfr.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be4b3c",
   "metadata": {},
   "source": [
    "### Testing and training report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "db4f2fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.9243887038208018\n",
      "mean_absolute_error:    377.4008196721311\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,y_hat))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f544d823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.9881047169309796\n",
      "mean_absolute_error:    140.4174087591241\n",
      "mean_squared_error:     43920.06406916059\n"
     ]
    }
   ],
   "source": [
    "y_hat_train=rfr.predict(x_train)\n",
    "print(\"r2_score:              \",r2_score(y_train,y_hat_train))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_train,y_hat_train))\n",
    "print(\"mean_squared_error:    \",mean_squared_error(y_train,y_hat_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ba1b04",
   "metadata": {},
   "source": [
    "#### CHECKING WITH RANDOMIZED SEARCH CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3861bada",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomizedSearchCV' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [48]\u001b[0m, in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m random_grid \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_estimators\u001b[39m\u001b[38;5;124m\"\u001b[39m:n_estimators,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_features\u001b[39m\u001b[38;5;124m\"\u001b[39m:max_features,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_depth\u001b[39m\u001b[38;5;124m\"\u001b[39m:max_depth,\n\u001b[0;32m     10\u001b[0m               \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin_samples_split\u001b[39m\u001b[38;5;124m\"\u001b[39m:min_samples_split,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin_samples_leaf\u001b[39m\u001b[38;5;124m\"\u001b[39m:min_samples_leaf}\n\u001b[0;32m     12\u001b[0m rf_r1 \u001b[38;5;241m=\u001b[39m RandomForestRegressor(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 13\u001b[0m rf_r\u001b[38;5;241m=\u001b[39m\u001b[43mRandomizedSearchCV\u001b[49m(estimator\u001b[38;5;241m=\u001b[39mrf_r1,param_distributions\u001b[38;5;241m=\u001b[39mrandom_grid,n_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m     14\u001b[0m                           random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     16\u001b[0m rf_r\u001b[38;5;241m.\u001b[39mfit(x_train,y_train)\n\u001b[0;32m     17\u001b[0m rf_best_params\u001b[38;5;241m=\u001b[39mrf_r\u001b[38;5;241m.\u001b[39mbest_params_\n",
      "\u001b[1;31mNameError\u001b[0m: name 'RandomizedSearchCV' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "n_estimators=[int(x) for x in np.linspace(start=200,stop=2000,num=10)]\n",
    "max_features=[\"auto\",\"sqrt\"]\n",
    "max_depth=[int(x) for x in np.linspace(10,110,num=11)]\n",
    "max_depth.append(None)\n",
    "min_samples_split=[2,5,10]\n",
    "min_samples_leaf=[1,2,4]\n",
    "\n",
    "random_grid = {\"n_estimators\":n_estimators,\"max_features\":max_features,\"max_depth\":max_depth,\n",
    "              \"min_samples_split\":min_samples_split,\"min_samples_leaf\":min_samples_leaf}\n",
    "\n",
    "rf_r1 = RandomForestRegressor(random_state=42)\n",
    "rf_r=RandomizedSearchCV(estimator=rf_r1,param_distributions=random_grid,n_iter=100,cv=3,verbose=3,\n",
    "                          random_state=42,n_jobs=-1)\n",
    "\n",
    "rf_r.fit(x_train,y_train)\n",
    "rf_best_params=rf_r.best_params_\n",
    "print(\"best parameters: \",rf_best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "73d345ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestRegressor(n_estimators=2000,random_state=42,min_samples_split= 2, min_samples_leaf= 1, max_features= 'sqrt', max_depth= None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ff6d49ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "RandomForestRegressor(max_features='sqrt', n_estimators=2000, random_state=42)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3cbe638c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6be14960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score:               0.9252180367696498\n",
      "mean_absolute_error:    355.5293825136612\n"
     ]
    }
   ],
   "source": [
    "print(\"r2_score:              \",r2_score(y_test,ypred))\n",
    "print(\"mean_absolute_error:   \",mean_absolute_error(y_test,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99098836",
   "metadata": {},
   "source": [
    "### CONCLUSION\n",
    "* WHEN WE APPLY RANDOM FOREST WITH DECISION TREE WE ARE GETTING 92% OF R2 SCORE .\n",
    "* WHEN WE APPLY RANDOM SEARCH CV IN ORDER TO GET BEST PARAMETERS THEN ALSO THERE IS NO CHANGE IN R2 SCORE AS WELL AS ERROR RATE.\n",
    "* HENCE, WE CAN CONSIDER THIS IS THE BEST SCORE."
   ]
  },
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