DataSet 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47489f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1e9c328",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=pd.read_excel(\"dataset_3.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb2185ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</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>620</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>621</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>623</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>624</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  yr  mnth  hr  holiday  weekday  weathersit  \\\n",
       "0      620 2011-01-29       1   0     1   1    False        6           1   \n",
       "1      621 2011-01-29       1   0     1   2    False        6           1   \n",
       "2      622 2011-01-29       1   0     1   3    False        6           1   \n",
       "3      623 2011-01-29       1   0     1   4    False        6           1   \n",
       "4      624 2011-01-29       1   0     1   6    False        6           1   \n",
       "\n",
       "   temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0  0.22  0.2273  0.64     0.1940       0          20   20  \n",
       "1  0.22  0.2273  0.64     0.1642       0          15   15  \n",
       "2  0.20  0.2121  0.64     0.1343       3           5    8  \n",
       "3  0.16  0.1818  0.69     0.1045       1           2    3  \n",
       "4  0.16  0.1818  0.64     0.1343       0           2    2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head() #First Five Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "063fb15a",
   "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>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</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>385</th>\n",
       "      <td>615</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>616</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>617</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>618</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>619</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  hr  holiday  weekday  weathersit  \\\n",
       "385      615 2011-01-28       1   0     1  20    False        5           2   \n",
       "386      616 2011-01-28       1   0     1  21    False        5           2   \n",
       "387      617 2011-01-28       1   0     1  22    False        5           1   \n",
       "388      618 2011-01-28       1   0     1  23    False        5           1   \n",
       "389      619 2011-01-29       1   0     1   0    False        6           1   \n",
       "\n",
       "     temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "385  0.24  0.2273  0.70     0.1940       1          61   62  \n",
       "386  0.22  0.2273  0.75     0.1343       1          57   58  \n",
       "387  0.24  0.2121  0.65     0.3582       0          26   26  \n",
       "388  0.24  0.2273  0.60     0.2239       1          22   23  \n",
       "389  0.22  0.1970  0.64     0.3582       2          26   28  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.tail() #Last Five Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "333e9a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(390, 16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cd2e0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "390"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3e06f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41526d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['weekday'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e48bbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  1,  4, 12,  9, 10, 22, 19,  6,  7, 13, 18, 14,  8,  2,  5,\n",
       "       15, 11, 28, 48, 47, 52, 42, 24, 16, 21, 17, 30, 27, 32, 26, 58, 62,\n",
       "       51, 40], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['casual'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c8cbc8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant                int64\n",
       "dteday        datetime64[ns]\n",
       "season                 int64\n",
       "yr                     int64\n",
       "mnth                   int64\n",
       "hr                     int64\n",
       "holiday                 bool\n",
       "weekday                int64\n",
       "weathersit             int64\n",
       "temp                 float64\n",
       "atemp                float64\n",
       "hum                  float64\n",
       "windspeed            float64\n",
       "casual                 int64\n",
       "registered             int64\n",
       "cnt                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bda40b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 390 entries, 0 to 389\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   instant     390 non-null    int64         \n",
      " 1   dteday      390 non-null    datetime64[ns]\n",
      " 2   season      390 non-null    int64         \n",
      " 3   yr          390 non-null    int64         \n",
      " 4   mnth        390 non-null    int64         \n",
      " 5   hr          390 non-null    int64         \n",
      " 6   holiday     390 non-null    bool          \n",
      " 7   weekday     390 non-null    int64         \n",
      " 8   weathersit  390 non-null    int64         \n",
      " 9   temp        390 non-null    float64       \n",
      " 10  atemp       390 non-null    float64       \n",
      " 11  hum         390 non-null    float64       \n",
      " 12  windspeed   390 non-null    float64       \n",
      " 13  casual      390 non-null    int64         \n",
      " 14  registered  390 non-null    int64         \n",
      " 15  cnt         390 non-null    int64         \n",
      "dtypes: bool(1), datetime64[ns](1), float64(4), int64(10)\n",
      "memory usage: 46.2 KB\n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09bb3f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58437b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "385    False\n",
       "386    False\n",
       "387    False\n",
       "388    False\n",
       "389    False\n",
       "Length: 390, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b93e0f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>weekday</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>390.000000</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>390.000000</td>\n",
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       "      <td>390.000000</td>\n",
       "      <td>390.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>805.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>11.687179</td>\n",
       "      <td>2.989744</td>\n",
       "      <td>1.484615</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.230424</td>\n",
       "      <td>0.613769</td>\n",
       "      <td>0.179416</td>\n",
       "      <td>5.576923</td>\n",
       "      <td>57.002564</td>\n",
       "      <td>62.579487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>112.727548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400514</td>\n",
       "      <td>6.980295</td>\n",
       "      <td>2.149884</td>\n",
       "      <td>0.663805</td>\n",
       "      <td>0.073095</td>\n",
       "      <td>0.069455</td>\n",
       "      <td>0.202361</td>\n",
       "      <td>0.138551</td>\n",
       "      <td>9.317478</td>\n",
       "      <td>49.070198</td>\n",
       "      <td>53.274838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>611.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.075800</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>708.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.181800</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.089600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>805.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.164200</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>902.750000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.272700</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.283600</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>88.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.424200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.582100</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>243.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           instant  season     yr        mnth          hr     weekday  \\\n",
       "count   390.000000   390.0  390.0  390.000000  390.000000  390.000000   \n",
       "mean    805.500000     1.0    0.0    1.800000   11.687179    2.989744   \n",
       "std     112.727548     0.0    0.0    0.400514    6.980295    2.149884   \n",
       "min     611.000000     1.0    0.0    1.000000    0.000000    0.000000   \n",
       "25%     708.250000     1.0    0.0    2.000000    6.000000    1.000000   \n",
       "50%     805.500000     1.0    0.0    2.000000   12.000000    3.000000   \n",
       "75%     902.750000     1.0    0.0    2.000000   18.000000    5.000000   \n",
       "max    1000.000000     1.0    0.0    2.000000   23.000000    6.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed      casual  \\\n",
       "count  390.000000  390.000000  390.000000  390.000000  390.000000  390.000000   \n",
       "mean     1.484615    0.220000    0.230424    0.613769    0.179416    5.576923   \n",
       "std      0.663805    0.073095    0.069455    0.202361    0.138551    9.317478   \n",
       "min      1.000000    0.060000    0.075800    0.210000    0.000000    0.000000   \n",
       "25%      1.000000    0.160000    0.181800    0.450000    0.089600    1.000000   \n",
       "50%      1.000000    0.220000    0.227300    0.600000    0.164200    3.000000   \n",
       "75%      2.000000    0.260000    0.272700    0.750000    0.283600    6.000000   \n",
       "max      3.000000    0.420000    0.424200    1.000000    0.582100   62.000000   \n",
       "\n",
       "       registered         cnt  \n",
       "count  390.000000  390.000000  \n",
       "mean    57.002564   62.579487  \n",
       "std     49.070198   53.274838  \n",
       "min      0.000000    1.000000  \n",
       "25%     17.000000   18.500000  \n",
       "50%     48.000000   52.000000  \n",
       "75%     83.750000   88.750000  \n",
       "max    236.000000  243.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ca5e809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'hr', 'holiday', 'weekday',\n",
       "       'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',\n",
       "       'registered', 'cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "853abe97",
   "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>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</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>620</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
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       "      <td>621</td>\n",
       "      <td>2011-01-29</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.1642</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>623</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>624</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>615</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>616</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
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       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>617</td>\n",
       "      <td>2011-01-28</td>\n",
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       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>618</td>\n",
       "      <td>2011-01-28</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>619</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  mnth  hr  holiday  weekday  weathersit  temp  \\\n",
       "0        620 2011-01-29       1     1   1    False        6           1  0.22   \n",
       "1        621 2011-01-29       1     1   2    False        6           1  0.22   \n",
       "2        622 2011-01-29       1     1   3    False        6           1  0.20   \n",
       "3        623 2011-01-29       1     1   4    False        6           1  0.16   \n",
       "4        624 2011-01-29       1     1   6    False        6           1  0.16   \n",
       "..       ...        ...     ...   ...  ..      ...      ...         ...   ...   \n",
       "385      615 2011-01-28       1     1  20    False        5           2  0.24   \n",
       "386      616 2011-01-28       1     1  21    False        5           2  0.22   \n",
       "387      617 2011-01-28       1     1  22    False        5           1  0.24   \n",
       "388      618 2011-01-28       1     1  23    False        5           1  0.24   \n",
       "389      619 2011-01-29       1     1   0    False        6           1  0.22   \n",
       "\n",
       "      atemp   hum  windspeed  casual  registered  cnt  \n",
       "0    0.2273  0.64     0.1940       0          20   20  \n",
       "1    0.2273  0.64     0.1642       0          15   15  \n",
       "2    0.2121  0.64     0.1343       3           5    8  \n",
       "3    0.1818  0.69     0.1045       1           2    3  \n",
       "4    0.1818  0.64     0.1343       0           2    2  \n",
       "..      ...   ...        ...     ...         ...  ...  \n",
       "385  0.2273  0.70     0.1940       1          61   62  \n",
       "386  0.2273  0.75     0.1343       1          57   58  \n",
       "387  0.2121  0.65     0.3582       0          26   26  \n",
       "388  0.2273  0.60     0.2239       1          22   23  \n",
       "389  0.1970  0.64     0.3582       2          26   28  \n",
       "\n",
       "[390 rows x 15 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.drop(['yr'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70911570",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>yr</th>\n",
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       "      <th>1</th>\n",
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       "      <td>2011-01-29</td>\n",
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       "      <td>0.1642</td>\n",
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       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>622</td>\n",
       "      <td>2011-01-29</td>\n",
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       "      <td>3</td>\n",
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       "      <td>6</td>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>623</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>624</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.16</td>\n",
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       "      <td>0.64</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>615</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>616</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>617</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>618</td>\n",
       "      <td>2011-01-28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>619</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  hr  holiday  weekday  weathersit  \\\n",
       "0        620 2011-01-29       1   0     1   1    False        6           1   \n",
       "1        621 2011-01-29       1   0     1   2    False        6           1   \n",
       "2        622 2011-01-29       1   0     1   3    False        6           1   \n",
       "3        623 2011-01-29       1   0     1   4    False        6           1   \n",
       "4        624 2011-01-29       1   0     1   6    False        6           1   \n",
       "..       ...        ...     ...  ..   ...  ..      ...      ...         ...   \n",
       "385      615 2011-01-28       1   0     1  20    False        5           2   \n",
       "386      616 2011-01-28       1   0     1  21    False        5           2   \n",
       "387      617 2011-01-28       1   0     1  22    False        5           1   \n",
       "388      618 2011-01-28       1   0     1  23    False        5           1   \n",
       "389      619 2011-01-29       1   0     1   0    False        6           1   \n",
       "\n",
       "     temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0    0.22  0.2273  0.64     0.1940       0          20   20  \n",
       "1    0.22  0.2273  0.64     0.1642       0          15   15  \n",
       "2    0.20  0.2121  0.64     0.1343       3           5    8  \n",
       "3    0.16  0.1818  0.69     0.1045       1           2    3  \n",
       "4    0.16  0.1818  0.64     0.1343       0           2    2  \n",
       "..    ...     ...   ...        ...     ...         ...  ...  \n",
       "385  0.24  0.2273  0.70     0.1940       1          61   62  \n",
       "386  0.22  0.2273  0.75     0.1343       1          57   58  \n",
       "387  0.24  0.2121  0.65     0.3582       0          26   26  \n",
       "388  0.24  0.2273  0.60     0.2239       1          22   23  \n",
       "389  0.22  0.1970  0.64     0.3582       2          26   28  \n",
       "\n",
       "[390 rows x 16 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ce5e0f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<p>390 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "     instant  dteday  season    yr  mnth    hr  holiday  weekday  weathersit  \\\n",
       "0       True    True    True  True  True  True     True     True        True   \n",
       "1       True    True    True  True  True  True     True     True        True   \n",
       "2       True    True    True  True  True  True     True     True        True   \n",
       "3       True    True    True  True  True  True     True     True        True   \n",
       "4       True    True    True  True  True  True     True     True        True   \n",
       "..       ...     ...     ...   ...   ...   ...      ...      ...         ...   \n",
       "385     True    True    True  True  True  True     True     True        True   \n",
       "386     True    True    True  True  True  True     True     True        True   \n",
       "387     True    True    True  True  True  True     True     True        True   \n",
       "388     True    True    True  True  True  True     True     True        True   \n",
       "389     True    True    True  True  True  True     True     True        True   \n",
       "\n",
       "     temp  atemp   hum  windspeed  casual  registered   cnt  \n",
       "0    True   True  True       True    True        True  True  \n",
       "1    True   True  True       True    True        True  True  \n",
       "2    True   True  True       True    True        True  True  \n",
       "3    True   True  True       True    True        True  True  \n",
       "4    True   True  True       True    True        True  True  \n",
       "..    ...    ...   ...        ...     ...         ...   ...  \n",
       "385  True   True  True       True    True        True  True  \n",
       "386  True   True  True       True    True        True  True  \n",
       "387  True   True  True       True    True        True  True  \n",
       "388  True   True  True       True    True        True  True  \n",
       "389  True   True  True       True    True        True  True  \n",
       "\n",
       "[390 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67a545f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "hr            0\n",
       "holiday       0\n",
       "weekday       0\n",
       "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64d6300d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0.0\n",
       "dteday        0.0\n",
       "season        0.0\n",
       "yr            0.0\n",
       "mnth          0.0\n",
       "hr            0.0\n",
       "holiday       0.0\n",
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       "weathersit    0.0\n",
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       "cnt           0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.isnull().sum()/data2.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91c0e748",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.replace('?', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57c9f8ec",
   "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",
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       "      <td>0.1818</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.1045</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>2011-01-28</td>\n",
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       "      <td>1</td>\n",
       "      <td>20</td>\n",
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       "      <td>0.24</td>\n",
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       "      <td>0.1940</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
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       "      <td>57</td>\n",
       "      <td>58</td>\n",
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       "      <td>0.3582</td>\n",
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       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>619</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  hr  holiday  weekday  weathersit  \\\n",
       "0        620 2011-01-29       1   0     1   1    False        6           1   \n",
       "1        621 2011-01-29       1   0     1   2    False        6           1   \n",
       "2        622 2011-01-29       1   0     1   3    False        6           1   \n",
       "3        623 2011-01-29       1   0     1   4    False        6           1   \n",
       "4        624 2011-01-29       1   0     1   6    False        6           1   \n",
       "..       ...        ...     ...  ..   ...  ..      ...      ...         ...   \n",
       "385      615 2011-01-28       1   0     1  20    False        5           2   \n",
       "386      616 2011-01-28       1   0     1  21    False        5           2   \n",
       "387      617 2011-01-28       1   0     1  22    False        5           1   \n",
       "388      618 2011-01-28       1   0     1  23    False        5           1   \n",
       "389      619 2011-01-29       1   0     1   0    False        6           1   \n",
       "\n",
       "     temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0    0.22  0.2273  0.64     0.1940       0          20   20  \n",
       "1    0.22  0.2273  0.64     0.1642       0          15   15  \n",
       "2    0.20  0.2121  0.64     0.1343       3           5    8  \n",
       "3    0.16  0.1818  0.69     0.1045       1           2    3  \n",
       "4    0.16  0.1818  0.64     0.1343       0           2    2  \n",
       "..    ...     ...   ...        ...     ...         ...  ...  \n",
       "385  0.24  0.2273  0.70     0.1940       1          61   62  \n",
       "386  0.22  0.2273  0.75     0.1343       1          57   58  \n",
       "387  0.24  0.2121  0.65     0.3582       0          26   26  \n",
       "388  0.24  0.2273  0.60     0.2239       1          22   23  \n",
       "389  0.22  0.1970  0.64     0.3582       2          26   28  \n",
       "\n",
       "[390 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a33d299",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9494b72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mnth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ranjan</td>\n",
       "      <td>0</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prakash</td>\n",
       "      <td>1</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Surekha</td>\n",
       "      <td>2</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prajakta</td>\n",
       "      <td>3</td>\n",
       "      <td>april</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>4</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nena</td>\n",
       "      <td>5</td>\n",
       "      <td>june</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  weekday   mnth\n",
       "0    Ranjan        0    jan\n",
       "1   Prakash        1    feb\n",
       "2   Surekha        2    mar\n",
       "3  Prajakta        3  april\n",
       "4     Vinod        4    may\n",
       "5      Nena        5   june"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe1\n",
    "df1 = {'Name':['Ranjan', 'Prakash', 'Surekha', 'Prajakta','Vinod', 'Nena'],\n",
    "       'weekday':[0,1,2,3,4,5],\n",
    "      'mnth':['jan','feb','mar','april','may','june']}\n",
    "df1 = pd.DataFrame(df1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d14210d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ranjan</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prakash</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Surekha</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prajakta</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nena</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  cnt\n",
       "0    Ranjan    5\n",
       "1   Prakash   15\n",
       "2   Surekha   35\n",
       "3  Prajakta   65\n",
       "4     Vinod  105\n",
       "5      Nena  155"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe2\n",
    "df2 = {'Name':['Ranjan', 'Prakash', 'Surekha', 'Prajakta','Vinod', 'Nena'],\n",
    "                    'cnt':[5,15,35,65,105,155],}\n",
    "df2 = pd.DataFrame(df2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe295957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>weekday</th>\n",
       "      <th>mnth</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ranjan</td>\n",
       "      <td>0</td>\n",
       "      <td>jan</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prakash</td>\n",
       "      <td>1</td>\n",
       "      <td>feb</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Surekha</td>\n",
       "      <td>2</td>\n",
       "      <td>mar</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prajakta</td>\n",
       "      <td>3</td>\n",
       "      <td>april</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>4</td>\n",
       "      <td>may</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nena</td>\n",
       "      <td>5</td>\n",
       "      <td>june</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  weekday   mnth  cnt\n",
       "0    Ranjan        0    jan    5\n",
       "1   Prakash        1    feb   15\n",
       "2   Surekha        2    mar   35\n",
       "3  Prajakta        3  april   65\n",
       "4     Vinod        4    may  105\n",
       "5      Nena        5   june  155"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging two dataframes\n",
    "df = pd.merge(df1, df2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ad5f2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='registered', ylabel='atemp'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xcZg0aRLuvfde/OhHP0JMzKW13AMPPIDdu3ejubkZTqcTOTk52Lx5M/Ly8mTHxGrwFEy9uxsNbd263+vFqHHVMjJez9hxMQ5cHFB3HyA18VvtGBFFGyWv34YvgMyICyAiIiLrscR9gIiIiIiMwgUQERER2Q4XQERERGQ7XAARERGR7XABRERERLZjeCkMu6tr7ULjmR7DLps1enwKLZxjpMfx3X+8BTWn2jF7fAoWZrtU92OVc9EqcVqF1fJptXhJGhdABmnv6cOa0hpU1LZ6txVku1BSlANnUnzUj0+hhXOM9Di+jW3dWLajCmd7Lni3pSTFY0/xAmSkyitLoVesWrBKnFZhtXxaLV4KjR+BGWRNaQ2qTrh9tlWdcGN1abUtxqfQwjlGehxf/8UPAJztuYBbdhxQ1I9VzkWrxGkVVsun1eKl0LgAMkBdaxcqalvR73cPyn5BQEVtK+rd3VE9PoUWzjHS4/juP94SsPjxONtzAZWD/i85GKuci1aJ0yqslk+rxUvycAFkgMYzPUEfb2iL7JPJ6PEptHCOkR7Ht+ZUe9DHjzWdldWPVc5Fq8RpFVbLp9XiJXm4ADJA5sjg34+YkBrZL9YZPT6FFs4x0uP4zho3Iujjs8enyOrHKueiVeK0Cqvl02rxkjxcABlgousyFGS7EOtw+GyPdThQkO2K+JUFRo9PoYVzjPQ4voumjEKKxBc/U5LiZV8NZpVz0SpxWoXV8mm1eEkeLoAMUlKUg/zJaT7b8ienoaQoxxbjU2jhHCM9ju+e4gUBiyDPVWBKWOVctEqcVmG1fFotXgqN1eBF6FkNvt7djYa2bsPuKWH0+BRaOMdIj+NbWduKY01nw74PkFXORavEaRVWy6fV4rUbJa/fXACJ0HMBRERERNpQ8vrNj8CIiIjIdrgAIiIiItvhAoiIiIhshwsgIiIish0WQzWIWEVh/212qTocDfO0+hyk4g82Lz3nbPX82pFZjplZ4lDKqnFbCRdAOhOrKDx/UioEAThU1+bdlpIU71NrKRqrDkdDdWWrz0Eq/seWzcCmNz4RnZcAQbc5Wz2/dmSWY2aWOJSyatxWxMvgRUTyMvg7XjqCqhPugKJ6ocQ6HMifnIZXVlyjaTxGEsuF1eZp9TlIxZ88NA6dX18UnRcA3eZs9fzakVmOmVniUMqqcZsFL4M3KamKwnJEW9XhaKiubPU5BIv/bM8FyXnpNWer59eOzHLMzBKHUlaN26q4ANJRqIrCckRL1eFoqK5s9TlocT7603LOVs+vHZnlmJklDqWsGrdVcQGko1AVheWIlqrD0VBd2epz0OJ89KflnK2eXzsyyzEzSxxKWTVuq+ICSEdSFYXliLaqw9FQXdnqcwgWf0pSvOS89Jqz1fNrR2Y5ZmaJQymrxm1VXADpTKyi8PxJqcibmOqzzb/KdjRWHY6G6spWn4NU/HuKF0jOS885Wz2/dmSWY2aWOJSyatxWxKvAROhRDFWsorD/NrtUHY6GeVp9DlLxB5uXnnO2en7tyCzHzCxxKGXVuI3GavBhYjV4IiIi6+Fl8ERERERBcAFEREREtsMFEBEREdkOF0BERERkO1wAERERke2wGrzB6lq70HimB7EOB/oFwTSXPHri8o9HarsWfUd6X7WsFq8W5MTt30btXL95DgD9AiyRK6seVyL6BhdABmnv6cOa0hpU1LYGPFaQ7UJJUQ6cfjdDNCqugmwXHls2A5ve+CRgu5I4pfqW00c4+6pltXi1ICdusTYpSfE423NBch+5YynZ3whWPa5EFIgfgRlkTWkNqk64RR+rOuHG6tJqnSO6RCyuqhNuLN1xQHS7kjil+pbTRzj7qmW1eLUgJ26xNoMXP2L7yB1Lyf5GsOpxJaJAhi+Adu7ciaysLCQmJiI3NxeVlZWSbQ8cOID8/HykpqZi6NChmDp1KrZt2xbQ7rXXXsP06dORkJCA6dOn4/XXX4/kFBSra+1CRW0r+iXuQdkvCKiobUW9W9/Kv1Jx9QsCzvZcEN0uN85gfYfqI5x91bJavFqQE3eoc1dsHyVjyd3fCFY9rkQkztAFUFlZGdatW4eNGzeiuroaCxcuxJIlS9DU1CTaftiwYVi1ahUqKirw+eefY9OmTdi0aRNeeOEFb5tDhw6hsLAQy5cvx4cffojly5fjtttuw+HDh/WaVkiNZ3pktWto0/cPqty4/MmJM1TfwfoIZ1+1rBavFuTErfQckZqrWZ8DwVj1uBKROEMXQE899RRWrFiBe+65B9OmTcP27duRkZGBZ599VrR9Tk4OioqKcOWVV2LChAn44Q9/iBtuuMHnXaPt27fj+uuvx4YNGzB16lRs2LAB3/nOd7B9+3adZhVa5sgkWe0mpOr75Uq5cfmTE2eovoP1Ec6+alktXi3IiVvpOSI1V7M+B4Kx6nElInGGLYD6+vpw9OhRLF682Gf74sWLcfDgQVl9VFdX4+DBg1i0aJF326FDhwL6vOGGG4L22dvbi87OTp+fSJrougwF2S7EOhyij8c6HCjIdul+dYlUXLEOB1KS4kW3y40zWN+h+ghnX7WsFq8W5MQd6twV20fJWHL3N4JVjysRiTNsAeR2u9Hf34/09HSf7enp6Whubg6677hx45CQkIA5c+aguLgY99xzj/ex5uZmxX1u3boVTqfT+5ORkaFiRsqUFOUgf3Ka6GP5k9NQUpQT8RjEiMWVPzkNe4oXiG5XEqdU33L6CGdftawWrxbkxC3WJsXvCig5czXrcyAYqx5XIgpkWDX4r776CpdffjkOHjyIvLw87/af/vSn+PWvf42//OUvkvvW19ejq6sLH3zwAR588EE888wzKCoqAgAMGTIEv/rVr7y/A8BvfvMbrFixAufPnxftr7e3F729vd7fOzs7kZGRoUs1+Hp3NxrauhEX48DFAfPcB8gTl388Utu16DvS+6pltXi1ICdu/zZq52rW50AwVj2uRNFOSTV4w+4DlJaWhtjY2IB3ZlpaWgLewfGXlZUFAJg5cyb+9re/YfPmzd4Fz+jRoxX3mZCQgISEBDXTCFtWmjn/gErFpUW84fRhRL6sFq8W5MTt30btXK2YIyvGTES+DPsIbMiQIcjNzUV5ebnP9vLycsyfP192P4Ig+Lx7k5eXF9Dn22+/rahPIiIiim6G3gl6/fr1WL58OebMmYO8vDy88MILaGpqwsqVKwEAGzZswJdffolXXnkFALBjxw6MHz8eU6dOBXDpvkBPPPEEVq9e7e1z7dq1KCgowOOPP46lS5fiD3/4A9555x0cOHBA/wkSERGRKRm6ACosLERbWxu2bNmC06dPY8aMGdi7dy8yMzMBAKdPn/a5J9DAwAA2bNiA+vp6xMXFYdKkSfjZz36GH/3oR9428+fPx6uvvopNmzbhoYcewqRJk1BWVoa5c+fqPj8iIiIyJ8O+BG1mSr5ERUREROZgiS9BU/hCVaRWUrFaSVX6SFbCDreiuFmuypGKJ1ic+4+3oOZUO2aPT8HCbJesfdTGobRvs+XXQ6uK9GQOap43RGpxAWRBoSpSK6lYraQidyQrYavt22zVuaXieWzZDGx64xPRONu/7sOyHVU+BUVTkuLxX3fPxeNvHY9INXq5eTNbfoPFpaYiPZmDmucNjyuFix+BiTD7R2B3vHQEVSfcPkUZYx0O5E9Owysrrgn5eKi+pPZR0q/Wc9J6v0iRiid5aBw6v74oGufHX7YHVFMHgLgYBwQBquam1TlitvwGi8ufGeIkedQ8b3hcSYyS12/Dq8GTMqEqUlf8tVV2xWolFbkjWQlbbd9mq84dLJ6zPRck4xRb/ADAxQEhItXo5Z4jZsuvh1YV6ckc1D5veFwpXFwAWUyoitTVJ88GfXxwxWolFbkjWQlbbd9mq86ttFJ6OMKpRi/3HDFbfj20qkhP5qD2ecPjSuHid4AsJlRF6pyMlKCPD65YraQid6hPSsOphK22yrbZqnMrrZQejnCq0cs9R8yWXw+tKtKTOah93vC4Urj4DpDFhKpIXXCFS3bFaiUVuSNZCVtt32arzh0snpSkeMk4/QuJesTFOCJSjV7uOWK2/HpoVZGezEHt84bHlcLFBZAFhapIraRitZKK3JGshK22b7NV55aKZ0/xAsk49xQvCFgEpSTFY09xfsSq0cvNm9ny66FVRXoyBzXPG6Jw8SowEWa/CswjVEVqJRWrlVTkjmQl7HAripvlPiFS8QSLs7K2FceazgbcByiS1ejl9m22/HpoVZGezEHN84ZoMCWv31wAibDKAoiIiIi+wcvgiYiIiILgAoiIiIhshwsgIiIish0ugIiIiMh2uAAiIiIi2+GdoA2y/3gLak61ey95rmvtwuH6NgAOzJuYaulLPetau9B4piesS1ZD9aFkDE/blo7zaD53PuAyc61ji3SfctuKtfPfpnYuWuYgEvnUo28inl/WxgWQzhrburFsR5VPAUzPvXcGy5uYiud+mAunxF2Czai9pw9rSmtQUdvq3VaQ7UJJUY7seYTqQ8kYYm09Lt1ocAEyUuXfhl+L+YXTp9y2Yu3yJqbC4QAOftHm3ZaSFO9zHsqZi5Y5iEQ+9eibiOdXdOBHYDrzX/wACFj8AMChujasLq3WKyxNrCmtQdUJt8+2qhNuRfMI1YeSMcTaepztuYBbdhyQHZfSsSPRp9y2Yu0O1bX5LH4ABJyHcuaiZQ4ikU89+ibi+RUduADS0f7jLQEvOsFU1Lai3m2Nisd1rV2oqG1Fv999NfsFQfY8QvVR8dcW2WNI9TXY2Z4LqBR5d0hNbGqOk5I+5baVM28poeaiZQ4ikU89+ibi+RU9uADSUc2pdsX7NLRZ48nUeKYn6ONy5hGqj+qT7bLHCNWXx7Gms7LaaTG/cPqU21buvOWOO5iWOYhEPvXom4jnV/TgAkhHs8aNULzPhFRrfLEuc2Tw79LImUeoPnIyRsgeI1RfHrPHp8hqp8X8wulTblu585Y77mBa5iAS+dSjbyKeX9Ej7AXQyZMncerUKS1iiXqLpowKqFgdTEG2yzJXFkx0XYaCbBdiHQ6f7bEOh+x5hOqj4IpRsseQ6muwlKR42VeDaTG/cPqU21bOvKWEmouWOYhEPvXom4jnV/RQtQC6ePEiHnroITidTkyYMAGZmZlwOp3YtGkTLlyQ/x0XO9pTvCBgERQXE/hilTcxFSVFOXqFpYmSohzkT07z2ZY/OU3RPEL1oWQMsbYenqvAlNBifuH0KbetWLu8iamYPynVZ5v/eShnLlrmIBL51KNvIp5f0UFVNfiVK1fi9ddfx5YtW5CXlwcAOHToEDZv3oylS5fiueee0zxQPelRDb6ythXHms5670lT7+7G4bo2CIDl7wNU7+5GQ1t3WPfGCNWHkjE8bd3nevFVx9dh3wdIi/mF06fctmLt/LepnYuWOYhEPvXom4jnl/koef1WtQByOp149dVXsWTJEp/tb775Jn7wgx+go6NDaZemoscCiIiIiLSl5PVb1UdgiYmJmDBhQsD2CRMmYMiQIWq6JCIiItKNqgVQcXEx/u3f/g29vb3ebb29vfjpT3+KVatWaRYcERERUSSoKoVRXV2Nd999F+PGjcPVV18NAPjwww/R19eH73znO7j11lu9bXfv3q1NpEREREQaUbUAGjFiBP7+7//eZ1tGRoYmARERERFFmqoF0Msvv6x1HERERES6YTV4gzzzbi2qvnBjYbYL9397MvYfb8EbNV/CAeD7s8cFvUy7rrULjWd6EOsA+gVoegmmp+9IXtYpNcbg7U1t3ag51a74knWxvsuONOFQfRvyJ6XhH+dkBLRt6TiP5nPnJccS69PIPBnJjDGpFU1zUcqu5y/RYKoug29ra8PDDz+M9957Dy0tLRgYGPB5/MyZM5oFaIRIXgZ/8EQrbn/xSMh2yYmx+N/VBchI/ea26+09fVhTWoMKkQKeBdkulBTlwKngTtODifUdbp9yx3hs2QxseuMT0XkB39y0cHAu5PQ9a5wTn3zViYsD35zicTEO/Nfdc7Fz/xei4w0eS6zPvImpcDjgU1ldrzxpOUY0xKRWNM1FKSOf53bILxkv4vcBWrJkCb744gusWLEC6enpcPjdEvzOO+9U2qWpRHIBNOHB/5XdNiUpHtUPL/b+fsdLR1B1wi1a6TvW4UD+5DS8suIaVXGJ9R1un3LHSB4ah86vLwatYO6fCzl9BxPrcEi29Ywlt0+98qTlGNEQk1rRNBeljHye2yG/ZDwlr9+qPgI7cOAADhw44L0CjOR55t1aRe3P9lxAZW0rFma7UNfaJfkOCQD0CwIqaltR7+5W/HazVN/h9KlkjLM9ocunDM6F3L6DCbaoOdtzAb/7U5PsPvXKk1ZjRENMakXTXJQy+nke7fkl61F1H6CpU6fi66+/1jqWqFf1hVvxPseazgIAGs/0yGrf0NateIxQfavpU+kYcnhyEYm+/R2sawvdyI8eedJiDKXMGJNa0TQXpczwPI/m/JL1qFoA7dy5Exs3bsT+/fvR1taGzs5Onx8Slz9JvDBnMLPHpwAAMkdKf/9lsAmpyv/vKlTfavpUOoYcnlxEom9/8yemhm7kR488aTGGUmaMSa1omotSZnieR3N+yXpULYBGjBiBjo4OXHfddRg1ahRSUlKQkpKCESNGICVF/EWKgFXfyVbUPiUp3vuRz0TXZSjIdiHWEVg5Hrj0GXtBtkvV28tSfYfTp5IxUpLiJeflMTgXcvsOJljblKR43Pat8bL71CtPWo0RDTGpFU1zUcro53m055esR9UC6J/+6Z8wZMgQ/Pa3v8W7776Lffv2Yd++fXjvvfewb98+RX3t3LkTWVlZSExMRG5uLiorKyXb7t69G9dffz1cLheSk5ORl5eHt956y6fNrl274HA4An7Onz+vZqqaK7t3nqx2yYmx2FO8wGdbSVEO8ieLv4uUPzkNJUU5quMS6zvcPuWOsad4geS8gG+uzFLad844J+JifP8Qx8U4UHbvPMnxBo8l1mfexFTMn+T77pBeedJyDKXMGJNa0TQXpYx8ntshv2Qtqq4CS0pKQnV1NaZMmRLW4GVlZVi+fDl27tyJ/Px8PP/883jxxRfx2WefYfz48QHt161bh7Fjx+Lb3/42RowYgZdffhlPPPEEDh8+jJycS0+uXbt2Ye3atTh+/LjPvqNHj5Ydlx7V4He+d8L7pd77vz0ZlbWteP3YlwCEkPcBqnd3o6GtG3ExDlwcEDS9z4an70jeu0NqjMHbT53twbGms4rvAyTW9+//fBJVX7gD7gPkaes+14uvOr6WHEusTyPzZCQzxqRWNM1FKbuevxT9In4ZfEFBAR5++GF897vfVR0kAMydOxezZ8/Gs88+6902bdo0LFu2DFu3bpXVx5VXXonCwkI8/PDDAC4tgNatW4f29nbVcemxACIiIiJtRfwy+NWrV2Pt2rX4yU9+gpkzZyI+3vfmVldddVXIPvr6+nD06FE8+OCDPtsXL16MgwcPyopjYGAA586dw8iRI322d3V1ITMzE/39/Zg1axb+7d/+zfsOkZje3l6fyvb8IjcREVF0U7UAKiwsBADcfffd3m0OhwOCIMDhcKC/vz9kH263G/39/UhPT/fZnp6ejubmZllxPPnkk+ju7sZtt93m3TZ16lTs2rULM2fORGdnJ37xi18gPz8fH374IbKzxb+EvHXrVjz66KOyxiQiIiLrU7UAqq+v1ywA/7tIexZRoZSWlmLz5s34wx/+gFGjRnm3z5s3D/PmffNF4/z8fMyePRslJSV4+umnRfvasGED1q9f7/29s7OT1e2JiIiimKoFUGZmZtgDp6WlITY2NuDdnpaWloB3hfyVlZVhxYoV+P3vfx/ye0gxMTH41re+hdpa6bswJyQkICEhQX7wREREZGmqq8H/+te/xnPPPYf6+nocOnQImZmZ2L59O7KysrB06dKQ+w8ZMgS5ubkoLy/H97//fe/28vLyoPuXlpbi7rvvRmlpKf7u7/4u5DiCIKCmpgYzZ86UNzGdfVPZ/VJtqmBXTPhXVw71u5j9x1tUVVmXikF8Lsqq1MuJO1SbYBXmD9e3AXBg3sRUVVejyJmz2itdtKiYLXVM1VSx9/Q1xpkI1/BE3auFs4J4+JhDInlULYCeffZZPPzww1i3bh1++tOfer/zM2LECGzfvl3WAggA1q9fj+XLl2POnDnIy8vDCy+8gKamJqxcuRLApY+mvvzyS7zyyisALi1+7rjjDvziF7/AvHnzvO8eDR06FE6nEwDw6KOPYt68ecjOzkZnZyeefvpp1NTUYMeOHWqmGjFKKruLtU1JivepoeX/u38fjW3dWLajKmCfUFXWg8XrGUOAoKpKvZyq0aHaBKsw/+Duj3yqtgOX7uPz3A9zZVWlVjpnJRWvtaiYLXVM/2vFXDz+x+OKqtiL9aU2rlDE5j5/UioEAThUJx4fhcYq7ETKqLoMfvr06fj3f/93LFu2DMOHD8eHH36IiRMn4pNPPsG1114Lt1t+zaudO3fi5z//OU6fPo0ZM2Zg27ZtKCgoAADcddddaGhowPvvvw8AuPbaa7F///6APu68807s2rULAPDAAw9g9+7daG5uhtPpRE5ODjZv3oy8vDzZMelxGbySyu5KK52L9ZGz5W3RF7dQVdaDxesZA4CqKvVyqkaHahOswrxUkdWCbJesqtRK56yk4rUWFbOljmlcjAOCELzgq/94Un2piSsUueczK4grwyrsRDpcBl9fXy96WXlCQgK6u5UVu7v//vtx//33iz7mWdR4eBZCwWzbtg3btm1TFIPelFR2F/7v30oN7qOprVvyxS1YlfVQ8fbLiE2qCrScqtFSc/e0qfhrq6oK83KqUquZs9yK11pUzN5/vEVyjhcH5C2UPeOVHWkKmi89qoVHetxoxyrsRMqpKoWRlZWFmpqagO1vvvkmpk+fHm5MUU9JZfdwK503tHWj5lR70DZSVdY9tKi27l8FWk7V6FBtqk8Gj1tJPP7CmXO4fcupmB3qmCpxqL4tdCPoUy08UuNGO1ZhJ1JO1TtAP/nJT1BcXIzz589DEAQcOXIEpaWl2Lp1K1588UWtY4w6Siq7q/iEMqCPUKtcqSrrHlpUW/evAi2nanSouedkqC+8G6oqdThzDrdvORWzZ40boSSkoPKyUvFG9Vch2+lRLTxS40Y7VmEnUk7VO0D//M//jEceeQT/+q//ip6eHtx+++147rnn8Itf/AI/+MEPtI4x6iip7K6m0rl/H4umjEKKxJcgg1VZDxWvZww1VerlVI0OOe4V4mN7KsxLkVOVWs2c5Va81qJidrBjGhfjUFTFvvCa8UHzpUe18EiPG+1YhZ1IOVULIAC499570djYiJaWFjQ3N+PkyZNYsWKFlrFFNSWV3cXa+r9g+f/u38ee4gWi+4Sqsh4sBs8YaqvUy6kaHapNsArz/lXbgUtXQ8mtSq10zkoqXmtRMVv6mOYrrmIv1pfauEIRm/v8SanImygdH4XGKuxEyqi6Cuy6667D7t27MWLECJ/tnZ2dWLZsGfbt26dVfIbQsxiqksru/tWVQ/0uprK2VVWVdakY1M5Fbp9y2wSrMH+4rg0CoPo+QHLmrPaeK1pUzJY6pmqq2Hv6GuscirThCbpXC2cF8fAxh2RnEa8GHxMTg+bmZp8SFMCluzhffvnluHBB+ooSK2A1eCIiIuuJ2GXwH330kfffn332mU8Zi/7+fvzxj3/E5ZdfrjBcIiIiIn0pWgDNmjULDocDDocD1113XcDjQ4cORUlJiWbBEREREUWCogVQfX09BEHAxIkTceTIEbhc33zfYMiQIRg1ahRiY2M1D5KIiIhIS4oWQJ4q8AMDAwAufQzW1NSEvr4+n3a33HKLRuERERERaU91KYzvf//7+Oijj+BwOLw3rHP83z0oPMVRiYiIiMxI1QJozZo1mDBhAsrLyzFx4kQcPnwYZ86cwY9//GM88cQTWscYVepau9B4pgctnefR3Hken33ZgcYzPZh5+Qh876oxPpeuetrGOhzoFwTvPmOSh8KVnPDN785EuIYnil726unD85j/7/uPt6DmVDviHA5cFATvpdTfjA30CxCNK9RltlLtyo404VB9G64YNRzTxiYHjdszvicHYvGI5rfjPJrPnQ96ub8njvxJafjHORmSfXnGFsvh/374Faq+cOPKscnI/78b0Q0+VqFuNyA3l3rznBdqb5eglWDnoZz9jM6rWeIgUsIu562qy+DT0tKwb98+XHXVVXA6nThy5AimTJmCffv24cc//jGqq6sjEatuInEZfHtPH9aU1sgqBDl/UioEAThUJ69G02AF2S6UFOVAgBAwXkpSvE/RS8/9evxJbReLyzOec9BN9MTmWpDtwr9cm4XlL/1JtO9gcYeaqzMpPmh+PTd8zEi9VC7g41Pt+P7Ogz5xxMU4sKc4H9Mvd4bsK1jhUDH+4wPSOfLPpd4a27qxbEeVzxzF4o+0YMcgWJ7MklezxEGkRDSctxG/D1BKSgqOHj2KiRMnYtKkSXjxxRfx7W9/G1988QVmzpyJnp7wi2caKRILoDteOoKqE270h1nbK5RYh8N7N1g9x3tlxTXebWJz9bwzEqofQH7cg8cOld+UpHhUP7wYADD5/9srufA78e/fi8ixGjw+IJ0j/1zqLWfL26ILPP/4Iy3YMQiWJ7Pk1SxxECkRDeetktdvVaUwZsyY4b0n0Ny5c/Hzn/8cVVVV2LJlCyZOnKimy6hW19qFitrWiC9GAKBfEFBR26r7ePXuS9WmpeYaKhY1cXv3+WtLyP3O9lxAZW0ryo40iS5+AODigIAd+2ojkjvP+EDwHA3Opd72H2+RfHdrcPyRFur5IpUns+TVLHEQKWHH81bVAmjTpk3eK8Eee+wxNDY2YuHChdi7dy+efvppTQOMBo1nrP2OmBwNbZeeHEbMtfpku6x2x5rO4lB98I8VD5xwaxCR9PhA6Bx5cqm3mlPtQR/3xB9pcs8h/zyZJa9miYNICTuet6q+BH3DDTd4/z1x4kR89tlnOHPmDFJSUrxXgtE3Mkfq990Jo0xIvfRFOSPmmpMxQla72eNTMHp4It6o/kqyzYLJaThUd0ajyALHB0LnyJNLvc0aNyLo4574I03uOeSfJ7Pk1SxxEClhx/NWdTV4fyNHjuTiR8JE12Uo+L8rhCIt1uFAQbZL9/E8VwpIzTVULGri9u5zxaiQ+6UkxWNhtguF14xHXIx4u7gYB4qvy45I7jzjA8FzNDiXels0ZZRkRfjB8UdaqOeLVJ7MklezxEGkhB3PW80WQBRcSVGO90u+ocyflIq8iamqxsmfnIaSohzR8fxf3IItBOTG5RlvMLGx8yenoezeeZJ9B4tbyuCxg+3nuYrJY09xfkAcnqvA5PSllP/4UmOI5VJve4oXBMxRLP5IC3YMguXJLHk1SxxEStjtvFV1FVi0i2Q1+Hp3NxrauuE+14uvOr7G5191oqGtW/Q+QJ62nsvSPfuMdQ5F2vCEgN/F7tng6cPzmP/vlbWtONZ0FvExMbgwMOC974v/2GJxhbpHhFS73//5JKq+cAe9D5D/+P7/lRrbP7/B7mPjiUPqPkBSORg8rzc/Po3K2lZcOdaJ/Oy0gGMV6j46cnOpN895YfR9gIKdh3L2MzqvZomDSAkrn7cRvww+2kVyAURERESREfHL4ImIiIisjAsgIiIish0ugIiIiMh2uAAiIiIi21F1I0QKn6fatqeS+4G/tuKz051YmO3ClWOTfSpxh6rMG6riu1ibUIJVSg+3Gry3YnuQiumh9pUa27+KebBcfFNh3rfauxpyjkGo/Q/Xn4EDwNyJqar6CEckxrJLRWmjaZFnHiuyI14FJiKSV4GJVdsOxr8ye6gK6P7VyuVWcPcIVil9bMpQWZWCpSoKP7bsSmx649OgFduHD42Tve/gscXy6p87sVz4U1r5WM4xCFW9/F/+61hATM6h8ej4Wl4f4YhE9edoqChtBVrkmceKog0vgw9TJBdAUtW25VJSAV1OH/6CVUqfPylNVqVgqYrCyUPj0Pn1xaAV22dePkL2voPHDjevweYTjJxjEKp6udiCMNy45IpE9edoqChtBVrkmceKog0vgzepYNW25fqmArr6iu9S1X1DVUqXUyk4WEXhsz0XQlZsV7KvZ+yyPzVpsvgRm08woaqWh+rTs7/WcckVierPdqwobQQt8sxjRXbHBZCOQlXbVqL6ZPiVuf2r+4aqlC6nLyOqwQf7SEstOZWPlc5VafVyOX2EIxLVn+1YUdoIWuSZx4rsjgsgHYWqtq1ETkb4lbn9q/vmZamrPza4LyOqwautmxaMnMrHSueqtHq5nD7CEYnqz3asKG0ELfLMY0V2xwWQjoJV25brmwro6iu+S1X3DVUpXU6l4GAVhVOS4kNWbFeyr2fswm+NDzuvUvMJJlTV8lB9evbXOi65IlH92Y4VpY2gRZ55rMjuuADSmVi17WD8FyShKqD79y23gvs38UlXSpdbKViq3Z7iBSErtivZd/DYYnn1n4dYLvwprXws5xiEql4uFpNzqPw+whGJ6s92qyhtFC3yzGNFdsarwEToUQzVU23bU8m9qtaNT7/qwMJsF2aOc/pU4g5VmTdUxXexNqEEq5QebjV4ORXbQ+0rNbZ/FfNguZBbYV4OOccg1P4f1LX53AdIz4rMkRjLyhWlrUSLPPNYUbTgZfBhYjV4IiIi6+Fl8ERERERBcAFEREREtsMFEBEREdmO4QugnTt3IisrC4mJicjNzUVlZaVk2927d+P666+Hy+VCcnIy8vLy8NZbbwW0e+211zB9+nQkJCRg+vTpeP311yM5BSIiIrIYQxdAZWVlWLduHTZu3Ijq6mosXLgQS5YsQVNTk2j7iooKXH/99di7dy+OHj2Kb3/727j55ptRXV3tbXPo0CEUFhZi+fLl+PDDD7F8+XLcdtttOHz4sF7TIiIiIpMz9CqwuXPnYvbs2Xj22We926ZNm4Zly5Zh69atsvq48sorUVhYiIcffhgAUFhYiM7OTrz55pveNjfeeCNSUlJQWloq2kdvby96e3u9v3d2diIjI0OXq8DqWrvQeKYn6OWnctpI2X+8BTWn2kUvNVfLE0+sA+gX4P2vJz7/xyNxaa1/TqR+HxyDIAiieZTKb7C8f9O/A/2CMCgXl35X0pdUn5G8JDmcc8rIvs0m2ueqx/yiPYckLlLHXclVYHGajapQX18fjh49igcffNBn++LFi3Hw4EFZfQwMDODcuXMYOXKkd9uhQ4fwwAMP+LS74YYbsH37dsl+tm7dikcffVR+8Bpo7+nDmtIan2KYBdkulBTlwPl/N9KT00ZKY1s3lu2o8ikS6rnZYEaqunIVYvH4S0mKFy1MKjduNTH4jykVg388jy27Epve+DQgv48tm4FNb3wimncBQsgcyBkj1HGWahuucM4pI/s2m2ifqx7zi/YckjgzHXfDPgJzu93o7+9Henq6z/b09HQ0NzfL6uPJJ59Ed3c3brvtNu+25uZmxX1u2LABHR0d3p+TJ08qmIk6a0prUHXC7bOt6oQbq0urFbWR4r/4AS5VW79lxwFNY/YntfCQG7eaGMTmGUrVCTeW7qgSze/SHQck8y4nB3LGCHWcpdqGK5xzysi+zSba56rH/KI9hyTOTMfd8C9BO/zq0AiCELBNTGlpKTZv3oyysjKMGjUqrD4TEhKQnJzs8xNJda1dqKhtRb/fp4/9goCK2lbUu7tltZGy/3iL5CLgbM8FVMp490JuzHLJiTvSMfjHc7bngmh+pbZX1LYqGj9UX8GOs1jbcIVzThnZt9lE+1z1mF+055DEme24G7YASktLQ2xsbMA7My0tLQHv4PgrKyvDihUr8Lvf/Q7f/e53fR4bPXq0qj711HimJ+jjDW3dstpIqTnVHnTfY01ngz4uJlQ8cgWLW68YzELOcR7cNlzhnFNG9m020T5XPeYX7TkkcWY77oYtgIYMGYLc3FyUl5f7bC8vL8f8+fMl9ystLcVdd92F3/72t/i7v/u7gMfz8vIC+nz77beD9qm3zJHBv4MzIXWYrDZSZo0bEXTf2eNTgj4uJlQ8cgWLW68YzELOcR7cNlzhnFNG9m020T5XPeYX7TkkcWY77oZ+BLZ+/Xq8+OKL+OUvf4nPP/8cDzzwAJqamrBy5UoAl76bc8cdd3jbl5aW4o477sCTTz6JefPmobm5Gc3Nzejo6PC2Wbt2Ld5++208/vjj+Mtf/oLHH38c77zzDtatW6f39CRNdF2GgmwXYv0+lot1OFCQ7UJW2jBZbaQsmjJKsuJ8SlK8qqvBpOKRS07ckY7BP56UpHjR/EptL8h2KRo/VF/BjrNY23CFc04Z2bfZRPtc9ZhftOeQxJntuBu6ACosLMT27duxZcsWzJo1CxUVFdi7dy8yMzMBAKdPn/a5J9Dzzz+Pixcvori4GGPGjPH+rF271ttm/vz5ePXVV/Hyyy/jqquuwq5du1BWVoa5c+fqPr9gSopykD85zWdb/uQ0lBTlKGojZU/xgoBFkOcqMC1j9ie18JIbt5oYxOYZSv7kNOwpXiCaX6ntJUU5snIgt69gc5JqG65wzikj+zabaJ+rHvOL9hySODMdd1aDF6FnNfh6dzca2rqD3gtBThsplbWtONZ0VtP7AHniiYtx4OKA4P2vJz7/xyNxfw//nEj9PjgGAKJ5lMpvsLxL5UBqzkqOcyTzpiQeM/ZtNtE+Vz3mF+05JHGROu5KXr+5ABKh5wKIiIiItKHk9dvwy+CJiIiI9MYFEBEREdkOF0BERERkO1wAERERke1wAURERES2Y1g1eLpk//EW1JxqxxhnIvoHBAAOzJuYiqy0Yahr7ULjmR5DLw/VMgZPX7EOB/oF8cu8pcaT2ldOfP5t5M7pmzGBfgGK4g3VZzj5DJUjLeJUG0M0iqa5hnu+RjoX0ZRrMj8ugAzS2NYtWrHdwzk0Dh1fX/T+XpDtQklRDpwybvKnhfaePqwprUHFoMKpamMQ68u/TwGC6HiPLbsSm974VHTflKR4n/z5xyc2bqh9wo1XKj9a5FOqD7EcqY1TbQx6npt6iaa5Kp2L2ueOXvERaYH3ARKhx32Acra8Lbn4ERPrcCB/chpeWXFNROLxd8dLR1B1wu1TtVdtDGJ9+fcJQHS85KFx6Pz6oqwK7P7xBRs32JzCiVcqP1rkU6oPsRypjVNtDHqem3qJprkqnYva545e8RFJ4X2ATG7/8RZFix8A6BcEVNS2ot4d+Wq5da1dqKhtDfjjpyYGqb78+5Qa72zPBVmLH//4Qo0rto8W8YrlR4t8ButDLEdq4gwnBr3OTb1E01yVzkXtc0ev+Ii0wgWQAWpOtavet6Et8n8MGs/0aBZDqL4ioaGtW/G4njlpEa9/frTIZyTyqPRc0vK8MLtomqvSuah97qgVTbkma+F3gAwwa9wI1ft6alpFUubIJM1iCNVXJExIHQaln+x65qRFvP750SKfkcij0nNJy/PC7KJprkrnovRcCzcX0ZRrsha+A2SARVNGyapYPlisw4GCbJcuV0ZMdF2GgmwXYh2OsGOQ6su/T6nxUpLiJfeV6isrbVjIccX20SJesfxokc9gfYjlSE2c4cSg17mpl2iaq9K5qH3u6BUfkVa4ADLInuIFQRdBzqG+b87lT05DSVFOpMPyKinK8X6JNtwYxPry71NqvD3FCyT39c+ff3xifYbaJ9x4pfKjRT6V5EhtnGpj0PPc1Es0zVWL81XOc0ev+Ii0wKvAROhZDb6ythXHms5irHMo+gcECID3PkD17m40tHUbek8MLWPw9BUX48DFAfH7AEmNJ7WvnPj828idUzjxhuoznHyGypEWcaqNIRpF01zDPV8jnYtoyjUZQ8nrNxdAIvRcABEREZE2eBk8ERERURBcABEREZHtcAFEREREtsMFEBEREdkOb4RokGferUXVF25cOSYZ+Ve4vDfvC1YJWU0VcD3pEYfSSu6RiCWcvpXsa4a5mpUd5xwteOzILLgA0tnBE624/cUj3t8P1Z3Bi1UNAe0GV0KWrgI+A5ve+MTwCsp6VHKWO0YkYwmnbyX7mmGuZmXHOUcLHjsyG34EprPBi59gqk64sbq0GgCwprQGVSfcAY8v3XFAdLtnP71IxadlHHLHiGQs4fStZF8zzNWs7DjnaMFjR2bDBZCOnnm3VnZbbzXvv7aoqgKuVwVlPSo5yx0jkrGE07eSfc0wV7Oy45yjBY8dmREXQDqq+sIdupGf6pPtqsbSq4KyHpWc5Y4RyVjC6VvJvmaYq1nZcc7RgseOzIgLIB3lTxKvLxVMTsYIVWPpVUFZj0rOcseIZCzh9K1kXzPM1azsOOdowWNHZsQFkI5WfSdbdltvNe8rRqmqAq7X1RV6VHKWO0YkYwmnbyX7mmGuZmXHOUcLHjsyIy6AdFZ27zxZ7QZXQlZTBVxPelRyljtGJGMJp28l+5phrmZlxzlHCx47MhsWQxWhRzHUne+dQGVtK64c60R+dpr3LeBglZDVVAHXkx5xKK3kHolYwulbyb5mmKtZ2XHO0YLHjiKJ1eDDxGrwRERE1sNq8ERERERBcAFEREREtsMFEBEREdkOF0BERERkO1wAERERke2wGrxJ1LV2ofFMDyakDoMgCN5/Z6UNw/7jLag51Y7Z41OwMNvl09YMl5HKjSdUO7HHpfZRul1JnHrQOxbPeLEOoF+AKXJAxvM/D830HCHt8LiK4wLIYO09fVhTWoOK2lbRx+NiHLg4IEj+XpDtQklRDpxJ8RGP1Z9Y7GLxhGon9vj8SakQBOBQXZvPPo8tuxKb3vg0oC+p7SVFORAgyIpTD3JzFsnx9BiXzE3svEhJisfZngve33l+WJ/ef2+sxvCPwHbu3ImsrCwkJiYiNzcXlZWVkm1Pnz6N22+/HVOmTEFMTAzWrVsX0GbXrl1wOBwBP+fPn4/gLNRbU1qDqhPSRVIHL3bEfq864cbq0uqIxBaKWOxi8YRqJ/b4wS/afBY/nn2W7qgS7Utq++rSatlx6kHvWIKdX0aeO2QssfNi8OIH4PkRDcz0t8+MDF0AlZWVYd26ddi4cSOqq6uxcOFCLFmyBE1NTaLte3t74XK5sHHjRlx99dWS/SYnJ+P06dM+P4mJiZGahmp1rV2oqG1Ffxj3ouwXBFTUtqLerW81ZanY/eMJ1a7iry2yc9AvCDjbc0G0L6ntFbWtsuLUg9ycRXq8SI9L5ib37w7PD2vT+++NFRm6AHrqqaewYsUK3HPPPZg2bRq2b9+OjIwMPPvss6LtJ0yYgF/84he444474HQ6Jft1OBwYPXq0z08wvb296Ozs9PnRQ+OZHs36amjT92QOFbsnnlDtqk+2axWSKnrmTW7O9BovUuOSuSn9u8Pzw5r0/ntjRYYtgPr6+nD06FEsXrzYZ/vixYtx8ODBsPru6upCZmYmxo0bh5tuugnV1cHf7tu6dSucTqf3JyMjI6zx5cocmaRZX55aYnoJFbsnnlDtcjJGaBWSKnrmTW7O9BovUuOSuSn9u8Pzw5r0/ntjRYYtgNxuN/r7+5Genu6zPT09Hc3Nzar7nTp1Knbt2oU9e/agtLQUiYmJyM/PR21treQ+GzZsQEdHh/fn5MmTqsdXYqLrMhRkuxDrcKjuI9bhQEG2S/dv9kvF7h9PqHYFV4ySnYNYhwMpSfGifUltL8h2yYpTD3JzFunxIj0umZvcvzs8P6xN7783VmT4l6AdfgdHEISAbUrMmzcPP/zhD3H11Vdj4cKF+N3vfocrrrgCJSUlkvskJCQgOTnZ50cvJUU5yJ+cJvl4XIwj6O/5k9NQUpQTkdhCEYtdLJ5Q7cQenz8pFXkTUwP22VO8QLQvqe0lRTmy49SD3rEEO7+MPHfIWGLnRYrfVUE8P6zPTH/7zMiwavB9fX1ISkrC73//e3z/+9/3bl+7di1qamqwf//+oPtfe+21mDVrFrZv3x5yrHvvvRenTp3Cm2++KSs2I6rB17u70dDW7X1b0vPvrLRhqKxtxbGms977AA1ua4ZVvNx4QrUTe1xqH6XblcSpB71j8YznuY2CGXJAxvM/D830HCHt2Om4Knn9NmwBBABz585Fbm4udu7c6d02ffp0LF26FFu3bg26r9wFkCAIuOaaazBz5kz88pe/lBWXEQsgIiIiCo+S129Db4S4fv16LF++HHPmzEFeXh5eeOEFNDU1YeXKlQAufTfnyy+/xCuvvOLdp6amBsClLzq3traipqYGQ4YMwfTp0wEAjz76KObNm4fs7Gx0dnbi6aefRk1NDXbs2KH7/IiIiMicDF0AFRYWoq2tDVu2bMHp06cxY8YM7N27F5mZmQAu3fjQ/55AOTnffHZ59OhR/Pa3v0VmZiYaGhoAAO3t7bjvvvvQ3NwMp9OJnJwcVFRU4JprrtFtXkRERGRuhn4EZlb8CIyIiMh6lLx+G34VGBEREZHeWAzVIJ4K72OSE+FKTkSsw4F+QZBVCV2KVSqhSxkcY1NbN2pOtXuvfNNrXK0rYivpywrHyKzU5C6cfPNYWYNRx4nnhzVwAaSzxrZuLNtRFVB4cDCpSuhSFXyDVfw1UyV0KcEqlgOX7k+yp3gBMlK1u3O21LhaVcRWUoWZFZvVU5O7cPLNY2UNRh0nnh/Wwu8AiYjkd4BytrwddPEjJdbhQP7kNLyyIvDL3He8dARVJ9w+Re887QFIPibWlxHE4veXkhSP6ocXSz4eqXHV5irYMfHvS0lb8qUmd+Hkm8fKGow6Tjw/jMfvAJnU/uMtqhY/gHQF35DV1k1eDVhuZeqzPRdQKfEOUSTHVZMrJVWYWbFZPTW5CyffPFbWYNRx4vlhPVwA6ajmVHvYffhX8A2norwZqgErif9Y01lDxgWU5UpJFWZWbFZPTe7CyTePlTUYdZx4flgPF0A6mjVuRNh9+FfwDaeivBmqASuJf/b4FEPGBZTlSkkVZlZsVk9N7sLJN4+VNRh1nHh+WA8XQDpaNGVUQMFBuaQq+Iastm7yasByK1OnJMVrejVYJCtiK6nCzIrN6qnJXTj55rGyBqOOE88P6+ECSGd7iheEXARJVUKXquAbrOKvFaoBB6tYDnxzFZge42pVEVtJ3q1wjMxKTe7CyTePlTUYdZx4flgLrwITocedoD0V3sc6hyJteIJolW6lFXytUgldyuAYT53twbGms7rcByiSFbGV9GWFY2RWanIXTr55rKzBqOPE88M4lqkGb1YshUFERGQ9vAyeiIiIKAgugIiIiMh2uAAiIiIi2+ECiIiIiGyHCyAiIiKyHVaDN5G61i40nunxXjrp/7tZmCkuM8ViRZ78xToc6BeEiObRjsfKjnM2q2g9FtE6Lz1wAWQC7T19WFNag4pBxT5TkuJ9CqcWZLtQUpQDp8o7SWtBLE6j4jJTLFYklj8PrfNox2NlxzmbVbQei2idl574EZgJrCmtQdUJt882/6rxVSfcWF1arWdYAcTiNCouM8ViRWL589A6j3Y8Vnacs1lF67GI1nnpiQsgg9W1dqGithX9Ie5H2S8IqKhtRb3bmIrCUnEaEZeZYrGiUOeclnm047Gy45zNKlqPRbTOS29cABms8UyPovYNbcac2KHi1DMuM8ViRXLPOS3yaMdjZcc5m1W0HotonZfeuAAyWObIJEXtJ6Qa8yW3UHHqGZeZYrEiueecFnm047Gy45zNKlqPRbTOS29cABlsousyFGS7EOtwBG0X63CgINtl2Lf8peI0Ii4zxWJFoc45LfNox2NlxzmbVbQei2idl964ADKBkqIc5E9O89mW4vct/vzJaSgpytEzrABicRoVl5lisSKx/HlonUc7His7ztmsovVYROu89MRq8CKMqgZf7+5GQ1u3934O/r+bhZniMlMsVuTJX1yMAxcHInsfIDseKzvO2ayi9VhE67zUUvL6zQWQCKMWQERERKSektdvfgRGREREtsMFEBEREdkOF0BERERkO1wAERERke1wAURERES2w2rwFrD/eAtqTrVj9vgULMx2oa61C41nekx32aPcuCIZv1lzE82iJefRMg8ikocLIBNrbOvGsh1VPpXhPfdr8SjIdqGkKAdOvxsn6qm9pw9rSmtQUdsaNC657SIZA2knWnIeLfMgImX4EZiJ+S9+APgsfgCg6oQbq0ur9QwrwJrSGlSdcPtsE4tLbrtIxkDaiZacR8s8iEgZLoBMav/xloDFj5h+QUBFbSvq3cZU/61r7UJFbSv6/e6n6R+X3HaRjIG0Ey05j5Z5EJFyXACZVM2pdkXtG9qM+UPdeKYn6OOeuOS2i2QMpJ1oyXm0zIOIlOMCyKRmjRuhqP2EVGO+tJk5Mino45645LaLZAyknWjJebTMg4iUM3wBtHPnTmRlZSExMRG5ubmorKyUbHv69GncfvvtmDJlCmJiYrBu3TrRdq+99hqmT5+OhIQETJ8+Ha+//nqEoo+cRVNGBVSEFxPrcKAg22XYVSsTXZehINuFWIcjaFxy20UyBtJOtOQ8WuZBRMoZugAqKyvDunXrsHHjRlRXV2PhwoVYsmQJmpqaRNv39vbC5XJh48aNuPrqq0XbHDp0CIWFhVi+fDk+/PBDLF++HLfddhsOHz4cyalExJ7iBQGLoLgY3z/U+ZPTUFKUo2dYAUqKcpA/Oc1nm1hccttFMgbSTrTkPFrmQUTKGFoNfu7cuZg9ezaeffZZ77Zp06Zh2bJl2Lp1a9B9r732WsyaNQvbt2/32V5YWIjOzk68+eab3m033ngjUlJSUFpaKisus1WDr6xtxbGms977ANW7u9HQ1m26+5XIjSuS8Zs1N9EsWnIeLfMgsjMlr9+G3Qeor68PR48exYMPPuizffHixTh48KDqfg8dOoQHHnjAZ9sNN9wQsFAarLe3F729vd7fOzs7VY8fCQuzXViY7fL+npVmzj/QcuOKZPxmzU00i5acR8s8iEgewz4Cc7vd6O/vR3p6us/29PR0NDc3q+63ublZcZ9bt26F0+n0/mRkZKgen4iIiMzP8C9BO/y+fCgIQsC2SPe5YcMGdHR0eH9OnjwZ1vhERERkboZ9BJaWlobY2NiAd2ZaWloC3sFRYvTo0Yr7TEhIQEJCguoxiYiIyFoMewdoyJAhyM3NRXl5uc/28vJyzJ8/X3W/eXl5AX2+/fbbYfVJRERE0cXQYqjr16/H8uXLMWfOHOTl5eGFF15AU1MTVq5cCeDSR1NffvklXnnlFe8+NTU1AICuri60traipqYGQ4YMwfTp0wEAa9euRUFBAR5//HEsXboUf/jDH/DOO+/gwIEDus+PiIiIzMnQBVBhYSHa2tqwZcsWnD59GjNmzMDevXuRmZkJ4NKND/3vCZST8829OY4ePYrf/va3yMzMRENDAwBg/vz5ePXVV7Fp0yY89NBDmDRpEsrKyjB37lzd5kVERETmZuh9gMzKbPcBIiIiotCUvH4bfhUYERERkd64ACIiIiLb4QKIiIiIbIcLICIiIrIdLoCIiIjIdrgAIiIiItvhAoiIiIhshwsgIiIish0ugIiIiMh2uAAiIiIi2+ECiIiIiGyHCyAiIiKyHUOrwROw/3gLak61Y/b4FCzMdkV8vLrWLjSe6cGE1GHIShsW8fGIeM6RUjxnSA9cABmksa0by3ZU4WzPBe+2lKR47ClegIzUJM3Ha+/pw5rSGlTUtnq3FWS7UFKUA2dSvObjEfGcI6V4zpCe+BGYQfwXPwBwtucCbtlxICLjrSmtQdUJt8+2qhNurC6tjsh4RDznSCmeM6QnLoAMsP94S8Dix+NszwVUDvq/Hy3UtXahorYV/YLgs71fEFBR24p6d7em4xHxnCOleM6Q3rgAMkDNqfagjx9rOqvpeI1neoI+3tDGPyykLZ5zpBTPGdIbF0AGmDVuRNDHZ49P0XS8zJHBv1M0IZVfMiRt8ZwjpXjOkN64ADLAoimjkCLxhb6UpHjNrwab6LoMBdkuxDocPttjHQ4UZLt4lQVpjuccKcVzhvTGBZBB9hQvCFgEea4Ci4SSohzkT07z2ZY/OQ0lRTkRGY+I5xwpxXOG9OQQBL9vnBE6OzvhdDrR0dGB5OTkiI5VWduKY01ndbsPUL27Gw1t3by/BumG5xwpxXOG1FLy+s0FkAg9F0BERESkDSWv3/wIjIiIiGyHCyAiIiKyHS6AiIiIyHa4ACIiIiLb4QKIiIiIbIcLICIiIrIdLoCIiIjIdrgAIiIiItvhAoiIiIhshwsgIiIisp04owMwI091kM7OToMjISIiIrk8r9tyqnxxASTi3LlzAICMjAyDIyEiIiKlzp07B6fTGbQNi6GKGBgYwFdffYXhw4fD4XBo2ndnZycyMjJw8uRJFlrVGXNvLObfOMy9sZh//QiCgHPnzmHs2LGIiQn+LR++AyQiJiYG48aNi+gYycnJfCIYhLk3FvNvHObeWMy/PkK98+PBL0ETERGR7XABRERERLbDBZDOEhIS8MgjjyAhIcHoUGyHuTcW828c5t5YzL858UvQREREZDt8B4iIiIhshwsgIiIish0ugIiIiMh2uAAiIiIi2+ECSEc7d+5EVlYWEhMTkZubi8rKSqNDikqbN2+Gw+Hw+Rk9erT3cUEQsHnzZowdOxZDhw7Ftddei08//dTAiK2roqICN998M8aOHQuHw4E33njD53E5ue7t7cXq1auRlpaGYcOG4ZZbbsGpU6d0nIV1hcr/XXfdFfBcmDdvnk8b5l+drVu34lvf+haGDx+OUaNGYdmyZTh+/LhPG57/5sYFkE7Kysqwbt06bNy4EdXV1Vi4cCGWLFmCpqYmo0OLSldeeSVOnz7t/fn444+9j/385z/HU089hWeeeQZ/+tOfMHr0aFx//fXeGnAkX3d3N66++mo888wzoo/LyfW6devw+uuv49VXX8WBAwfQ1dWFm266Cf39/XpNw7JC5R8AbrzxRp/nwt69e30eZ/7V2b9/P4qLi/HBBx+gvLwcFy9exOLFi9Hd3e1tw/Pf5ATSxTXXXCOsXLnSZ9vUqVOFBx980KCIotcjjzwiXH311aKPDQwMCKNHjxZ+9rOfebedP39ecDqdwnPPPadThNEJgPD66697f5eT6/b2diE+Pl549dVXvW2+/PJLISYmRvjjH/+oW+zRwD//giAId955p7B06VLJfZh/7bS0tAgAhP379wuCwPPfCvgOkA76+vpw9OhRLF682Gf74sWLcfDgQYOiim61tbUYO3YssrKy8IMf/AB1dXUAgPr6ejQ3N/sci4SEBCxatIjHQmNycn306FFcuHDBp83YsWMxY8YMHg+NvP/++xg1ahSuuOIK3HvvvWhpafE+xvxrp6OjAwAwcuRIADz/rYALIB243W709/cjPT3dZ3t6ejqam5sNiip6zZ07F6+88greeust/Od//ieam5sxf/58tLW1efPNYxF5cnLd3NyMIUOGICUlRbINqbdkyRL85je/wb59+/Dkk0/iT3/6E6677jr09vYCYP61IggC1q9fjwULFmDGjBkAeP5bAavB68jhcPj8LghCwDYK35IlS7z/njlzJvLy8jBp0iT86le/8n4BlMdCP2pyzeOhjcLCQu+/Z8yYgTlz5iAzMxP/+7//i1tvvVVyP+ZfmVWrVuGjjz7CgQMHAh7j+W9efAdIB2lpaYiNjQ1Y0be0tAT83wFpb9iwYZg5cyZqa2u9V4PxWESenFyPHj0afX19OHv2rGQb0s6YMWOQmZmJ2tpaAMy/FlavXo09e/bgvffew7hx47zbef6bHxdAOhgyZAhyc3NRXl7us728vBzz5883KCr76O3txeeff44xY8YgKysLo0eP9jkWfX192L9/P4+FxuTkOjc3F/Hx8T5tTp8+jU8++YTHIwLa2tpw8uRJjBkzBgDzHw5BELBq1Srs3r0b+/btQ1ZWls/jPP8twLCvX9vMq6++KsTHxwsvvfSS8Nlnnwnr1q0Thg0bJjQ0NBgdWtT58Y9/LLz//vtCXV2d8MEHHwg33XSTMHz4cG+uf/aznwlOp1PYvXu38PHHHwtFRUXCmDFjhM7OToMjt55z584J1dXVQnV1tQBAeOqpp4Tq6mqhsbFREAR5uV65cqUwbtw44Z133hGOHTsmXHfddcLVV18tXLx40ahpWUaw/J87d0748Y9/LBw8eFCor68X3nvvPSEvL0+4/PLLmX8N/Mu//IvgdDqF999/Xzh9+rT3p6enx9uG57+5cQGkox07dgiZmZnCkCFDhNmzZ3svlyRtFRYWCmPGjBHi4+OFsWPHCrfeeqvw6aefeh8fGBgQHnnkEWH06NFCQkKCUFBQIHz88ccGRmxd7733ngAg4OfOO+8UBEFerr/++mth1apVwsiRI4WhQ4cKN910k9DU1GTAbKwnWP57enqExYsXCy6XS4iPjxfGjx8v3HnnnQG5Zf7VEcs7AOHll1/2tuH5b24OQRAEvd91IiIiIjISvwNEREREtsMFEBEREdkOF0BERERkO1wAERERke1wAURERES2wwUQERER2Q4XQERERGQ7XAARERGR7XABRESWN2HCBGzfvt3oMGSzWrxE0YgLICKyvD/96U+47777ZLXl4oOIAC6AiMhAfX19mvTjcrmQlJSkSV9yaRU7ERmDCyAi0s21116LVatWYf369UhLS8P111+Pzz77DN/73vdw2WWXIT09HcuXL4fb7fbuc+7cOfzTP/0Thg0bhjFjxmDbtm249tprsW7dOm8b/3d1Nm/ejPHjxyMhIQFjx47FmjVrvOM3NjbigQcegMPhgMPh8O5z8OBBFBQUYOjQocjIyMCaNWvQ3d3tM8Zjjz2Gu+66C06nE/fee6+s/VpaWnDzzTdj6NChyMrKwm9+8xut00pEKnABRES6+tWvfoW4uDhUVVXhZz/7GRYtWoRZs2bhz3/+M/74xz/ib3/7G2677TZv+/Xr16Oqqgp79uxBeXk5KisrcezYMcn+//u//xvbtm3D888/j9raWrzxxhuYOXMmAGD37t0YN24ctmzZgtOnT+P06dMAgI8//hg33HADbr31Vnz00UcoKyvDgQMHsGrVKp++/+M//gMzZszA0aNH8dBDD8na76677kJDQwP27duH//7v/8bOnTvR0tKiZUqJSA2jy9ETkX0sWrRImDVrlvf3hx56SFi8eLFPm5MnTwoAhOPHjwudnZ1CfHy88Pvf/977eHt7u5CUlCSsXbvWuy0zM1PYtm2bIAiC8OSTTwpXXHGF0NfXJxrD4LYey5cvF+677z6fbZWVlUJMTIzw9ddfe/dbtmyZov2OHz8uABA++OAD7+Off/65ACAgBiLSV5zB6y8ispk5c+Z4/3306FG89957uOyyywLaffHFF/j6669x4cIFXHPNNd7tTqcTU6ZMkez/H//xH7F9+3ZMnDgRN954I773ve/h5ptvRlyc9J+7o0eP4sSJEz4fTwmCgIGBAdTX12PatGkBscvZ769//Svi4uJ89ps6dSpGjBghGQsR6YMLICLS1bBhw7z/HhgYwM0334zHH388oN2YMWNQW1sLAD7f1QEuLTKkZGRk4Pjx4ygvL8c777yD+++/H//xH/+B/fv3Iz4+XnSfgYEB/OhHP/J+V2iw8ePHi8YuZ7/jx4+Lxk9ExuMCiIgMM3v2bLz22muYMGGC6Ds0kyZNQnx8PI4cOYKMjAwAQGdnJ2pra7Fo0SLJfocOHYpbbrkFt9xyC4qLizF16lR8/PHHmD17NoYMGYL+/v6AOD799FNMnjxZcfzB9ps2bRouXryIP//5z953sY4fP4729nZF4xCR9vglaCIyTHFxMc6cOYOioiIcOXIEdXV1ePvtt3H33Xejv78fw4cPx5133omf/OQneO+99/Dpp5/i7rvvRkxMjOS7Krt27cJLL72ETz75BHV1dfj1r3+NoUOHIjMzE8Clq7kqKirw5Zdfeq82+3//7//h0KFDKC4uRk1NDWpra7Fnzx6sXr06aPyh9psyZQpuvPFG3HvvvTh8+DCOHj2Ke+65B0OHDtUwi0SkBhdARGSYsWPHoqqqCv39/bjhhhswY8YMrF27Fk6nEzExl/48PfXUU8jLy8NNN92E7373u8jPz8e0adOQmJgo2ueIESPwn//5n8jPz8dVV12Fd999F//zP/+D1NRUAMCWLVvQ0NCASZMmweVyAQCuuuoq7N+/H7W1tVi4cCFycnLw0EMPYcyYMUHjl7Pfyy+/jIyMDCxatAi33nor7rvvPowaNUqL9BFRGBxCsA/TiYhMpru7G5dffjmefPJJrFixwuhwiMii+B0gIjK16upq/OUvf8E111yDjo4ObNmyBQCwdOlSgyMjIivjAoiITO+JJ57A8ePHMWTIEOTm5qKyshJpaWlGh0VEFsaPwIiIiMh2+CVoIiIish0ugIiIiMh2uAAiIiIi2+ECiIiIiGyHCyAiIiKyHS6AiIiIyHa4ACIiIiLb4QKIiIiIbOf/B4lLv8tkzd+7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "data2.plot(x = 'registered', y= 'atemp', kind = 'scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ddd1fdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "78d48d88",
   "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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</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>instant</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.692823</td>\n",
       "      <td>-0.018596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>-0.220022</td>\n",
       "      <td>0.164053</td>\n",
       "      <td>0.117704</td>\n",
       "      <td>-0.403658</td>\n",
       "      <td>0.206271</td>\n",
       "      <td>0.219238</td>\n",
       "      <td>0.041795</td>\n",
       "      <td>0.076839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.692823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093148</td>\n",
       "      <td>0.027074</td>\n",
       "      <td>0.126446</td>\n",
       "      <td>0.091299</td>\n",
       "      <td>-0.125794</td>\n",
       "      <td>0.142861</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.063073</td>\n",
       "      <td>0.071107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>-0.018596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056458</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050662</td>\n",
       "      <td>-0.054857</td>\n",
       "      <td>0.278821</td>\n",
       "      <td>0.225257</td>\n",
       "      <td>-0.211255</td>\n",
       "      <td>0.097096</td>\n",
       "      <td>0.203295</td>\n",
       "      <td>0.392769</td>\n",
       "      <td>0.397325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.000053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093148</td>\n",
       "      <td>0.050662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052128</td>\n",
       "      <td>-0.289220</td>\n",
       "      <td>-0.319828</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.023188</td>\n",
       "      <td>-0.120978</td>\n",
       "      <td>0.013744</td>\n",
       "      <td>-0.008499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.220022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027074</td>\n",
       "      <td>-0.054857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.052128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093247</td>\n",
       "      <td>-0.036555</td>\n",
       "      <td>0.531591</td>\n",
       "      <td>-0.228990</td>\n",
       "      <td>-0.156710</td>\n",
       "      <td>-0.088272</td>\n",
       "      <td>-0.108713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.164053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126446</td>\n",
       "      <td>0.278821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.289220</td>\n",
       "      <td>-0.093247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909959</td>\n",
       "      <td>-0.182066</td>\n",
       "      <td>0.242623</td>\n",
       "      <td>0.530170</td>\n",
       "      <td>0.183277</td>\n",
       "      <td>0.261536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.117704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091299</td>\n",
       "      <td>0.225257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.319828</td>\n",
       "      <td>-0.036555</td>\n",
       "      <td>0.909959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019152</td>\n",
       "      <td>-0.130303</td>\n",
       "      <td>0.485061</td>\n",
       "      <td>0.103077</td>\n",
       "      <td>0.179776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>-0.403658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.125794</td>\n",
       "      <td>-0.211255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.531591</td>\n",
       "      <td>-0.182066</td>\n",
       "      <td>-0.019152</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.462165</td>\n",
       "      <td>-0.337181</td>\n",
       "      <td>-0.255911</td>\n",
       "      <td>-0.294684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>0.206271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142861</td>\n",
       "      <td>0.097096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023188</td>\n",
       "      <td>-0.228990</td>\n",
       "      <td>0.242623</td>\n",
       "      <td>-0.130303</td>\n",
       "      <td>-0.462165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154892</td>\n",
       "      <td>0.173689</td>\n",
       "      <td>0.187071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.219238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074397</td>\n",
       "      <td>0.203295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.120978</td>\n",
       "      <td>-0.156710</td>\n",
       "      <td>0.530170</td>\n",
       "      <td>0.485061</td>\n",
       "      <td>-0.337181</td>\n",
       "      <td>0.154892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375657</td>\n",
       "      <td>0.520903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.041795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.063073</td>\n",
       "      <td>0.392769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.013744</td>\n",
       "      <td>-0.088272</td>\n",
       "      <td>0.183277</td>\n",
       "      <td>0.103077</td>\n",
       "      <td>-0.255911</td>\n",
       "      <td>0.173689</td>\n",
       "      <td>0.375657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.076839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.397325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008499</td>\n",
       "      <td>-0.108713</td>\n",
       "      <td>0.261536</td>\n",
       "      <td>0.179776</td>\n",
       "      <td>-0.294684</td>\n",
       "      <td>0.187071</td>\n",
       "      <td>0.520903</td>\n",
       "      <td>0.986777</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instant  season  yr      mnth        hr  holiday   weekday  \\\n",
       "instant     1.000000     NaN NaN  0.692823 -0.018596      NaN  0.000053   \n",
       "season           NaN     NaN NaN       NaN       NaN      NaN       NaN   \n",
       "yr               NaN     NaN NaN       NaN       NaN      NaN       NaN   \n",
       "mnth        0.692823     NaN NaN  1.000000 -0.056458      NaN  0.093148   \n",
       "hr         -0.018596     NaN NaN -0.056458  1.000000      NaN  0.050662   \n",
       "holiday          NaN     NaN NaN       NaN       NaN      NaN       NaN   \n",
       "weekday     0.000053     NaN NaN  0.093148  0.050662      NaN  1.000000   \n",
       "weathersit -0.220022     NaN NaN  0.027074 -0.054857      NaN  0.052128   \n",
       "temp        0.164053     NaN NaN  0.126446  0.278821      NaN -0.289220   \n",
       "atemp       0.117704     NaN NaN  0.091299  0.225257      NaN -0.319828   \n",
       "hum        -0.403658     NaN NaN -0.125794 -0.211255      NaN  0.049960   \n",
       "windspeed   0.206271     NaN NaN  0.142861  0.097096      NaN  0.023188   \n",
       "casual      0.219238     NaN NaN  0.074397  0.203295      NaN -0.120978   \n",
       "registered  0.041795     NaN NaN  0.063073  0.392769      NaN  0.013744   \n",
       "cnt         0.076839     NaN NaN  0.071107  0.397325      NaN -0.008499   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed    casual  \\\n",
       "instant      -0.220022  0.164053  0.117704 -0.403658   0.206271  0.219238   \n",
       "season             NaN       NaN       NaN       NaN        NaN       NaN   \n",
       "yr                 NaN       NaN       NaN       NaN        NaN       NaN   \n",
       "mnth          0.027074  0.126446  0.091299 -0.125794   0.142861  0.074397   \n",
       "hr           -0.054857  0.278821  0.225257 -0.211255   0.097096  0.203295   \n",
       "holiday            NaN       NaN       NaN       NaN        NaN       NaN   \n",
       "weekday       0.052128 -0.289220 -0.319828  0.049960   0.023188 -0.120978   \n",
       "weathersit    1.000000 -0.093247 -0.036555  0.531591  -0.228990 -0.156710   \n",
       "temp         -0.093247  1.000000  0.909959 -0.182066   0.242623  0.530170   \n",
       "atemp        -0.036555  0.909959  1.000000 -0.019152  -0.130303  0.485061   \n",
       "hum           0.531591 -0.182066 -0.019152  1.000000  -0.462165 -0.337181   \n",
       "windspeed    -0.228990  0.242623 -0.130303 -0.462165   1.000000  0.154892   \n",
       "casual       -0.156710  0.530170  0.485061 -0.337181   0.154892  1.000000   \n",
       "registered   -0.088272  0.183277  0.103077 -0.255911   0.173689  0.375657   \n",
       "cnt          -0.108713  0.261536  0.179776 -0.294684   0.187071  0.520903   \n",
       "\n",
       "            registered       cnt  \n",
       "instant       0.041795  0.076839  \n",
       "season             NaN       NaN  \n",
       "yr                 NaN       NaN  \n",
       "mnth          0.063073  0.071107  \n",
       "hr            0.392769  0.397325  \n",
       "holiday            NaN       NaN  \n",
       "weekday       0.013744 -0.008499  \n",
       "weathersit   -0.088272 -0.108713  \n",
       "temp          0.183277  0.261536  \n",
       "atemp         0.103077  0.179776  \n",
       "hum          -0.255911 -0.294684  \n",
       "windspeed     0.173689  0.187071  \n",
       "casual        0.375657  0.520903  \n",
       "registered    1.000000  0.986777  \n",
       "cnt           0.986777  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "50341be7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0.000000\n",
       "season        0.000000\n",
       "yr            0.000000\n",
       "mnth         -1.505798\n",
       "hr           -0.050086\n",
       "holiday       0.000000\n",
       "weekday       0.014906\n",
       "weathersit    1.037234\n",
       "temp          0.479548\n",
       "atemp         0.440540\n",
       "hum           0.161484\n",
       "windspeed     0.620359\n",
       "casual        3.413724\n",
       "registered    1.115912\n",
       "cnt           0.987140\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.skew(axis = 0, skipna = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6063d34c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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