notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Proyek Analisis Data: [Bike Sharing]**\n",
    "- **Nama:** [Rifki Muhammad]\n",
    "- **Email:** [rifki18995@gmail.com]\n",
    "- **ID Dicoding:** [rifmudth]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Menentukan Pertanyaan Bisnis**\n",
    "Apakah ada peran dari cuaca terhadap jumlah pengguna sepeda?\n",
    "- Bagaimana kondisi yang tampak ketika sepeda digunakan pada workingday, holiday, dan weekday?\n",
    "- Bagaimana tren penggunaan sepeda dalam setahun, apakah tahun 2011 atau 2012 yang lebih unggul?\n",
    "- Bagaimana variabel seperti temp dan atemp serta humidity dapat mempengaruhi jumlah total pengguna sepeda (baik Casual ataupun Registered)?\n",
    "- Apakah ada pengaruh musim terhadap penyewa sepeda berdasarkan pelanggan Casual dan Registered?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Menyiapkan semua library yang dibutuhkan**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sebelum memulai proyek, pastikan kita telah mengimport semua library yang kita butuhkan untuk mengerjakan proyeknya.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Wrangling**\n",
    "### Gathering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada tahap ini kita akan mengumpulkan data yang telah kita kumpulkan. Karena saya memilih 'Bike Sharing Dataset', maka saya mengimpor file 'bike_day.csv' dan 'bike_hour.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Memuat Tabel day_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df = pd.read_csv(r\"C:\\Users\\USER\\Desktop\\Submission\\Data\\bike_day.csv\")\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Memuat Tabel hr_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_df = pd.read_csv(r\"C:\\Users\\USER\\Desktop\\Submission\\Data\\bike_hour.csv\")\n",
    "hr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **ASSESING DATA**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Memeriksa Tabel 'day_df'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     731 non-null    int64  \n",
      " 1   dteday      731 non-null    object \n",
      " 2   season      731 non-null    int64  \n",
      " 3   yr          731 non-null    int64  \n",
      " 4   mnth        731 non-null    int64  \n",
      " 5   holiday     731 non-null    int64  \n",
      " 6   weekday     731 non-null    int64  \n",
      " 7   workingday  731 non-null    int64  \n",
      " 8   weathersit  731 non-null    int64  \n",
      " 9   temp        731 non-null    float64\n",
      " 10  atemp       731 non-null    float64\n",
      " 11  hum         731 non-null    float64\n",
      " 12  windspeed   731 non-null    float64\n",
      " 13  casual      731 non-null    int64  \n",
      " 14  registered  731 non-null    int64  \n",
      " 15  cnt         731 non-null    int64  \n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "day_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat diatas, terdapat 1 tipe data berdasarkan kolomnya yang tidak sesuai. Data yang perlu diperbaiki adalah: dteday -> datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Cek missing values day_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada hasil di atas dapat kita simpulkan bahwa tabel day_df tidak ada missing values, berarti aman."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Cek duplikat day_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi data:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi data: \", day_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada tabel day_df terdapat 0 data yang double, artinya semua data bersifat unik."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Cek keanehan pada tabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat pada tabel di atas, tidak ada keanehan pada nilai statistiknya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Menilai Tabel 'hr_df'**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Memeriksa Tabel hr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "hr_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat diatas, terdapat 1 tipe data berdasarkan kolomnya yang tidak sesuai. Data yang perlu diperbaiki adalah: dteday -> datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Cek missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "hr            0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita perhatikan tabel hr_df tidak ada missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Cek Duplikat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah duplikasi: \", hr_df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Mengecek apakah terdapat keanehan pada nilai statistiknya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dapat kita lihat diatas, tidak ada keanehan pada nilai statistiknya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **CLEANNING DATA**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Menghapus tabel yang tidak diperlukan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebelum kita masuk ke tahap selanjutnya, kita akan menghapus terlebih dahulu tabel **hr_df**. Alasannya karena tidak relevan dengan pertanyaan bisnis yang sudah kita tetapkan, dan juga tabel hr dan day berbeda hanya di kolom hrnya saja, selebihnya semua sama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "del hr_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Menghapus beberapa kolom yang tidak akan digunakan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Berdasarkan pertanyaan bisnis yang sudah ada, maka untuk kolom yang akan didrop, yakni:\n",
    "- instant: Karena indeks catatan ini tidak memberikan informasi tambahan yang berguna.\n",
    "- windspeed: Tidak ada pertanyaan bisnis yang terkait dengan efek kecepatan angin terhadap jumlah penyewa sepeda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1        0        6           0           2   \n",
       "1  2011-01-02       1   0     1        0        0           0           2   \n",
       "2  2011-01-03       1   0     1        0        1           1           1   \n",
       "3  2011-01-04       1   0     1        0        2           1           1   \n",
       "4  2011-01-05       1   0     1        0        3           1           1   \n",
       "\n",
       "       temp     atemp       hum  casual  registered   cnt  \n",
       "0  0.344167  0.363625  0.805833     331         654   985  \n",
       "1  0.363478  0.353739  0.696087     131         670   801  \n",
       "2  0.196364  0.189405  0.437273     120        1229  1349  \n",
       "3  0.200000  0.212122  0.590435     108        1454  1562  \n",
       "4  0.226957  0.229270  0.436957      82        1518  1600  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_col = ['instant', 'windspeed']\n",
    "\n",
    "for i in day_df.columns:\n",
    "  if i in drop_col:\n",
    "    day_df.drop(labels=i, axis=1, inplace=True)\n",
    "\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Mengubah beberapa detail tentang kolom\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kita ubah nama pada kolom agar memudahkan saya, maka kita menggunakan tipe data yang bentuknya kategorikal, jadi beberapa yang sebelumnya adalah angka akan saya rubah. Serta jika  ada tipe data yang juga tidak sesuai,kita rubah juga."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>dateday</th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather_cond</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dateday  season  year  month  holiday  weekday  workingday  \\\n",
       "0  2011-01-01       1     0      1        0        6           0   \n",
       "1  2011-01-02       1     0      1        0        0           0   \n",
       "2  2011-01-03       1     0      1        0        1           1   \n",
       "3  2011-01-04       1     0      1        0        2           1   \n",
       "4  2011-01-05       1     0      1        0        3           1   \n",
       "\n",
       "   weather_cond      temp     atemp       hum  casual  registered  count  \n",
       "0             2  0.344167  0.363625  0.805833     331         654    985  \n",
       "1             2  0.363478  0.353739  0.696087     131         670    801  \n",
       "2             1  0.196364  0.189405  0.437273     120        1229   1349  \n",
       "3             1  0.200000  0.212122  0.590435     108        1454   1562  \n",
       "4             1  0.226957  0.229270  0.436957      82        1518   1600  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mengubah nama judul kolom\n",
    "day_df.rename(columns={\n",
    "    'dteday': 'dateday',\n",
    "    'yr': 'year',\n",
    "    'mnth': 'month',\n",
    "    'weathersit': 'weather_cond',\n",
    "    'cnt': 'count'\n",
    "}, inplace=True)\n",
    "\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengubah angka menjadi keterangan\n",
    "day_df['month'] = day_df['month'].map({\n",
    "    1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n",
    "    7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n",
    "})\n",
    "day_df['season'] = day_df['season'].map({\n",
    "    1: 'Spring', 2: 'Summer', 3: 'Fall', 4: 'Winter'\n",
    "})\n",
    "day_df['weekday'] = day_df['weekday'].map({\n",
    "    0: 'Sun', 1: 'Mon', 2: 'Tue', 3: 'Wed', 4: 'Thu', 5: 'Fri', 6: 'Sat'\n",
    "})\n",
    "day_df['weather_cond'] = day_df['weather_cond'].map({\n",
    "    1: 'Clear/Partly Cloudy',\n",
    "    2: 'Misty/Cloudy',\n",
    "    3: 'Light Snow/Rain',\n",
    "    4: 'Severe Weather'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengubah tipe data ke datetime\n",
    "day_df['dateday'] = pd.to_datetime(day_df.dateday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>dateday</th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather_cond</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0</td>\n",
       "      <td>Sat</td>\n",
       "      <td>0</td>\n",
       "      <td>Misty/Cloudy</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>Misty/Cloudy</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0</td>\n",
       "      <td>Mon</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear/Partly Cloudy</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0</td>\n",
       "      <td>Tue</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear/Partly Cloudy</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0</td>\n",
       "      <td>Wed</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear/Partly Cloudy</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dateday  season year month holiday weekday workingday  \\\n",
       "0 2011-01-01  Spring    0   Jan       0     Sat          0   \n",
       "1 2011-01-02  Spring    0   Jan       0     Sun          0   \n",
       "2 2011-01-03  Spring    0   Jan       0     Mon          1   \n",
       "3 2011-01-04  Spring    0   Jan       0     Tue          1   \n",
       "4 2011-01-05  Spring    0   Jan       0     Wed          1   \n",
       "\n",
       "          weather_cond      temp     atemp       hum  casual  registered  \\\n",
       "0         Misty/Cloudy  0.344167  0.363625  0.805833     331         654   \n",
       "1         Misty/Cloudy  0.363478  0.353739  0.696087     131         670   \n",
       "2  Clear/Partly Cloudy  0.196364  0.189405  0.437273     120        1229   \n",
       "3  Clear/Partly Cloudy  0.200000  0.212122  0.590435     108        1454   \n",
       "4  Clear/Partly Cloudy  0.226957  0.229270  0.436957      82        1518   \n",
       "\n",
       "   count  \n",
       "0    985  \n",
       "1    801  \n",
       "2   1349  \n",
       "3   1562  \n",
       "4   1600  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mengubaha tipe data ke categorical\n",
    "day_df['season'] = day_df.season.astype('category')\n",
    "day_df['year'] = day_df.year.astype('category')\n",
    "day_df['month'] = day_df.month.astype('category')\n",
    "day_df['holiday'] = day_df.holiday.astype('category')\n",
    "day_df['weekday'] = day_df.weekday.astype('category')\n",
    "day_df['workingday'] = day_df.workingday.astype('category')\n",
    "day_df['weather_cond'] = day_df.weather_cond.astype('category')\n",
    "\n",
    "day_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   dateday       731 non-null    datetime64[ns]\n",
      " 1   season        731 non-null    category      \n",
      " 2   year          731 non-null    category      \n",
      " 3   month         731 non-null    category      \n",
      " 4   holiday       731 non-null    category      \n",
      " 5   weekday       731 non-null    category      \n",
      " 6   workingday    731 non-null    category      \n",
      " 7   weather_cond  731 non-null    category      \n",
      " 8   temp          731 non-null    float64       \n",
      " 9   atemp         731 non-null    float64       \n",
      " 10  hum           731 non-null    float64       \n",
      " 11  casual        731 non-null    int64         \n",
      " 12  registered    731 non-null    int64         \n",
      " 13  count         731 non-null    int64         \n",
      "dtypes: category(7), datetime64[ns](1), float64(3), int64(3)\n",
      "memory usage: 46.5 KB\n"
     ]
    }
   ],
   "source": [
    "day_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan bulan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\2974312987.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='month').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>7460</td>\n",
       "      <td>795</td>\n",
       "      <td>4484.900000</td>\n",
       "      <td>269094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>7865</td>\n",
       "      <td>1115</td>\n",
       "      <td>5664.419355</td>\n",
       "      <td>351194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>6606</td>\n",
       "      <td>441</td>\n",
       "      <td>3403.806452</td>\n",
       "      <td>211036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>5062</td>\n",
       "      <td>1005</td>\n",
       "      <td>2655.298246</td>\n",
       "      <td>151352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>4521</td>\n",
       "      <td>431</td>\n",
       "      <td>2176.338710</td>\n",
       "      <td>134933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>8173</td>\n",
       "      <td>3285</td>\n",
       "      <td>5563.677419</td>\n",
       "      <td>344948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>8120</td>\n",
       "      <td>3767</td>\n",
       "      <td>5772.366667</td>\n",
       "      <td>346342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>8362</td>\n",
       "      <td>605</td>\n",
       "      <td>3692.258065</td>\n",
       "      <td>228920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>8294</td>\n",
       "      <td>2633</td>\n",
       "      <td>5349.774194</td>\n",
       "      <td>331686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>6852</td>\n",
       "      <td>1495</td>\n",
       "      <td>4247.183333</td>\n",
       "      <td>254831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>8156</td>\n",
       "      <td>22</td>\n",
       "      <td>5199.225806</td>\n",
       "      <td>322352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>8714</td>\n",
       "      <td>1842</td>\n",
       "      <td>5766.516667</td>\n",
       "      <td>345991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count                           \n",
       "        max   min         mean     sum\n",
       "month                                 \n",
       "Apr    7460   795  4484.900000  269094\n",
       "Aug    7865  1115  5664.419355  351194\n",
       "Dec    6606   441  3403.806452  211036\n",
       "Feb    5062  1005  2655.298246  151352\n",
       "Jan    4521   431  2176.338710  134933\n",
       "Jul    8173  3285  5563.677419  344948\n",
       "Jun    8120  3767  5772.366667  346342\n",
       "Mar    8362   605  3692.258065  228920\n",
       "May    8294  2633  5349.774194  331686\n",
       "Nov    6852  1495  4247.183333  254831\n",
       "Oct    8156    22  5199.225806  322352\n",
       "Sep    8714  1842  5766.516667  345991"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='month').agg({\n",
    "    'count': ['max', 'min', 'mean', 'sum']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil diatas, dapat disimpulkan bahwa bulan yang memiliki rata-rata terbesar dan jumlah terbanyak adalah bulan Juni. Sedangkan rata-rata terkecil dan jumlah terkecil adalah bulan Januari."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan keadaan cuaca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\2311190310.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='weather_cond').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather_cond</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear/Partly Cloudy</th>\n",
       "      <td>8714</td>\n",
       "      <td>431</td>\n",
       "      <td>4876.786177</td>\n",
       "      <td>2257952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Snow/Rain</th>\n",
       "      <td>4639</td>\n",
       "      <td>22</td>\n",
       "      <td>1803.285714</td>\n",
       "      <td>37869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misty/Cloudy</th>\n",
       "      <td>8362</td>\n",
       "      <td>605</td>\n",
       "      <td>4035.862348</td>\n",
       "      <td>996858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count                           \n",
       "                      max  min         mean      sum\n",
       "weather_cond                                        \n",
       "Clear/Partly Cloudy  8714  431  4876.786177  2257952\n",
       "Light Snow/Rain      4639   22  1803.285714    37869\n",
       "Misty/Cloudy         8362  605  4035.862348   996858"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='weather_cond').agg({\n",
    "    'count': ['max', 'min', 'mean', 'sum']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil diatas, dapat disimpulkan bahwa para penyewa sepeda cenderung menyewa pada saat cuaca ketika sedang cerah atau mendung. Sangat sedikit ketika salju turun dan tidak ada ketika cuaca sedang sangat buruk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan holiday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\1497150874.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='holiday').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8714</td>\n",
       "      <td>22</td>\n",
       "      <td>4527.104225</td>\n",
       "      <td>3214244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7403</td>\n",
       "      <td>1000</td>\n",
       "      <td>3735.000000</td>\n",
       "      <td>78435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count                            \n",
       "          max   min         mean      sum\n",
       "holiday                                  \n",
       "0        8714    22  4527.104225  3214244\n",
       "1        7403  1000  3735.000000    78435"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='holiday').agg({\n",
    "    'count': ['max', 'min', 'mean', 'sum']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil diatas, dapat disimpulkan bahwa para penyewa sepeda lebih suka menyewa sepeda saat bukan hari-hari libur, melainkan pada saat hari-hari biasa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Bandingkan penyewa sepeda pada saat hari kerja (weekday) vs akhir pekan (weekend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\2870456447.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='weekday').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>8362</td>\n",
       "      <td>1167</td>\n",
       "      <td>4690.288462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>7525</td>\n",
       "      <td>22</td>\n",
       "      <td>4338.123810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>8714</td>\n",
       "      <td>627</td>\n",
       "      <td>4550.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>8227</td>\n",
       "      <td>605</td>\n",
       "      <td>4228.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>7804</td>\n",
       "      <td>431</td>\n",
       "      <td>4667.259615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>7767</td>\n",
       "      <td>683</td>\n",
       "      <td>4510.663462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>8173</td>\n",
       "      <td>441</td>\n",
       "      <td>4548.538462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count                   \n",
       "          max   min         mean\n",
       "weekday                         \n",
       "Fri      8362  1167  4690.288462\n",
       "Mon      7525    22  4338.123810\n",
       "Sat      8714   627  4550.542857\n",
       "Sun      8227   605  4228.828571\n",
       "Thu      7804   431  4667.259615\n",
       "Tue      7767   683  4510.663462\n",
       "Wed      8173   441  4548.538462"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='weekday').agg({\n",
    "    'count':['max','min','mean']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil diatas, Urutan rata-rata jumlah penyewa dari terbesar ke yang terkecil yakni: Jumat (Fri), Kamis (Thu), Sabtu (Sat), Rabu (Wed), Selasa (Tue), Senin (Mon), dan Minggu (Sun)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan workingday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\3484920239.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='workingday').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8714</td>\n",
       "      <td>605</td>\n",
       "      <td>4330.168831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8362</td>\n",
       "      <td>22</td>\n",
       "      <td>4584.820000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count                  \n",
       "             max  min         mean\n",
       "workingday                        \n",
       "0           8714  605  4330.168831\n",
       "1           8362   22  4584.820000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='workingday').agg({\n",
    "    'count': ['max', 'min', 'mean']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil diatas, jumlah penyewaan sepeda terbesar jatuh kepada workingday atau hari kerja dibandingkan dengan hari non-workingday. Namun dapat kita lihat juga, hasilnya lumayan berbeda walaupun tidak banyal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\3641718347.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='season').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fall</th>\n",
       "      <td>1202.611702</td>\n",
       "      <td>4441.691489</td>\n",
       "      <td>8714</td>\n",
       "      <td>1115</td>\n",
       "      <td>5644.303191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spring</th>\n",
       "      <td>334.928177</td>\n",
       "      <td>2269.204420</td>\n",
       "      <td>7836</td>\n",
       "      <td>431</td>\n",
       "      <td>2604.132597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Summer</th>\n",
       "      <td>1106.097826</td>\n",
       "      <td>3886.233696</td>\n",
       "      <td>8362</td>\n",
       "      <td>795</td>\n",
       "      <td>4992.331522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winter</th>\n",
       "      <td>729.112360</td>\n",
       "      <td>3999.050562</td>\n",
       "      <td>8555</td>\n",
       "      <td>22</td>\n",
       "      <td>4728.162921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             casual   registered count                   \n",
       "               mean         mean   max   min         mean\n",
       "season                                                   \n",
       "Fall    1202.611702  4441.691489  8714  1115  5644.303191\n",
       "Spring   334.928177  2269.204420  7836   431  2604.132597\n",
       "Summer  1106.097826  3886.233696  8362   795  4992.331522\n",
       "Winter   729.112360  3999.050562  8555    22  4728.162921"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='season').agg({\n",
    "    'casual': 'mean',\n",
    "    'registered': 'mean',\n",
    "    'count': ['max', 'min', 'mean']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil analisis diatas, para penyewa sepeda baik Casual Dan Registered, suka sekali pada musim gugur (Fall), dapat dilihat dari rata-ratanya. Sedangkan yang paling sedikit adalah pada musim semi (Spring) dengan rata-rata yang paling kecil."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Kelompokkan temp, hum, dan humidity berdasarkan season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\3725487608.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  day_df.groupby(by='season').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">temp</th>\n",
       "      <th colspan=\"3\" halign=\"left\">atemp</th>\n",
       "      <th colspan=\"3\" halign=\"left\">hum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fall</th>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.469167</td>\n",
       "      <td>0.706309</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.655898</td>\n",
       "      <td>0.939565</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.633482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spring</th>\n",
       "      <td>0.572500</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.297748</td>\n",
       "      <td>0.548617</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.296914</td>\n",
       "      <td>0.948261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.582903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Summer</th>\n",
       "      <td>0.808333</td>\n",
       "      <td>0.253043</td>\n",
       "      <td>0.544405</td>\n",
       "      <td>0.757579</td>\n",
       "      <td>0.250339</td>\n",
       "      <td>0.520307</td>\n",
       "      <td>0.922500</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.626948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winter</th>\n",
       "      <td>0.657500</td>\n",
       "      <td>0.220833</td>\n",
       "      <td>0.422906</td>\n",
       "      <td>0.607975</td>\n",
       "      <td>0.227913</td>\n",
       "      <td>0.415539</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.333478</td>\n",
       "      <td>0.668719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp                         atemp                           hum  \\\n",
       "             max       min      mean       max       min      mean       max   \n",
       "season                                                                         \n",
       "Fall    0.861667  0.469167  0.706309  0.840896  0.242400  0.655898  0.939565   \n",
       "Spring  0.572500  0.059130  0.297748  0.548617  0.079070  0.296914  0.948261   \n",
       "Summer  0.808333  0.253043  0.544405  0.757579  0.250339  0.520307  0.922500   \n",
       "Winter  0.657500  0.220833  0.422906  0.607975  0.227913  0.415539  0.972500   \n",
       "\n",
       "                            \n",
       "             min      mean  \n",
       "season                      \n",
       "Fall    0.360000  0.633482  \n",
       "Spring  0.000000  0.582903  \n",
       "Summer  0.254167  0.626948  \n",
       "Winter  0.333478  0.668719  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_df.groupby(by='season').agg({\n",
    "    'temp': ['max', 'min', 'mean'],\n",
    "    'atemp': ['max', 'min', 'mean'],\n",
    "    'hum': ['max', 'min', 'mean']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil analisis diatas, dapat disimpulkan beberapa pernyataan sebagai berikut:\n",
    "- Rata-rata temperatur dan atemp tertinggi terjadi pada musim gugur (fall season) yang selanjutnya diikuti oleh summer, winter, dan spring.\n",
    "- Rata-rata humidity tertinggi terjadi pada musim terjadi pada musim dingin (winter season) yang selanjutnya diikuti oleh fall, summer, dan spring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Hubungan antara penyewa casual, registered, dan count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "correlation_matrix = day_df.corr(numeric_only=True)\n",
    "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
    "\n",
    "sn.heatmap(\n",
    "    correlation_matrix,\n",
    "    annot=True,\n",
    "    mask=mask,\n",
    "    cmap=\"coolwarm\",\n",
    "    center=0,\n",
    "    fmt=\".2f\")\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil analisis diatas, didapat pernyataan sebagai berikut:\n",
    "- atemp dan temp sangat berkorelasi (0.99).\n",
    "- hum memiliki korelasi lemah dengan temp dan atemp (0.13 dan 0.14).\n",
    "- casual cukup berkorelasi dengan temp dan atemp (0.54), dan sedikit negatif dengan hum (-0.08).\n",
    "- registered memiliki pola yang sama seperti casual, dan berkorelasi moderat dengan casual (0.40).\n",
    "- count berkorelasi kuat dengan temp, atemp, casual, dan registered (0.63, 0.63, 0.67, dan 0.95), dan sedikit negatif dengan hum (-0.10)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Visualization & Explanatory Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Pertanyaan 1: Apakah ada peran cuaca terhadap jumlah pengguna sepeda?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sn.barplot(\n",
    "    x='weather_cond',\n",
    "    y='count',\n",
    "    data=day_df)\n",
    "\n",
    "plt.title('Jumlah Pengguna Sepeda berdasarkan Kondisi Cuaca')\n",
    "plt.xlabel('Kondisi Cuaca')\n",
    "plt.ylabel('Jumlah Pengguna Sepeda')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan visualisasi data menggunakan boxplot, dapat disimpulkan bahwa jelas terdapat hubungan antara kondisi cuaca dan penyewa sepeda, Kondisi cuaca saat sedang cerah/setengah mendung menjadi kondisi yang paling diminati, kemudian diikuti dengan cuaca berkabut/mendung, dan terakhir ketika cuaca sedang sedikit bersalju/hujan menjadi yang paling sedikit diminati."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Pertanyaan 2: Bagaimana kondisi yang tampak ketika sepeda digunakan pada workingday, holiday, dan weekday?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15,10))\n",
    "\n",
    "# Berdasarkan workingday\n",
    "sn.barplot(\n",
    "    x='workingday',\n",
    "    y='count',\n",
    "    data=day_df,\n",
    "    ax=axes[0])\n",
    "axes[0].set_title('Jumlah Pengguna Sepeda berdasarkan Hari Kerja')\n",
    "axes[0].set_xlabel('Hari Kerja')\n",
    "axes[0].set_ylabel('Jumlah Pengguna Sepeda')\n",
    "\n",
    "# Berdasarkan holiday\n",
    "sn.barplot(\n",
    "  x='holiday',\n",
    "  y='count',\n",
    "  data=day_df,\n",
    "  ax=axes[1])\n",
    "axes[1].set_title('Jumlah Pengguna Sepeda berdasarkan Hari Libur')\n",
    "axes[1].set_xlabel('Hari Libur')\n",
    "axes[1].set_ylabel('Jumlah Pengguna Sepeda')\n",
    "\n",
    "# Berdasarkan weekday\n",
    "sn.barplot(\n",
    "  x='weekday',\n",
    "  y='count',\n",
    "  data=day_df,\n",
    "  ax=axes[2])\n",
    "axes[2].set_title('Jumlah Pengguna Sepeda berdasarkan Hari dalam Seminggu')\n",
    "axes[2].set_xlabel('Hari dalam Seminggu')\n",
    "axes[2].set_ylabel('Jumlah Pengguna Sepeda')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan visualisasi data diatas menggunakan barplot, didapat pernyataan sebagai berikut:\n",
    "- Jumlah penyewa sepeda lebih banyak pada hari kerja (yakni senin - jumat) daripada akhir pekan (yakni sabtu dan minggu). 0 menandakan akhir pekan dan 1 menandakan hari kerja.\n",
    "- Jumlah penyewa sepeda jauh lebih banyak pada hari biasa (bukan hari libur) dibandingkan hari libur (nasional).\n",
    "- Jumlah penyewa sepeda pada hari Jumat ialah yang menempati posisi pertama sebagai hari dengan penyewa sepeda terbanyak, dan hari Minggu menempati posisi terakhir dengan jumlah penyewa sepeda yang paling sedikit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Pertanyaan 3: Bagaimana tren penggunaan sepeda dalam setahun, apakah tahun 2011 atau 2012 yang lebih unggul?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\1386823209.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  monthly_counts = day_df.groupby(by=[\"month\",\"year\"]).agg({\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_df['month'] = pd.Categorical(day_df['month'], categories=\n",
    "    ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'],\n",
    "    ordered=True)\n",
    "\n",
    "monthly_counts = day_df.groupby(by=[\"month\",\"year\"]).agg({\n",
    "    \"count\": \"sum\"\n",
    "}).reset_index()\n",
    "\n",
    "sn.lineplot(\n",
    "    data=monthly_counts,\n",
    "    x=\"month\",\n",
    "    y=\"count\",\n",
    "    hue=\"year\",\n",
    "    palette=\"rocket\",\n",
    "    marker=\"o\")\n",
    "\n",
    "plt.title(\"Jumlah total sepeda yang disewakan berdasarkan Bulan dan tahun\")\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(None)\n",
    "plt.legend(title=\"Tahun\", loc=\"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan visualisasi diatas menggunakan lineplot, dapat disimpulkan bahwa tahun 2012 (yang diwakili oleh angka 1) jelas menang unggul baik dari januari sampai desember. Puncak penyewaan sepeda pada tahun 2012 terjadi pada bulan September, sementara bulan yang paling sepi terjadi pada bulan januari. Sedangkan pada tahun 2011 puncak penyewaan sepeda terjadi pada bulan Juni dan memiliki penyewaan tersepi pada bulan Januari."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Pertanyaan 4: Bagaimana variabel seperti temp dan atemp serta humidity dapat mempengaruhi jumlah total pengguna sepeda (baik Casual ataupun Registered)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Humidity vs Count')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# Scatter plot untuk 'temp' vs 'count'\n",
    "plt.subplot(1, 3, 1)\n",
    "sn.scatterplot(\n",
    "    x='temp',\n",
    "    y='count',\n",
    "    data=day_df,\n",
    "    alpha=0.5\n",
    ")\n",
    "plt.title('Temperature vs Count')\n",
    "\n",
    "# Scatter plot untuk 'atemp' vs 'count'\n",
    "plt.subplot(1, 3, 2)\n",
    "sn.scatterplot(\n",
    "    x='atemp',\n",
    "    y='count',\n",
    "    data=day_df,\n",
    "    alpha=0.5\n",
    ")\n",
    "plt.title('Feels Like Temperature vs Count')\n",
    "\n",
    "# Scatter plot untuk 'hum' vs 'count'\n",
    "plt.subplot(1, 3, 3)\n",
    "sn.scatterplot(\n",
    "    x='hum',\n",
    "    y='count',\n",
    "    data=day_df,\n",
    "    alpha=0.5\n",
    ")\n",
    "plt.title('Humidity vs Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil analisis diatas menggunakan scatter plot, dapat kita tarik pernyataan sebagai berikut:\n",
    "- Hubungan antara temp dan count ternyata menunjukan korelasi yang positif diantara dua variabel.\n",
    "- Hubungan antara atemp dan count ternyata juga menunjukan korelasi yang positif diantara dua variabel.\n",
    "- Hubungan antara hum dan count ternyata menunjukan korelasi yang sedikit negatif, tetapi tidak terlalu signifikan karena condong ke kirinya tidak terlalu nampak.\n",
    "\n",
    "Kesimpulannya jika temperatur (temp dan atemp) meningkat, maka pengguna sepeda juga cenderung meningkat. Dan pengaruh dari humidity sepertinya sangat kecil disini, walaupun cenderung dapat menurunkan jumlah pengguna sepeda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Pertanyaan 5: Apakah ada pengaruh musim terhadap penyewa sepeda berdasarkan pelanggan Casual dan Registered?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_2876\\3020191024.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  seasonal_usage = day_df.groupby('season')[['registered', 'casual']].sum().reset_index()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAIQCAYAAABzIaQjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjFElEQVR4nO3df3zN9f//8fvZ2A+bs/m1zTI2vy3zazLze1pGU9QIScuvSptiId5vISlRfubH+mne4hP6oaJIfiXm16SkSEW8YwjbEBvb6/uH715vx4ZNL6a5XS+Xc7l0Xq/H6/V6nHOeTue+13k9j80wDEMAAAAAAEs4FXUDAAAAAFCcELIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgDcktq0aaM2bdoUerv9+/fLZrPptddes74poBAee+wxBQYGWra/Nm3aqG7dupbt73okJSXJZrNp//79RdrH1dhsNsXHxxd1G7ckm82mMWPGFHUbwG2BkAXginI/UG3btq2oWwEAAPjHKFHUDQAAAODGO3v2rEqU4KMfcDNwJgsAAEiScnJydO7cuaJuo9AMw9DZs2eLuo1bnpubGyELuEkIWQAK7ErXSV1+7cml10XNnDlTVatWValSpdSuXTsdPHhQhmHoxRdfVKVKleTu7q5OnTrpxIkTVz12VlaWRo0apdDQUHl5ecnDw0MtW7bUmjVrrrjNm2++qWrVqsnV1VV33XWXtm7des3HmPsVya+//lpPPPGEypUrJ7vdrkcffVQnT57MU//FF1+oZcuW8vDwUOnSpRUdHa1du3bleX48PT31xx9/qHPnzvL09FSFChU0ZMgQZWdnS7r4ITEwMFCdOnXKc4xz587Jy8tLTzzxhLksMzNTo0ePVvXq1eXq6qqAgAANGzZMmZmZZs2DDz6oRo0aOezrvvvuk81m06effmou27x5s2w2m7744gtJ0okTJzRkyBCFhITI09NTdrtdHTp00Hfffeewr4K+JpeOh+t5Tc6fP68XXnhBNWrUkJubm8qVK6cWLVpo5cqVDnW7d+9Wly5dVLZsWbm5ualx48YOj1O6Ma+vJC1ZskR169aVm5ub6tatq48//jjfx/Laa6+pWbNmKleunNzd3RUaGqoPPvjgms/BpVJSUtSsWTO5u7srKChIiYmJeWoKMj6k/12/NH/+fN15551ydXXV8uXLJUm7du1S27Zt5e7urkqVKmncuHHKycnJc6xPPvlE0dHR8vf3l6urq6pVq6YXX3zRHNu59u7dq5iYGPn5+cnNzU2VKlVS9+7dlZ6ebtbMmTNHbdu2lY+Pj1xdXRUcHKzZs2fnOWZgYKA6duyoFStWqHHjxnJ3d9cbb7xxxeds3LhxcnJy0uuvvy5J+v333/XUU0+pVq1acnd3V7ly5dS1a9c815rljpcNGzYoISFBFSpUkIeHhx544AEdO3bsisfLlftv/8CBA+rYsaM8PT11xx13aObMmZKknTt3qm3btvLw8FCVKlW0YMECh+3HjBkjm82WZ7/5XRu3bds2RUVFqXz58ubY6NOnj8N2l1+Tlbv/n3/+WY888oi8vLxUoUIFPf/88zIMQwcPHlSnTp1kt9vl5+enSZMmXfMxA7iIP2cAuGHmz5+vrKwsDRw4UCdOnNDEiRP10EMPqW3btlq7dq2ee+45/fLLL3r99dc1ZMgQvfvuu1fcV0ZGht5++2316NFD/fv316lTp/TOO+8oKipKW7ZsUYMGDRzqFyxYoFOnTumJJ56QzWbTxIkT9eCDD+q3335TyZIlr9l7fHy8vL29NWbMGO3Zs0ezZ8/W77//rrVr15ofeubNm6fY2FhFRUVpwoQJ+uuvvzR79my1aNFC3377rUPwzM7OVlRUlMLCwvTaa6/pq6++0qRJk1StWjUNGDBANptNjzzyiCZOnKgTJ06obNmy5rafffaZMjIy9Mgjj0i6eLbh/vvv1zfffKPHH39cderU0c6dOzVlyhT9/PPPWrJkiSSpZcuW+uSTT5SRkSG73S7DMLRhwwY5OTlp/fr1uv/++yVJ69evl5OTk5o3by5J+u2337RkyRJ17dpVQUFBOnLkiN544w21bt1aP/74o/z9/W/qazJmzBiNHz9e/fr1U5MmTZSRkaFt27Zp+/btuueeeyRdDATNmzfXHXfcoeHDh8vDw0OLFi1S586d9eGHH+qBBx64Ya/vl19+qZiYGAUHB2v8+PE6fvy4evfurUqVKuV5LNOmTdP999+vnj17KisrS++//766du2qpUuXKjo6+orPQa6TJ0/q3nvv1UMPPaQePXpo0aJFGjBggFxcXMwP1AUdH7lWr16tRYsWKT4+XuXLl1dgYKBSU1MVERGhCxcumM/nm2++KXd39zw9JSUlydPTUwkJCfL09NTq1as1atQoZWRk6NVXX5V0MZBHRUUpMzNTAwcOlJ+fn/744w8tXbpUaWlp8vLykiTNnj1bd955p+6//36VKFFCn332mZ566inl5OQoLi7O4bh79uxRjx499MQTT6h///6qVatWvs/ZyJEj9fLLL+uNN95Q//79JUlbt27Vxo0b1b17d1WqVEn79+/X7Nmz1aZNG/34448qVaqUwz4GDhyoMmXKaPTo0dq/f7+mTp2q+Ph4LVy48JqvWXZ2tjp06KBWrVpp4sSJmj9/vuLj4+Xh4aF///vf6tmzpx588EElJibq0UcfVXh4uIKCgq6530sdPXpU7dq1U4UKFTR8+HB5e3tr//79+uijjwq0fbdu3VSnTh298sorWrZsmcaNG6eyZcvqjTfeUNu2bTVhwgTNnz9fQ4YM0V133aVWrVoVqj/gtmQAwBXMmTPHkGRs3brVMAzDaN26tdG6des8dbGxsUaVKlXM+/v27TMkGRUqVDDS0tLM5SNGjDAkGfXr1zfOnz9vLu/Ro4fh4uJinDt3zlx2+bEuXLhgZGZmOhz35MmThq+vr9GnT588xy5Xrpxx4sQJc/knn3xiSDI+++yzAj3m0NBQIysry1w+ceJEQ5LxySefGIZhGKdOnTK8vb2N/v37O2yfmppqeHl5OSyPjY01JBljx451qG3YsKERGhpq3t+zZ48hyZg9e7ZD3f33328EBgYaOTk5hmEYxrx58wwnJydj/fr1DnWJiYmGJGPDhg2GYRjG1q1bDUnG559/bhiGYXz//feGJKNr165GWFiYw/4bNmxo3j937pyRnZ3tsO99+/YZrq6uDo/hZr0m9evXN6Kjo69ac/fddxshISEOYygnJ8do1qyZUaNGDXPZjXh9GzRoYFSsWNFhrH/55ZeGJId/F4ZhGH/99ZfD/aysLKNu3bpG27Ztr/r4DOPivwlJxqRJk8xlmZmZRoMGDQwfHx/z8RR0fBiGYUgynJycjF27djnUDho0yJBkbN682Vx29OhRw8vLy5Bk7Nu374qPyTAM44knnjBKlSplvh7ffvutIclYvHjxVR9jfvuKiooyqlat6rCsSpUqhiRj+fLleeolGXFxcYZhGMazzz5rODk5GUlJSdc8TnJysiHJ+M9//mMuyx0vkZGR5r8/wzCMwYMHG87Ozg6veX5y/+2//PLL5rKTJ08a7u7uhs1mM95//31z+e7duw1JxujRo81lo0ePNvL7qJbbV+7r8PHHHzu8V1/Jlfb/+OOPm8suXLhgVKpUybDZbMYrr7ySp+/Y2NirHgPARXxdEMAN07VrV/Mv1JIUFhYmSXrkkUccrgsICwtTVlaW/vjjjyvuy9nZWS4uLpIu/qX+xIkTunDhgho3bqzt27fnqe/WrZvKlClj3m/ZsqWki2dpCuLxxx93OLsyYMAAlShRQp9//rkkaeXKlUpLS1OPHj30559/mjdnZ2eFhYXl+zXGJ5980uF+y5YtHfqpWbOmwsLCNH/+fHPZiRMn9MUXX6hnz57mGZbFixerTp06ql27tsOx27ZtK0nmsRs2bChPT099/fXXki6esapUqZIeffRRbd++XX/99ZcMw9A333xjPj+S5OrqKieni/97yM7O1vHjx+Xp6alatWo5PNc36zXx9vbWrl27tHfv3nzXnzhxQqtXr9ZDDz2kU6dOmc/H8ePHFRUVpb179+YZW1a9vocPH9aOHTsUGxvrMNbvueceBQcH5+n10jNBJ0+eVHp6ulq2bJnv85WfEiVKOHxt1MXFRU888YSOHj2qlJQUSQUfH7lat26dp9fPP/9cTZs2VZMmTcxlFSpUUM+ePa/6mHKf/5YtW+qvv/7S7t27Jcl8blasWKG//vrrio/v0n2lp6frzz//VOvWrfXbb785fK1QkoKCghQVFZXvfgzDUHx8vKZNm6b33ntPsbGxVzzO+fPndfz4cVWvXl3e3t75vhaPP/64w9f2WrZsqezsbP3+++9XfCyX6tevn/nf3t7eqlWrljw8PPTQQw+Zy2vVqiVvb+8Cv0ddytvbW5K0dOlSnT9/vtDbX9qfs7OzGjduLMMw1Ldv3zx9X09/wO2IrwsCuGEqV67scD/3g1ZAQEC+y/O7JuZSc+fO1aRJk7R7926HDxL5fbXm8mPnfri/1jFy1ahRw+G+p6enKlasaF4DkfuBP/eD6+XsdrvDfTc3N1WoUCFPT5f38+ijjyo+Pl6///67qlSposWLF+v8+fPq1auXWbN371799NNPefaX6+jRo5IuflgKDw/X+vXrJV0MWS1btlSLFi2UnZ2tTZs2ydfXVydOnHAIWTk5OZo2bZpmzZqlffv2OVxbU65cOYdj3YzXZOzYserUqZNq1qypunXrqn379urVq5fq1asnSfrll19kGIaef/55Pf/881d8Tu644w7zvlWvb+6H7Mv3JylPKJUufggeN26cduzY4XB9VH7X3eTH399fHh4eDstq1qwp6eK1b02bNi3w+MiV32v1+++/m38UufwxXW7Xrl0aOXKkVq9erYyMDId1ucEoKChICQkJmjx5subPn6+WLVvq/vvvN68DyrVhwwaNHj1aycnJecJYenq6Q+3VvlL3n//8R6dPn9bs2bPVo0ePPOvPnj2r8ePHa86cOfrjjz9kGEaeni/1d95P8vu37+XlpUqVKuV53b28vAr8HnWp1q1bKyYmRi+88IKmTJmiNm3aqHPnznr44Yfl6up6ze3ze692c3NT+fLl8yw/fvx4ofsDbkeELAAFZrPZHD6M5Lr8Avdczs7OhVqe375zvffee3rsscfUuXNnDR06VD4+PnJ2dtb48eP166+/WnKMwsidAGDevHny8/PLs/7yGbyu1M/lunfvrsGDB2v+/Pn617/+pffee0+NGzd2+HCbk5OjkJAQTZ48Od99XBpiW7RooZdeeknnzp3T+vXr9e9//1ve3t6qW7eu1q9fL19fX0lyCFkvv/yynn/+efXp00cvvviiypYtKycnJw0aNMhh4oOb9Zq0atVKv/76qz755BN9+eWXevvttzVlyhQlJiaqX79+Zk9Dhgy54pmN6tWrX/UYlyvs61sQudfBtWrVSrNmzVLFihVVsmRJzZkzJ8+EB39HYcaHpHyvsyqotLQ0tW7dWna7XWPHjlW1atXk5uam7du367nnnnMYL5MmTdJjjz1mvo5PP/20xo8fr02bNqlSpUr69ddfdffdd6t27dqaPHmyAgIC5OLios8//1xTpkzJM+nG1fpu3ry5duzYoRkzZuihhx5yuMZRuniN1Zw5czRo0CCFh4fLy8tLNptN3bt3z3dyj7/zfvJ33gevFL4vf8+12Wz64IMPtGnTJn322WdasWKF+vTpo0mTJmnTpk3y9PQsdI83+j0UKO4IWQAKrEyZMvl+VaSgX5n5Oz744ANVrVpVH330kcMHj9GjR9+Q4+3du1cRERHm/dOnT+vw4cO69957JUnVqlWTJPn4+CgyMtKy45YtW1bR0dGaP3++evbsqQ0bNmjq1KkONdWqVdN3332nu++++5pnQFq2bKmsrCz93//9n/744w8zTLVq1coMWTVr1jTDlnTxuY6IiNA777zjsK+0tDSHv2zfzNekbNmy6t27t3r37q3Tp0+rVatWGjNmjPr166eqVatKkkqWLFng18Kq17dKlSrm/i63Z88eh/sffvih3NzctGLFCoezC3PmzClQz5J06NAhnTlzxuFs1s8//yxJ5kQchRkfV1KlSpUCPaa1a9fq+PHj+uijjxwmQ9i3b1+++w0JCVFISIhGjhypjRs3qnnz5kpMTNS4ceP02WefKTMzU59++qnDmZWrzSB6JdWrV9fEiRPVpk0btW/fXqtWrVLp0qXN9R988IFiY2MdZss7d+6c0tLSCn2sGyn3jFlaWpr5lUDpyu+5TZs2VdOmTfXSSy9pwYIF6tmzp95//32HrwMCuDm4JgtAgVWrVk27d+92mLr4u+++04YNG274sXP/qnrpX1E3b96s5OTkG3K8N9980+Hrb7Nnz9aFCxfUoUMHSVJUVJTsdrtefvnlfK+BKMj0zlfSq1cv/fjjjxo6dKicnZ3VvXt3h/UPPfSQ/vjjD7311lt5tj179qzOnDlj3g8LC1PJkiU1YcIElS1bVnfeeaeki+Fr06ZNWrduncNZLOnic335X6sXL16c57qmm/WaXP71JE9PT1WvXt38up2Pj4/atGmjN954Q4cPH86zfX6vhVWvb8WKFdWgQQPNnTvX4WtmK1eu1I8//uiwjbOzs2w2m8NZiP379+eZ7e9qLly44DBVeVZWlt544w1VqFBBoaGhkgo3Pq7k3nvv1aZNm7RlyxZz2bFjxxyuF8x9TJLjGMjKytKsWbMc6jIyMnThwgWHZSEhIXJycjJfx/z2lZ6eXqgQeql69erp888/108//aT77rvP4Xe08hvjr7/++hXPyheV3LCfe12lJJ05c0Zz5851qDt58mSex5M7u+fl0/YDuDk4kwWgwPr06aPJkycrKipKffv21dGjR5WYmKg777wzz7UYVuvYsaM++ugjPfDAA4qOjta+ffuUmJio4OBgnT592vLjZWVl6e6779ZDDz2kPXv2aNasWWrRooU57bndbtfs2bPVq1cvNWrUSN27d1eFChV04MABLVu2TM2bN9eMGTOu69jR0dEqV66cFi9erA4dOsjHx8dhfa9evbRo0SI9+eSTWrNmjZo3b67s7Gzt3r1bixYtMn87SJJKlSql0NBQbdq0yfyNLOnimawzZ87ozJkzeUJWx44dNXbsWPXu3VvNmjXTzp07NX/+fPOM0aV1N+M1CQ4OVps2bRQaGqqyZctq27Zt+uCDDxQfH2/WzJw5Uy1atFBISIj69++vqlWr6siRI0pOTtZ///vffH/jy6rXd/z48YqOjlaLFi3Up08fnThxQq+//rruvPNOh+chOjpakydPVvv27fXwww/r6NGjmjlzpqpXr67vv/++QM+Fv7+/JkyYoP3796tmzZpauHChduzYoTfffNOcyKMw4+NKhg0bpnnz5ql9+/Z65plnzCncq1Sp4tBrs2bNVKZMGcXGxurpp5+WzWbTvHnz8nzgX716teLj49W1a1fVrFlTFy5c0Lx58+Ts7KyYmBhJUrt27eTi4qL77rtPTzzxhE6fPq233npLPj4++YbngmjatKk++eQT3XvvverSpYuWLFmikiVLqmPHjpo3b568vLwUHBys5ORkffXVV3muOSxq7dq1U+XKldW3b1/zjy7vvvuuORZzzZ07V7NmzdIDDzygatWq6dSpU3rrrbdkt9vNs7MAbrKbO5khgH+Sd99915BkbN++3Vz23nvvGVWrVjVcXFyMBg0aGCtWrLjiFO6vvvqqw/7WrFmT7zTOl08Vbxh5p3DPyckxXn75ZaNKlSqGq6ur0bBhQ2Pp0qUFPrZh5J2+OD+5vaxbt854/PHHjTJlyhienp5Gz549jePHj+epX7NmjREVFWV4eXkZbm5uRrVq1YzHHnvM2LZtm1kTGxtreHh45Nn2StMzG4ZhPPXUU4YkY8GCBfmuz8rKMiZMmGDceeedhqurq1GmTBkjNDTUeOGFF4z09HSH2qFDhxqSjAkTJjgsr169uiHJ+PXXXx2Wnzt3znj22WeNihUrGu7u7kbz5s2N5OTkIntNxo0bZzRp0sTw9vY23N3djdq1axsvvfSSwxTshmEYv/76q/Hoo48afn5+RsmSJY077rjD6Nixo/HBBx+YNTfi9TUMw/jwww+NOnXqGK6urkZwcLDx0Ucf5XkeDMMw3nnnHaNGjRqGq6urUbt2bWPOnDlXHQeXat26tXHnnXca27ZtM8LDww03NzejSpUqxowZM/LUFnR86JLpzi/3/fffG61btzbc3NyMO+64w3jxxReNd955J88U7hs2bDCaNm1quLu7G/7+/sawYcOMFStWGJKMNWvWGIZhGL/99pvRp08fo1q1aoabm5tRtmxZIyIiwvjqq68cjvnpp58a9erVM9zc3IzAwEBjwoQJ5vvQpcesUqXKFaf1z+8xffLJJ0aJEiWMbt26GdnZ2cbJkyeN3r17G+XLlzc8PT2NqKgoY/fu3UaVKlUcpijP773JMP73Xpb7+K7kSv/2c1/Ly+X3uFJSUoywsDDDxcXFqFy5sjF58uQ8U7hv377d6NGjh1G5cmXD1dXV8PHxMTp27JhnnF7+7y137B07duxv9Q0gL5thcAUjgPxNnz5dzzzzjH755RfzayvFXVJSknr37q2tW7de86/9N9LgwYP1zjvvKDU1Nc8Po+L63SqvLwCgeOOaLABXtHXrVnl4eJgX9+PmOHfunN577z3FxMQQsAAA+AfimiwAeXz44Ydau3at5s+fr379+l3XdNUovKNHj+qrr77SBx98oOPHj+uZZ54p6pYAAMB14JMTgDyGDBmiU6dOqW/fvpoyZUpRt3Pb+PHHH9WzZ0/5+Pho+vTp5uxgAADgn4VrsgAAAADAQlyTBQAAAAAWImQBAAAAgIW4JusqcnJydOjQIZUuXdr8AU8AAAAAtx/DMHTq1Cn5+/vLyenq56oIWVdx6NAhBQQEFHUbAAAAAG4RBw8eVKVKla5aQ8i6itKlS0u6+ETa7fYi7gYAAABAUcnIyFBAQICZEa6GkHUVuV8RtNvthCwAAAAABbqMiIkvAAAAAMBChCwAAAAAsBAhCwAAAAAsxDVZAAAAQAFkZ2fr/PnzRd0GbiAXF5drTs9eEIQsAAAA4CoMw1BqaqrS0tKKuhXcYE5OTgoKCpKLi8vf2g8hCwAAALiK3IDl4+OjUqVKFWh2Ofzz5OTk6NChQzp8+LAqV678t15nQhYAAABwBdnZ2WbAKleuXFG3gxusQoUKOnTokC5cuKCSJUte936Y+AIAAAC4gtxrsEqVKlXEneBmyP2aYHZ29t/aDyELAAAAuAa+Inh7sOp1JmQBAAAAgIUIWQAAAACuS1JSkry9vYu6jQK7Wf0y8QUAAABwHQKHL7tpx9r/SnSht3nsscc0d+5cSVKJEiVUqVIlde3aVWPHjpWbm5slfXXr1k333ntvgWqTkpI0aNCg22IqfEIWAAAAUEy1b99ec+bM0fnz55WSkqLY2FjZbDZNmDDBkv27u7vL3d3dkn0VVHZ2tmw2myU/Gnyj3LqdAQAAAPhbXF1d5efnp4CAAHXu3FmRkZFauXKlpIu/CzV+/HgFBQXJ3d1d9evX1wcffOCw/aeffqoaNWrIzc1NERERmjt3rmw2m3k26vKv33333XeKiIhQ6dKlZbfbFRoaqm3btmnt2rXq3bu30tPTZbPZZLPZNGbMGElSZmamhgwZojvuuEMeHh4KCwvT2rVrzX3mHuPTTz9VcHCwXF1ddeDAgWtul7tt5cqVVapUKT3wwAM6fvy41U9xvghZAAAAwG3ghx9+0MaNG81pysePH6///Oc/SkxM1K5duzR48GA98sgjWrdunSRp37596tKlizp37qzvvvtOTzzxhP79739f9Rg9e/ZUpUqVtHXrVqWkpGj48OEqWbKkmjVrpqlTp8put+vw4cM6fPiwhgwZIkmKj49XcnKy3n//fX3//ffq2rWr2rdvr71795r7/euvvzRhwgS9/fbb2rVrl3x8fK653ebNm9W3b1/Fx8drx44dioiI0Lhx427EU5sHXxcEAAAAiqmlS5fK09NTFy5cUGZmppycnDRjxgxlZmbq5Zdf1ldffaXw8HBJUtWqVfXNN9/ojTfeUOvWrfXGG2+oVq1aevXVVyVJtWrV0g8//KCXXnrpisc7cOCAhg4dqtq1a0uSatSoYa7z8vKSzWaTn5+fQ/2cOXN04MAB+fv7S5KGDBmi5cuXa86cOXr55ZclXfy9slmzZql+/foF3m7atGlq3769hg0bJkmqWbOmNm7cqOXLl1vy3F4NIQsAAAAopiIiIjR79mydOXNGU6ZMUYkSJRQTE6Ndu3bpr7/+0j333ONQn5WVpYYNG0qS9uzZo7vuusthfZMmTa56vISEBPXr10/z5s1TZGSkunbtqmrVql2xfufOncrOzlbNmjUdlmdmZqpcuXLmfRcXF9WrV69Q2/3000964IEHHNaHh4cTsgAAAABcPw8PD1WvXl2S9O6776p+/fp65513VLduXUnSsmXLdMcddzhs4+rqet3HGzNmjB5++GEtW7ZMX3zxhUaPHq33338/T9jJdfr0aTk7OyslJUXOzs4O6zw9Pc3/dnd3d/ih4IJuV1QIWQAAAMBtwMnJSf/617+UkJCgn3/+2ZxAonXr1vnW16pVS59//rnDsq1bt17zODVr1lTNmjU1ePBg9ejRQ3PmzNEDDzwgFxcXZWdnO9Q2bNhQ2dnZOnr0qFq2bFngx1KQ7erUqaPNmzc7LNu0aVOBj/F3ELL+YW7m7zGg+Lue39wAAAD/XF27dtXQoUP1xhtvaMiQIRo8eLBycnLUokULpaena8OGDbLb7YqNjdUTTzyhyZMn67nnnlPfvn21Y8cOJSUlSZLDWaVcZ8+e1dChQ9WlSxcFBQXpv//9r7Zu3aqYmBhJUmBgoE6fPq1Vq1apfv36KlWqlGrWrKmePXvq0Ucf1aRJk9SwYUMdO3ZMq1atUr169RQdnf9nlYJs9/TTT6t58+Z67bXX1KlTJ61YseKmfFVQYnZBAAAA4LZRokQJxcfHa+LEiRoxYoSef/55jR8/XnXq1FH79u21bNkyBQUFSZKCgoL0wQcf6KOPPlK9evU0e/Zsc3bB/L5S6OzsrOPHj+vRRx9VzZo19dBDD6lDhw564YUXJEnNmjXTk08+qW7duqlChQqaOHGiJGnOnDl69NFH9eyzz6pWrVrq3Lmztm7dqsqVK1/1sVxru6ZNm+qtt97StGnTVL9+fX355ZcaOXKkZc/l1dgMwzBuypH+gTIyMuTl5aX09HTZ7faibkcSZ7JgLc5kAQBwdefOndO+ffsUFBQkNze3om6nyL300ktKTEzUwYMHi7qVG+Jqr3dhsgFfFwQAAACQr1mzZumuu+5SuXLltGHDBr366quKj48v6rZueYQsAAAAAPnau3evxo0bpxMnTqhy5cp69tlnNWLEiKJu65ZHyAIAAACQrylTpmjKlClF3cY/DhNfAAAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAACAIhMYGKipU6cWdRuW4neyAAAAgOsxxusmHiv9ujZLTU3VSy+9pGXLlumPP/6Qj4+PGjRooEGDBunuu++2uEnkKtSZrOzsbD3//PMKCgqSu7u7qlWrphdffFGGYZg1hmFo1KhRqlixotzd3RUZGam9e/c67OfEiRPq2bOn7Ha7vL291bdvX50+fdqh5vvvv1fLli3l5uamgIAATZw4MU8/ixcvVu3ateXm5qaQkBB9/vnnDusL0gsAAABQHO3fv1+hoaFavXq1Xn31Ve3cuVPLly9XRESE4uLiirq9Yq1QIWvChAmaPXu2ZsyYoZ9++kkTJkzQxIkT9frrr5s1EydO1PTp05WYmKjNmzfLw8NDUVFROnfunFnTs2dP7dq1SytXrtTSpUv19ddf6/HHHzfXZ2RkqF27dqpSpYpSUlL06quvasyYMXrzzTfNmo0bN6pHjx7q27evvv32W3Xu3FmdO3fWDz/8UKheAAAAgOLoqaeeks1m05YtWxQTE6OaNWvqzjvvVEJCgjZt2iRJmjx5skJCQuTh4aGAgAA99dRTDic/fv/9d913330qU6aMPDw8dOedd5onNpKSkuTt7e1wzCVLlshms5n3f/31V3Xq1Em+vr7y9PTUXXfdpa+++urGP/giVqiQtXHjRnXq1EnR0dEKDAxUly5d1K5dO23ZskXSxTNHU6dO1ciRI9WpUyfVq1dP//nPf3To0CEtWbJEkvTTTz9p+fLlevvttxUWFqYWLVro9ddf1/vvv69Dhw5JkubPn6+srCy9++67uvPOO9W9e3c9/fTTmjx5stnLtGnT1L59ew0dOlR16tTRiy++qEaNGmnGjBkF7gUAAAAojk6cOKHly5crLi5OHh4eedbnhiMnJydNnz5du3bt0ty5c7V69WoNGzbMrIuLi1NmZqa+/vpr7dy5UxMmTJCnp2eB+zh9+rTuvfderVq1St9++63at2+v++67TwcOHPjbj/FWVqiQ1axZM61atUo///yzJOm7777TN998ow4dOkiS9u3bp9TUVEVGRprbeHl5KSwsTMnJyZKk5ORkeXt7q3HjxmZNZGSknJyctHnzZrOmVatWcnFxMWuioqK0Z88enTx50qy59Di5NbnHKUgvAAAAQHH0yy+/yDAM1a5d+6p1gwYNUkREhAIDA9W2bVuNGzdOixYtMtcfOHBAzZs3V0hIiKpWraqOHTuqVatWBe6jfv36euKJJ1S3bl3VqFFDL774oqpVq6ZPP/30uh/bP0GhJr4YPny4MjIyVLt2bTk7Oys7O1svvfSSevbsKenihXWS5Ovr67Cdr6+vuS41NVU+Pj6OTZQoobJlyzrUBAUF5dlH7royZcooNTX1mse5Vi+Xy8zMVGZmpnk/IyPjak8HAAAAcEu6dM6Eq/nqq680fvx47d69WxkZGbpw4YLOnTunv/76S6VKldLTTz+tAQMG6Msvv1RkZKRiYmJUr169Avdx+vRpjRkzRsuWLdPhw4d14cIFnT17ljNZl1q0aJHmz5+vBQsWaPv27Zo7d65ee+01zZ0790b1d1ONHz9eXl5e5i0gIKCoWwIAAAAKrUaNGrLZbNq9e/cVa/bv36+OHTuqXr16+vDDD5WSkqKZM2dKkrKysiRJ/fr102+//aZevXpp586daty4sTkfg5OTU54wd/78eYf7Q4YM0ccff6yXX35Z69ev144dOxQSEmLuv7gqVMgaOnSohg8fru7duyskJES9evXS4MGDNX78eEmSn5+fJOnIkSMO2x05csRc5+fnp6NHjzqsv3Dhgk6cOOFQk98+Lj3GlWouXX+tXi43YsQIpaenm7eDBw9e6ykBAAAAbjlly5ZVVFSUZs6cqTNnzuRZn5aWppSUFOXk5GjSpElq2rSpatasac6RcKmAgAA9+eST+uijj/Tss8/qrbfekiRVqFBBp06dctj/jh07HLbdsGGDHnvsMT3wwAMKCQmRn5+f9u/fb+ljvRUVKmT99ddfcnJy3MTZ2Vk5OTmSpKCgIPn5+WnVqlXm+oyMDG3evFnh4eGSpPDwcPNFzbV69Wrl5OQoLCzMrPn6668dkvDKlStVq1YtlSlTxqy59Di5NbnHKUgvl3N1dZXdbne4AQAAAP9EM2fOVHZ2tpo0aaIPP/xQe/fu1U8//aTp06crPDxc1atX1/nz5/X666/rt99+07x585SYmOiwj0GDBmnFihXat2+ftm/frjVr1qhOnTqSpLCwMJUqVUr/+te/9Ouvv2rBggVKSkpy2L5GjRr66KOPtGPHDn333Xd6+OGHzexQnBUqZN13333mj5nt379fH3/8sSZPnqwHHnhAkmSz2TRo0CCNGzdOn376qXbu3KlHH31U/v7+6ty5sySpTp06at++vfr3768tW7Zow4YNio+PV/fu3eXv7y9Jevjhh+Xi4qK+fftq165dWrhwoaZNm6aEhASzl2eeeUbLly/XpEmTtHv3bo0ZM0bbtm1TfHx8gXsBAAAAiquqVatq+/btioiI0LPPPqu6devqnnvu0apVqzR79mzVr19fkydP1oQJE1S3bl3Nnz/f/IZaruzsbMXFxZmf4WvWrKlZs2ZJuni27L333tPnn3+ukJAQ/d///Z/GjBnjsP3kyZNVpkwZNWvWTPfdd5+ioqLUqFGjm/UUFBmbUdCr4iSdOnVKzz//vD7++GMdPXpU/v7+6tGjh0aNGmXOBGgYhkaPHq0333xTaWlpatGihWbNmqWaNWua+zlx4oTi4+P12WefycnJSTExMZo+fbrDdJDff/+94uLitHXrVpUvX14DBw7Uc88959DP4sWLNXLkSO3fv181atTQxIkTde+995rrC9LL1WRkZMjLy0vp6em3zFmtwOHLiroFFCP7X4ku6hYAALilnTt3Tvv27VNQUJDc3NyKuh3cYFd7vQuTDQoVsm43hCwUd4QsAACujpB1e7EqZBXq64IAAAAAgKsjZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAFzD7fDbTrg4O7kVSliyFwAAAKAYcnFxkZOTkw4dOqQKFSrIxcVFNputqNvCDWAYho4dOyabzaaSJUv+rX0RsgAAAIArcHJyUlBQkA4fPqxDhw4VdTu4wWw2mypVqiRnZ+e/tR9CFgAAAHAVLi4uqly5si5cuKDs7Oyibgc3UMmSJf92wJIIWQAAAMA15X6F7O9+jQy3Bya+AAAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAAC5Uo6gYAALidBA5fVtQtoJjZ/0p0UbcA4DKcyQIAAAAAC3EmCwAAAJbijC2s9E88W8uZLAAAAACwUKFCVmBgoGw2W55bXFycJOncuXOKi4tTuXLl5OnpqZiYGB05csRhHwcOHFB0dLRKlSolHx8fDR06VBcuXHCoWbt2rRo1aiRXV1dVr15dSUlJeXqZOXOmAgMD5ebmprCwMG3ZssVhfUF6AQAAAACrFSpkbd26VYcPHzZvK1eulCR17dpVkjR48GB99tlnWrx4sdatW6dDhw7pwQcfNLfPzs5WdHS0srKytHHjRs2dO1dJSUkaNWqUWbNv3z5FR0crIiJCO3bs0KBBg9SvXz+tWLHCrFm4cKESEhI0evRobd++XfXr11dUVJSOHj1q1lyrFwAAAAC4EWyGYRjXu/GgQYO0dOlS7d27VxkZGapQoYIWLFigLl26SJJ2796tOnXqKDk5WU2bNtUXX3yhjh076tChQ/L19ZUkJSYm6rnnntOxY8fk4uKi5557TsuWLdMPP/xgHqd79+5KS0vT8uXLJUlhYWG66667NGPGDElSTk6OAgICNHDgQA0fPlzp6enX7KUgMjIy5OXlpfT0dNnt9ut9mizFd5xhpX/id5yBfzrex2G1W/G9nHEOK90qY7ww2eC6r8nKysrSe++9pz59+shmsyklJUXnz59XZGSkWVO7dm1VrlxZycnJkqTk5GSFhISYAUuSoqKilJGRoV27dpk1l+4jtyZ3H1lZWUpJSXGocXJyUmRkpFlTkF7yk5mZqYyMDIcbAAAAABTGdYesJUuWKC0tTY899pgkKTU1VS4uLvL29nao8/X1VWpqqllzacDKXZ+77mo1GRkZOnv2rP78809lZ2fnW3PpPq7VS37Gjx8vLy8v8xYQEHDtJwIAAAAALnHdIeudd95Rhw4d5O/vb2U/RWrEiBFKT083bwcPHizqlgAAAAD8w1zX72T9/vvv+uqrr/TRRx+Zy/z8/JSVlaW0tDSHM0hHjhyRn5+fWXP5LIC5M/5dWnP5LIBHjhyR3W6Xu7u7nJ2d5ezsnG/Npfu4Vi/5cXV1laurawGfBQAAAADI67rOZM2ZM0c+Pj6Kjv7fRWihoaEqWbKkVq1aZS7bs2ePDhw4oPDwcElSeHi4du7c6TAL4MqVK2W32xUcHGzWXLqP3Jrcfbi4uCg0NNShJicnR6tWrTJrCtILAAAAANwIhT6TlZOTozlz5ig2NlYlSvxvcy8vL/Xt21cJCQkqW7as7Ha7Bg4cqPDwcHM2v3bt2ik4OFi9evXSxIkTlZqaqpEjRyouLs48g/Tkk09qxowZGjZsmPr06aPVq1dr0aJFWrbsf7PUJCQkKDY2Vo0bN1aTJk00depUnTlzRr179y5wLwAAAABwIxQ6ZH311Vc6cOCA+vTpk2fdlClT5OTkpJiYGGVmZioqKkqzZs0y1zs7O2vp0qUaMGCAwsPD5eHhodjYWI0dO9asCQoK0rJlyzR48GBNmzZNlSpV0ttvv62oqCizplu3bjp27JhGjRql1NRUNWjQQMuXL3eYDONavQAAAADAjfC3fieruON3slDc3Sq/OwHcTngfh9VuxfdyxjmsdKuM8ZvyO1kAAAAAgLwIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYKFCh6w//vhDjzzyiMqVKyd3d3eFhIRo27Zt5nrDMDRq1ChVrFhR7u7uioyM1N69ex32ceLECfXs2VN2u13e3t7q27evTp8+7VDz/fffq2XLlnJzc1NAQIAmTpyYp5fFixerdu3acnNzU0hIiD7//HOH9QXpBQAAAACsVKiQdfLkSTVv3lwlS5bUF198oR9//FGTJk1SmTJlzJqJEydq+vTpSkxM1ObNm+Xh4aGoqCidO3fOrOnZs6d27dqllStXaunSpfr666/1+OOPm+szMjLUrl07ValSRSkpKXr11Vc1ZswYvfnmm2bNxo0b1aNHD/Xt21fffvutOnfurM6dO+uHH34oVC8AAAAAYCWbYRhGQYuHDx+uDRs2aP369fmuNwxD/v7+evbZZzVkyBBJUnp6unx9fZWUlKTu3bvrp59+UnBwsLZu3arGjRtLkpYvX657771X//3vf+Xv76/Zs2fr3//+t1JTU+Xi4mIee8mSJdq9e7ckqVu3bjpz5oyWLl1qHr9p06Zq0KCBEhMTC9TLtWRkZMjLy0vp6emy2+0FfZpuqMDhy4q6BRQj+1+JLuoWgNsO7+Ow2q34Xs44h5VulTFemGxQqDNZn376qRo3bqyuXbvKx8dHDRs21FtvvWWu37dvn1JTUxUZGWku8/LyUlhYmJKTkyVJycnJ8vb2NgOWJEVGRsrJyUmbN282a1q1amUGLEmKiorSnj17dPLkSbPm0uPk1uQepyC9XC4zM1MZGRkONwAAAAAojEKFrN9++02zZ89WjRo1tGLFCg0YMEBPP/205s6dK0lKTU2VJPn6+jps5+vra65LTU2Vj4+Pw/oSJUqobNmyDjX57ePSY1yp5tL11+rlcuPHj5eXl5d5CwgIuNZTAgAAAAAOChWycnJy1KhRI7388stq2LChHn/8cfXv31+JiYk3qr+basSIEUpPTzdvBw8eLOqWAAAAAPzDFCpkVaxYUcHBwQ7L6tSpowMHDkiS/Pz8JElHjhxxqDly5Ii5zs/PT0ePHnVYf+HCBZ04ccKhJr99XHqMK9Vcuv5avVzO1dVVdrvd4QYAAAAAhVGokNW8eXPt2bPHYdnPP/+sKlWqSJKCgoLk5+enVatWmeszMjK0efNmhYeHS5LCw8OVlpamlJQUs2b16tXKyclRWFiYWfP111/r/PnzZs3KlStVq1YtcybD8PBwh+Pk1uQepyC9AAAAAIDVChWyBg8erE2bNunll1/WL7/8ogULFujNN99UXFycJMlms2nQoEEaN26cPv30U+3cuVOPPvqo/P391blzZ0kXz3y1b99e/fv315YtW7RhwwbFx8ere/fu8vf3lyQ9/PDDcnFxUd++fbVr1y4tXLhQ06ZNU0JCgtnLM888o+XLl2vSpEnavXu3xowZo23btik+Pr7AvQAAAACA1UoUpviuu+7Sxx9/rBEjRmjs2LEKCgrS1KlT1bNnT7Nm2LBhOnPmjB5//HGlpaWpRYsWWr58udzc3Mya+fPnKz4+XnfffbecnJwUExOj6dOnm+u9vLz05ZdfKi4uTqGhoSpfvrxGjRrl8FtazZo104IFCzRy5Ej961//Uo0aNbRkyRLVrVu3UL0AAAAAgJUK9TtZtxt+JwvF3a3yuxPA7YT3cVjtVnwvZ5zDSrfKGL9hv5MFAAAAALg6QhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgoUKFrDFjxshmszncateuba4/d+6c4uLiVK5cOXl6eiomJkZHjhxx2MeBAwcUHR2tUqVKycfHR0OHDtWFCxccatauXatGjRrJ1dVV1atXV1JSUp5eZs6cqcDAQLm5uSksLExbtmxxWF+QXgAAAADAaoU+k3XnnXfq8OHD5u2bb74x1w0ePFifffaZFi9erHXr1unQoUN68MEHzfXZ2dmKjo5WVlaWNm7cqLlz5yopKUmjRo0ya/bt26fo6GhFRERox44dGjRokPr166cVK1aYNQsXLlRCQoJGjx6t7du3q379+oqKitLRo0cL3AsAAAAA3AiFDlklSpSQn5+feStfvrwkKT09Xe+8844mT56stm3bKjQ0VHPmzNHGjRu1adMmSdKXX36pH3/8Ue+9954aNGigDh066MUXX9TMmTOVlZUlSUpMTFRQUJAmTZqkOnXqKD4+Xl26dNGUKVPMHiZPnqz+/furd+/eCg4OVmJiokqVKqV33323wL0AAAAAwI1Q6JC1d+9e+fv7q2rVqurZs6cOHDggSUpJSdH58+cVGRlp1tauXVuVK1dWcnKyJCk5OVkhISHy9fU1a6KiopSRkaFdu3aZNZfuI7cmdx9ZWVlKSUlxqHFyclJkZKRZU5BeAAAAAOBGKFGY4rCwMCUlJalWrVo6fPiwXnjhBbVs2VI//PCDUlNT5eLiIm9vb4dtfH19lZqaKklKTU11CFi563PXXa0mIyNDZ8+e1cmTJ5WdnZ1vze7du819XKuX/GRmZiozM9O8n5GRcY1nBAAAAAAcFSpkdejQwfzvevXqKSwsTFWqVNGiRYvk7u5ueXM32/jx4/XCCy8UdRsAAAAA/sH+1hTu3t7eqlmzpn755Rf5+fkpKytLaWlpDjVHjhyRn5+fJMnPzy/PDH+5969VY7fb5e7urvLly8vZ2Tnfmkv3ca1e8jNixAilp6ebt4MHDxbsiQAAAACA/+9vhazTp0/r119/VcWKFRUaGqqSJUtq1apV5vo9e/bowIEDCg8PlySFh4dr586dDrMArly5Una7XcHBwWbNpfvIrcndh4uLi0JDQx1qcnJytGrVKrOmIL3kx9XVVXa73eEGAAAAAIVRqK8LDhkyRPfdd5+qVKmiQ4cOafTo0XJ2dlaPHj3k5eWlvn37KiEhQWXLlpXdbtfAgQMVHh6upk2bSpLatWun4OBg9erVSxMnTlRqaqpGjhypuLg4ubq6SpKefPJJzZgxQ8OGDVOfPn20evVqLVq0SMuWLTP7SEhIUGxsrBo3bqwmTZpo6tSpOnPmjHr37i1JBeoFAAAAAG6EQoWs//73v+rRo4eOHz+uChUqqEWLFtq0aZMqVKggSZoyZYqcnJwUExOjzMxMRUVFadasWeb2zs7OWrp0qQYMGKDw8HB5eHgoNjZWY8eONWuCgoK0bNkyDR48WNOmTVOlSpX09ttvKyoqyqzp1q2bjh07plGjRik1NVUNGjTQ8uXLHSbDuFYvAAAAAHAj2AzDMIq6iVtVRkaGvLy8lJ6efst8dTBw+LJrFwEFtP+V6KJuAbjt8D4Oq92K7+WMc1jpVhnjhckGf+uaLAAAAACAI0IWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYKESRd0AAFwqcPiyom4Bxcj+V6KLugUAwG3ob53JeuWVV2Sz2TRo0CBz2blz5xQXF6dy5crJ09NTMTExOnLkiMN2Bw4cUHR0tEqVKiUfHx8NHTpUFy5ccKhZu3atGjVqJFdXV1WvXl1JSUl5jj9z5kwFBgbKzc1NYWFh2rJli8P6gvQCAAAAAFa67pC1detWvfHGG6pXr57D8sGDB+uzzz7T4sWLtW7dOh06dEgPPviguT47O1vR0dHKysrSxo0bNXfuXCUlJWnUqFFmzb59+xQdHa2IiAjt2LFDgwYNUr9+/bRixQqzZuHChUpISNDo0aO1fft21a9fX1FRUTp69GiBewEAAAAAq11XyDp9+rR69uypt956S2XKlDGXp6en65133tHkyZPVtm1bhYaGas6cOdq4caM2bdokSfryyy/1448/6r333lODBg3UoUMHvfjii5o5c6aysrIkSYmJiQoKCtKkSZNUp04dxcfHq0uXLpoyZYp5rMmTJ6t///7q3bu3goODlZiYqFKlSundd98tcC8AAAAAYLXrCllxcXGKjo5WZGSkw/KUlBSdP3/eYXnt2rVVuXJlJScnS5KSk5MVEhIiX19fsyYqKkoZGRnatWuXWXP5vqOiosx9ZGVlKSUlxaHGyclJkZGRZk1BegEAAAAAqxV64ov3339f27dv19atW/OsS01NlYuLi7y9vR2W+/r6KjU11ay5NGDlrs9dd7WajIwMnT17VidPnlR2dna+Nbt37y5wL5fLzMxUZmameT8jIyPfOgAAAAC4kkKdyTp48KCeeeYZzZ8/X25ubjeqpyIzfvx4eXl5mbeAgICibgkAAADAP0yhQlZKSoqOHj2qRo0aqUSJEipRooTWrVun6dOnq0SJEvL19VVWVpbS0tIctjty5Ij8/PwkSX5+fnlm+Mu9f60au90ud3d3lS9fXs7OzvnWXLqPa/VyuREjRig9Pd28HTx4sOBPDgAAAACokCHr7rvv1s6dO7Vjxw7z1rhxY/Xs2dP875IlS2rVqlXmNnv27NGBAwcUHh4uSQoPD9fOnTsdZgFcuXKl7Ha7goODzZpL95Fbk7sPFxcXhYaGOtTk5ORo1apVZk1oaOg1e7mcq6ur7Ha7ww0AAAAACqNQ12SVLl1adevWdVjm4eGhcuXKmcv79u2rhIQElS1bVna7XQMHDlR4eLiaNm0qSWrXrp2Cg4PVq1cvTZw4UampqRo5cqTi4uLk6uoqSXryySc1Y8YMDRs2TH369NHq1au1aNEiLVv2vx8pTUhIUGxsrBo3bqwmTZpo6tSpOnPmjHr37i1J8vLyumYvAAAAAGC1Qk98cS1TpkyRk5OTYmJilJmZqaioKM2aNctc7+zsrKVLl2rAgAEKDw+Xh4eHYmNjNXbsWLMmKChIy5Yt0+DBgzVt2jRVqlRJb7/9tqKiosyabt266dixYxo1apRSU1PVoEEDLV++3GEyjGv1AgAAAABWsxmGYRR1E7eqjIwMeXl5KT09/Zb56mDg8GXXLgIKaP8r0UXdQh6McViJMY7bAeMcxd2tMsYLkw2u63eyAAAAAAD5I2QBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFChWyZs+erXr16slut8tutys8PFxffPGFuf7cuXOKi4tTuXLl5OnpqZiYGB05csRhHwcOHFB0dLRKlSolHx8fDR06VBcuXHCoWbt2rRo1aiRXV1dVr15dSUlJeXqZOXOmAgMD5ebmprCwMG3ZssVhfUF6AQAAAACrFSpkVapUSa+88opSUlK0bds2tW3bVp06ddKuXbskSYMHD9Znn32mxYsXa926dTp06JAefPBBc/vs7GxFR0crKytLGzdu1Ny5c5WUlKRRo0aZNfv27VN0dLQiIiK0Y8cODRo0SP369dOKFSvMmoULFyohIUGjR4/W9u3bVb9+fUVFReno0aNmzbV6AQAAAIAbwWYYhvF3dlC2bFm9+uqr6tKliypUqKAFCxaoS5cukqTdu3erTp06Sk5OVtOmTfXFF1+oY8eOOnTokHx9fSVJiYmJeu6553Ts2DG5uLjoueee07Jly/TDDz+Yx+jevbvS0tK0fPlySVJYWJjuuusuzZgxQ5KUk5OjgIAADRw4UMOHD1d6evo1eymIjIwMeXl5KT09XXa7/e88TZYJHL6sqFtAMbL/leiibiEPxjisxBjH7YBxjuLuVhnjhckG131NVnZ2tt5//32dOXNG4eHhSklJ0fnz5xUZGWnW1K5dW5UrV1ZycrIkKTk5WSEhIWbAkqSoqChlZGSYZ8OSk5Md9pFbk7uPrKwspaSkONQ4OTkpMjLSrClIL/nJzMxURkaGww0AAAAACqPQIWvnzp3y9PSUq6urnnzySX388ccKDg5WamqqXFxc5O3t7VDv6+ur1NRUSVJqaqpDwMpdn7vuajUZGRk6e/as/vzzT2VnZ+dbc+k+rtVLfsaPHy8vLy/zFhAQULAnBQAAAAD+v0KHrFq1amnHjh3avHmzBgwYoNjYWP344483orebbsSIEUpPTzdvBw8eLOqWAAAAAPzDlCjsBi4uLqpevbokKTQ0VFu3btW0adPUrVs3ZWVlKS0tzeEM0pEjR+Tn5ydJ8vPzyzMLYO6Mf5fWXD4L4JEjR2S32+Xu7i5nZ2c5OzvnW3PpPq7VS35cXV3l6upaiGcDAAAAABz97d/JysnJUWZmpkJDQ1WyZEmtWrXKXLdnzx4dOHBA4eHhkqTw8HDt3LnTYRbAlStXym63Kzg42Ky5dB+5Nbn7cHFxUWhoqENNTk6OVq1aZdYUpBcAAAAAuBEKdSZrxIgR6tChgypXrqxTp05pwYIFWrt2rVasWCEvLy/17dtXCQkJKlu2rOx2uwYOHKjw8HBzNr927dopODhYvXr10sSJE5WamqqRI0cqLi7OPIP05JNPasaMGRo2bJj69Omj1atXa9GiRVq27H+z1CQkJCg2NlaNGzdWkyZNNHXqVJ05c0a9e/eWpAL1AgAAAAA3QqFC1tGjR/Xoo4/q8OHD8vLyUr169bRixQrdc889kqQpU6bIyclJMTExyszMVFRUlGbNmmVu7+zsrKVLl2rAgAEKDw+Xh4eHYmNjNXbsWLMmKChIy5Yt0+DBgzVt2jRVqlRJb7/9tqKiosyabt266dixYxo1apRSU1PVoEEDLV++3GEyjGv1AgAAAAA3wt/+nazijN/JQnF3q/zuxKUY47ASYxy3A8Y5irtbZYzflN/JAgAAAADkRcgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwUImibgCFs9/t4aJuAcVKelE3AAAAUOxwJgsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACwECELAAAAACxEyAIAAAAACxGyAAAAAMBChCwAAAAAsFChQtb48eN11113qXTp0vLx8VHnzp21Z88eh5pz584pLi5O5cqVk6enp2JiYnTkyBGHmgMHDig6OlqlSpWSj4+Phg4dqgsXLjjUrF27Vo0aNZKrq6uqV6+upKSkPP3MnDlTgYGBcnNzU1hYmLZs2VLoXgAAAADASoUKWevWrVNcXJw2bdqklStX6vz582rXrp3OnDlj1gwePFifffaZFi9erHXr1unQoUN68MEHzfXZ2dmKjo5WVlaWNm7cqLlz5yopKUmjRo0ya/bt26fo6GhFRERox44dGjRokPr166cVK1aYNQsXLlRCQoJGjx6t7du3q379+oqKitLRo0cL3AsAAAAAWM1mGIZxvRsfO3ZMPj4+WrdunVq1aqX09HRVqFBBCxYsUJcuXSRJu3fvVp06dZScnKymTZvqiy++UMeOHXXo0CH5+vpKkhITE/Xcc8/p2LFjcnFx0XPPPadly5bphx9+MI/VvXt3paWlafny5ZKksLAw3XXXXZoxY4YkKScnRwEBARo4cKCGDx9eoF6uJSMjQ15eXkpPT5fdbr/ep8laY7yKugMUJ2PSi7qDPAKHLyvqFlCM7H8luqhbyIMxDqsxzlHc3SpjvDDZoMTfOVB6+sUPaGXLlpUkpaSk6Pz584qMjDRrateurcqVK5vBJjk5WSEhIWbAkqSoqCgNGDBAu3btUsOGDZWcnOywj9yaQYMGSZKysrKUkpKiESNGmOudnJwUGRmp5OTkAvcCAMDNtt/t4aJuAcXOrfcHM+B2d90hKycnR4MGDVLz5s1Vt25dSVJqaqpcXFzk7e3tUOvr66vU1FSz5tKAlbs+d93VajIyMnT27FmdPHlS2dnZ+dbs3r27wL1cLjMzU5mZmeb9jIyMaz0NAAAAAODgumcXjIuL0w8//KD333/fyn6K1Pjx4+Xl5WXeAgICirolAAAAAP8w1xWy4uPjtXTpUq1Zs0aVKlUyl/v5+SkrK0tpaWkO9UeOHJGfn59Zc/kMf7n3r1Vjt9vl7u6u8uXLy9nZOd+aS/dxrV4uN2LECKWnp5u3gwcPFuDZAAAAAID/KVTIMgxD8fHx+vjjj7V69WoFBQU5rA8NDVXJkiW1atUqc9mePXt04MABhYeHS5LCw8O1c+dOh1kAV65cKbvdruDgYLPm0n3k1uTuw8XFRaGhoQ41OTk5WrVqlVlTkF4u5+rqKrvd7nADAAAAgMIo1DVZcXFxWrBggT755BOVLl3avLbJy8tL7u7u8vLyUt++fZWQkKCyZcvKbrdr4MCBCg8PNyeaaNeunYKDg9WrVy9NnDhRqampGjlypOLi4uTq6ipJevLJJzVjxgwNGzZMffr00erVq7Vo0SItW/a/mWoSEhIUGxurxo0bq0mTJpo6darOnDmj3r17mz1dqxcAAAAAsFqhQtbs2bMlSW3atHFYPmfOHD322GOSpClTpsjJyUkxMTHKzMxUVFSUZs2aZdY6Oztr6dKlGjBggMLDw+Xh4aHY2FiNHTvWrAkKCtKyZcs0ePBgTZs2TZUqVdLbb7+tqKgos6Zbt246duyYRo0apdTUVDVo0EDLly93mAzjWr0AAAAAgNX+1u9kFXf8ThaKPX4nC8XcrfLbKg54H4fVeC9HMXervJcXJhtc9+yCAAAAAIC8CFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWKhEUTcAAACA4mW/28NF3QKKlfSibqDQOJMFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCh2yvv76a913333y9/eXzWbTkiVLHNYbhqFRo0apYsWKcnd3V2RkpPbu3etQc+LECfXs2VN2u13e3t7q27evTp8+7VDz/fffq2XLlnJzc1NAQIAmTpyYp5fFixerdu3acnNzU0hIiD7//PNC9wIAAAAAVip0yDpz5ozq16+vmTNn5rt+4sSJmj59uhITE7V582Z5eHgoKipK586dM2t69uypXbt2aeXKlVq6dKm+/vprPf744+b6jIwMtWvXTlWqVFFKSopeffVVjRkzRm+++aZZs3HjRvXo0UN9+/bVt99+q86dO6tz58764YcfCtULAAAAAFjJZhiGcd0b22z6+OOP1blzZ0kXzxz5+/vr2Wef1ZAhQyRJ6enp8vX1VVJSkrp3766ffvpJwcHB2rp1qxo3bixJWr58ue69917997//lb+/v2bPnq1///vfSk1NlYuLiyRp+PDhWrJkiXbv3i1J6tatm86cOaOlS5ea/TRt2lQNGjRQYmJigXq5loyMDHl5eSk9PV12u/16nyZrjfEq6g5QnIxJL+oO8ggcvqyoW0Axsv+V6KJuIS/ex2G1W/C9nHEOS90iY7ww2cDSa7L27dun1NRURUZGmsu8vLwUFham5ORkSVJycrK8vb3NgCVJkZGRcnJy0ubNm82aVq1amQFLkqKiorRnzx6dPHnSrLn0OLk1uccpSC8AAAAAYLUSVu4sNTVVkuTr6+uw3NfX11yXmpoqHx8fxyZKlFDZsmUdaoKCgvLsI3ddmTJllJqaes3jXKuXy2VmZiozM9O8n5GRcY1HDAAAAACOmF3wEuPHj5eXl5d5CwgIKOqWAAAAAPzDWBqy/Pz8JElHjhxxWH7kyBFznZ+fn44ePeqw/sKFCzpx4oRDTX77uPQYV6q5dP21ernciBEjlJ6ebt4OHjxYgEcNAAAAAP9jacgKCgqSn5+fVq1aZS7LyMjQ5s2bFR4eLkkKDw9XWlqaUlJSzJrVq1crJydHYWFhZs3XX3+t8+fPmzUrV65UrVq1VKZMGbPm0uPk1uQepyC9XM7V1VV2u93hBgAAAACFUeiQdfr0ae3YsUM7duyQdHGCiR07dujAgQOy2WwaNGiQxo0bp08//VQ7d+7Uo48+Kn9/f3MGwjp16qh9+/bq37+/tmzZog0bNig+Pl7du3eXv7+/JOnhhx+Wi4uL+vbtq127dmnhwoWaNm2aEhISzD6eeeYZLV++XJMmTdLu3bs1ZswYbdu2TfHx8ZJUoF4AAAAAwGqFnvhi27ZtioiIMO/nBp/Y2FglJSVp2LBhOnPmjB5//HGlpaWpRYsWWr58udzc3Mxt5s+fr/j4eN19991ycnJSTEyMpk+fbq738vLSl19+qbi4OIWGhqp8+fIaNWqUw29pNWvWTAsWLNDIkSP1r3/9SzVq1NCSJUtUt25ds6YgvQAAAACAlf7W72QVd/xOFoq9W+R3Jy7F72TBSvxOFm4Lt+B7OeMclrpFxniR/U4WAAAAANzuCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYKESRd0AAFxqv9vDRd0CipX0om4AAHAb4kwWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFCFkAAAAAYCFCFgAAAABYiJAFAAAAABYiZAEAAACAhQhZAAAAAGAhQhYAAAAAWIiQBQAAAAAWImQBAAAAgIUIWQAAAABgIUIWAAAAAFiIkAUAAAAAFiJkAQAAAICFbouQNXPmTAUGBsrNzU1hYWHasmVLUbcEAAAAoJgq9iFr4cKFSkhI0OjRo7V9+3bVr19fUVFROnr0aFG3BgAAAKAYKvYha/Lkyerfv7969+6t4OBgJSYmqlSpUnr33XeLujUAAAAAxVCJom7gRsrKylJKSopGjBhhLnNyclJkZKSSk5Pz1GdmZiozM9O8n56eLknKyMi48c0WVKZR1B2gOLmVxnYuxjisxBjH7YBxjuLuFhnjuZnAMK49vot1yPrzzz+VnZ0tX19fh+W+vr7avXt3nvrx48frhRdeyLM8ICDghvUIFKlXvIq6A+DGYozjdsA4R3F3i43xU6dOycvr6j0V65BVWCNGjFBCQoJ5PycnRydOnFC5cuVks9mKsDMURkZGhgICAnTw4EHZ7faibgewHGMctwPGOYo7xvg/j2EYOnXqlPz9/a9ZW6xDVvny5eXs7KwjR444LD9y5Ij8/Pzy1Lu6usrV1dVhmbe3941sETeQ3W7nTQvFGmMctwPGOYo7xvg/y7XOYOUq1hNfuLi4KDQ0VKtWrTKX5eTkaNWqVQoPDy/CzgAAAAAUV8X6TJYkJSQkKDY2Vo0bN1aTJk00depUnTlzRr179y7q1gAAAAAUQ8U+ZHXr1k3Hjh3TqFGjlJqaqgYNGmj58uV5JsNA8eHq6qrRo0fn+eonUFwwxnE7YJyjuGOMF282oyBzEAIAAAAACqRYX5MFAAAAADcbIQsAAAAALETIAgAAAAALEbJwW0hKSnL4zbMxY8aoQYMGRdYPYJXLxzYAoOitXbtWNptNaWlpRd0KigghC/8ojz32mGw2W57bL7/8UtStAVd17NgxDRgwQJUrV5arq6v8/PwUFRWlDRs2/K39duvWTT///LNFXQJXd6PGMXArS0xMVOnSpXXhwgVz2enTp1WyZEm1adPGoTY3XFWsWFGHDx8u8A/XShc/43Tu3NmirlHUiv0U7ih+2rdvrzlz5jgsq1ChQhF1AxRMTEyMsrKyNHfuXFWtWlVHjhzRqlWrdPz48eve5/nz5+Xu7i53d3cLOwWu7EaM41tJVlaWXFxciroN3GIiIiJ0+vRpbdu2TU2bNpUkrV+/Xn5+ftq8ebPOnTsnNzc3SdKaNWtUuXJl1apVq8j6ZRzfGjiThX+c3L+eXnqbNm2aQkJC5OHhoYCAAD311FM6ffp0UbcKSJLS0tK0fv16TZgwQREREapSpYqaNGmiESNG6P7775ck2Ww2zZ49Wx06dJC7u7uqVq2qDz74wNzH/v37ZbPZtHDhQrVu3Vpubm6aP3/+Fb8KO2/ePAUGBsrLy0vdu3fXqVOnzJpTp06pZ8+e8vDwUMWKFTVlyhS1adNGgwYNullPCf6BrjWOc8fojh07HLax2Wxau3atpP/9lX/FihVq2LCh3N3d1bZtWx09elRffPGF6tSpI7vdrocfflh//fWXuZ82bdpo4MCBGjRokMqUKSNfX1+99dZbOnPmjHr37q3SpUurevXq+uKLLxx6/uGHH9ShQwd5enrK19dXvXr10p9//umw3/j4eA0aNEjly5dXVFTUDX0O8c9Uq1YtVaxY0RzH0sWx3KlTJwUFBWnTpk0OyyMiIvJ8XTD3vXrFihWqU6eOPD091b59ex0+fFjSxffuuXPn6pNPPjG/pZN7vIMHD+qhhx6St7e3ypYtq06dOmn//v3mMXPPgL300kvy9/cv0oCH/yFkoVhwcnLS9OnTtWvXLs2dO1erV6/WsGHDirotQJLk6ekpT09PLVmyRJmZmVese/755xUTE6PvvvtOPXv2VPfu3fXTTz851AwfPlzPPPOMfvrppyt+IPz111+1ZMkSLV26VEuXLtW6dev0yiuvmOsTEhK0YcMGffrpp1q5cqXWr1+v7du3W/NgUWwVdBwXxJgxYzRjxgxt3LjR/AA5depULViwQMuWLdOXX36p119/3WGbuXPnqnz58tqyZYsGDhyoAQMGqGvXrmrWrJm2b9+udu3aqVevXmY4S0tLU9u2bdWwYUNt27ZNy5cv15EjR/TQQw/l2a+Li4s2bNigxMTEv/W4UHxFRERozZo15v01a9aoTZs2at26tbn87Nmz2rx5syIiIvLdx19//aXXXntN8+bN09dff60DBw5oyJAhkqQhQ4booYceMoPX4cOH1axZM50/f15RUVEqXbq01q9frw0bNpgBLSsry9z3qlWrtGfPHq1cuVJLly69gc8ECswA/kFiY2MNZ2dnw8PDw7x16dIlT93ixYuNcuXKmffnzJljeHl5mfdHjx5t1K9f/yZ0DFz0wQcfGGXKlDHc3NyMZs2aGSNGjDC+++47c70k48knn3TYJiwszBgwYIBhGIaxb98+Q5IxdepUh5r8xnapUqWMjIwMc9nQoUONsLAwwzAMIyMjwyhZsqSxePFic31aWppRqlQp45lnnrHq4aKYuto4zh2j3377rVl/8uRJQ5KxZs0awzAMY82aNYYk46uvvjJrxo8fb0gyfv31V3PZE088YURFRZn3W7dubbRo0cK8f+HCBcPDw8Po1auXuezw4cOGJCM5OdkwDMN48cUXjXbt2jn0f/DgQUOSsWfPHnO/DRs2/JvPCm4Hb731luHh4WGcP3/eyMjIMEqUKGEcPXrUWLBggdGqVSvDMAxj1apVhiTj999/N8f6yZMnDcO4+F4tyfjll1/Mfc6cOdPw9fU178fGxhqdOnVyOO68efOMWrVqGTk5OeayzMxMw93d3VixYoW5na+vr5GZmXmDHj2uB2ey8I8TERGhHTt2mLfp06frq6++0t1336077rhDpUuXVq9evXT8+HGHr5sARSkmJkaHDh3Sp59+qvbt22vt2rVq1KiRkpKSzJrw8HCHbcLDw/OcyWrcuPE1jxUYGKjSpUub9ytWrKijR49Kkn777TedP39eTZo0Mdd7eXnx9RIUSEHGcUHUq1fP/G9fX1+VKlVKVatWdViWO2bz28bZ2VnlypVTSEiIwzaSzO2+++47rVmzxjwD5+npqdq1a0u6eLY3V2hoaKF6x+2pTZs2OnPmjLZu3ar169erZs2aqlChglq3bm1el7V27VpVrVpVlStXzncfpUqVUrVq1cz7l743X8l3332nX375RaVLlzbHcdmyZXXu3DmHcRwSEsJ1WLcYJr7AP46Hh4eqV69u3t+/f786duyoAQMG6KWXXlLZsmX1zTffqG/fvsrKylKpUqWKsFvgf9zc3HTPPffonnvu0fPPP69+/fpp9OjReuyxxwq8Dw8Pj2vWlCxZ0uG+zWZTTk5OYdsF8nWlcbx+/XpJkmEYZu358+fz3celY9RmsxVozOZXc/l+JJnbnT59Wvfdd58mTJiQ5/gVK1Y0/7sg/6aA6tWrq1KlSlqzZo1Onjyp1q1bS5L8/f0VEBCgjRs3as2aNWrbtu0V95HfGL7030t+Tp8+rdDQUM2fPz/Puksn/WIc33o4k4V/vJSUFOXk5GjSpElq2rSpatasqUOHDhV1W8A1BQcH68yZM+b9Sy+ezr1fp04dS49ZtWpVlSxZUlu3bjWXpaenMw08rlvuOM79wJd7Ib8kh0kwbrZGjRpp165dCgwMVPXq1R1ufCDF9cid0GLt2rUOU7e3atVKX3zxhbZs2XLF67EKwsXFRdnZ2Q7LGjVqpL1798rHxyfPOC7M9PC4+QhZ+MerXr26zp8/r9dff12//fab5s2bx8XLuKUcP35cbdu21Xvvvafvv/9e+/bt0+LFizVx4kR16tTJrFu8eLHeffdd/fzzzxo9erS2bNmi+Ph4S3spXbq0YmNjNXToUK1Zs0a7du1S37595eTkZJ4JAPJzrXHs7u6upk2b6pVXXtFPP/2kdevWaeTIkUXWb1xcnE6cOKEePXpo69at+vXXX7VixQr17t07zwdZoCAiIiL0zTffaMeOHeaZLElq3bq13njjDWVlZf2tkBUYGKjvv/9ee/bs0Z9//qnz58+rZ8+eKl++vDp16qT169dr3759Wrt2rZ5++mn997//teJh4QYhZOEfr379+po8ebImTJigunXrav78+Ro/fnxRtwWYPD09FRYWpilTpqhVq1aqW7eunn/+efXv318zZsww61544QW9//77qlevnv7zn//o//7v/xQcHGx5P5MnT1Z4eLg6duyoyMhINW/eXHXq1DF/5wXIT0HG8bvvvqsLFy4oNDRUgwYN0rhx44qsX39/f23YsEHZ2dlq166dQkJCNGjQIHl7e8vJiY8/KLyIiAidPXtW1atXN68BlC6GrFOnTplTvV+v/v37q1atWmrcuLEqVKigDRs2qFSpUvr6669VuXJlPfjgg6pTp4769u2rc+fOyW63W/GwcIPYjGt9GRQAcMPZbDZ9/PHH6ty5800/9pkzZ3THHXdo0qRJ6tu3700/PgAAxQ0TXwDAbebbb7/V7t271aRJE6Wnp2vs2LGS5PDVRQAAcP0IWQBwG3rttde0Z88eubi4KDQ0VOvXr1f58uWLui0AAIoFvi4IAAAAABbiyk8AAAAAsBAhCwAAAAAsRMgCAAAAAAsRsgAAAADAQoQsAAAAALAQIQsAAAAALETIAgAAAAALEbIAAAAAwEKELAAAAACw0P8DgOTmAwK0qlQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mengelompokkan data berdasarkan musim dan menghitung jumlah penggunaan terdaftar dan tidak terdaftar\n",
    "seasonal_usage = day_df.groupby('season')[['registered', 'casual']].sum().reset_index()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Membuat\n",
    "plt.bar(\n",
    "    seasonal_usage['season'],\n",
    "    seasonal_usage['registered'],\n",
    "    label='Registered',\n",
    "    color='tab:blue'\n",
    ")\n",
    "\n",
    "plt.bar(\n",
    "    seasonal_usage['season'],\n",
    "    seasonal_usage['casual'],\n",
    "    label='Casual',\n",
    "    color='tab:orange'\n",
    ")\n",
    "\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(None)\n",
    "plt.title('Jumlah penyewaan sepeda berdasarkan musim')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berdasarkan hasil analisis diatas menggunakan barplot, dapat disimpulkan bahwa di semua musim yang paling dinikmati bagi para pengguna sepeda (Casual dan Registered) adalah musim gugur (Fall), lalu diikuti dengan musim panas (Summer), musim dingin (Winter), dan diakhiri dengan musim semi (Spring)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Conclusion pertanyaan 1:\n",
    "Dapat terlihat jelas terdapat hubungan diantara keduanya. Para penyewa sepeda sangat menyukai ketika cuaca sedang Cerah/Sedikit mendung dengan Q3 diatas 6000 dan Q1 kurang dari 4000 serta mediannya hampir 5000. Lalu posisi kedua ditempati ketika cuaca sedang Berkabut/Mendung dengan Q3 diatas 5000 dan Q1 dibawah 3000 serta mediannya diatas 4000. Terakhir pengguna kurang menyukai ketika suasana sedang Sedikit bersalju/Hujan dengan Q3 diatas 2000 dan Q1 dibawah 1000 serta mediannya diatas 1000. Dan untuk cuaca ketika Badai Besar tidak ada datanya, karena memang tidak memungkinan untuk bersepeda ketika kondisi cuaca semacam itu.\n",
    "\n",
    "- conclusion pertanyaan 2:\n",
    "Kondisi yang tampak pada visualisasi ini terbagi menjadi tiga bagian. Untuk jumlah pengguna sepeda berdasarkan workingday, workingday lah yang mengungguli non-workingday walaupun hasilnya memang tidak terlalu signifikan, tetapi tetap ada perbedaan yang jelas walaupun keduanya sama-sama diatas 4000 jumlah pengguna. Lanjut berdasarkan holiday (hari libur), disini non-holidaylah yang menang dengan perbedaan yang cukup signifikan daripada yang holiday. Hal ini mungkin pengguna lebih menyukai sewa sepeda saat hari-hari biasa saja. Terakhir berdasarkan weekday, berdasarkan data diatas, Hari Jumat (Fri) lah yang menempati urutan pertama dengan mean 4690.288462 dan di urutan terakhir ditempati oleh Minggu (Sun): 4228.828571.\n",
    "\n",
    "- Conclusion pertanyaan 3:\n",
    "Pada visualisasi kali ini, dapat kita amati pada kedua tahun yakni 2011 dan 2012, terjadi perbedaan puncak dan dasar bulan pada kedua tahun tersebut. Puncak dari tahun 2011 berada pada bulan Juni dan dasarnya berada pada bulan Januari. Sedangkan Puncak dari tahun 2012 terdapat pada bulan September dan dasarnya sama yakni bulan Januari. Dan dari kedua visualisasi tersebut dapat kita amati bahwa jumlah total sepeda yang disewakan pada tahun 2012, seluruhnya lebih tinggi daripada pada tahun 2011.\n",
    "\n",
    "- conclusion pertanyaan 4:\n",
    "Dari 3 visualisasi yang ditampilkan, dapat disimpulkan bahwa temp dan atemp memiliki korelasi positif dengan count, sehingga jika temperatur (temp dan atemp) meningkat, maka pengguna sepeda juga cenderung meningkat. Terbalik dengan Humidity yang memiliki korelasi negatif dengan count walaupun sangat minim (tidak terlalu signifikan), tetapi tetap dapat menurunkan jumlah pengguna sepeda ketika humiditynya meningkat.\n",
    "\n",
    "- Conclusion pertanyaan 5:\n",
    "Pada visualisasi terakhir, ternyata hasil yang didapatkan cukup mengejutkan dimana musim dingin (Winter) tidak menjadi yang terakhir, melainkan yang terakhir adalah musin semi (Spring). Hal ini bisa diakibatkan oleh banyak faktor seperti kondisi cuaca, temperatur udara, hari kerja, hari libur, dan lain-lain. Sedangkan urutan pertamanya ditempati oleh musim gugur (Fall) yang mampu meraih penyewaan sepeda sebanyak lebih dari 800000."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}