032-supervised-learning-linear-regression-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7423caae-33ca-4cfe-bde8-d63d13bfaefe",
   "metadata": {},
   "source": [
    "# Лінійна регресія. Прокат велосипедів"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2414ff5",
   "metadata": {},
   "source": [
    "Курс: \"Математичне моделювання в R\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ced297",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "726e138b-accb-48a1-bef9-a919cf904358",
   "metadata": {},
   "source": [
    "::: {.callout-important}\n",
    "# Перевірити матеріали лекції!!! \n",
    "\n",
    "Розділ про прогноз з кроком 10-30. Втрачено окремі комірки коду.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28124ff7-5b2f-4ce9-b273-34fc3f33d7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(\"mice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607361bd-ec64-40da-9d45-497af39439f0",
   "metadata": {},
   "source": [
    "## Dataset overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3191a739-7cc1-4a25-a17c-08af969d46f5",
   "metadata": {},
   "source": [
    "Source: https://www.kaggle.com/c/bike-sharing-demand/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9603dea4-8656-4c05-9307-10c045f2ab29",
   "metadata": {},
   "source": [
    "**Dataset description:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bfbe51-8519-41e2-8087-c9f919e5a67b",
   "metadata": {},
   "source": [
    "You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a90aac-c2e2-4035-9498-491bc455d042",
   "metadata": {},
   "source": [
    "**Data Fields:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c03a47-25ce-4af1-b6fd-7291bcaa210c",
   "metadata": {},
   "source": [
    "- [x] `datetime` - hourly date + timestamp\n",
    "- [x] `season` - \n",
    "    - [x] 1 = spring\n",
    "    - [x] 2 = summer\n",
    "    - [x] 3 = fall\n",
    "    - [x] 4 = winter\n",
    "- [x] `holiday` - whether the day is considered a holiday\n",
    "- [x] `workingday` - whether the day is neither a weekend nor holiday\n",
    "- [x] `weather`\n",
    "    - [x] 1: Clear, Few clouds, Partly cloudy, Partly cloudy \n",
    "    - [x] 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \n",
    "    - [x] 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds \n",
    "    - [x] 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- [x] `temp` - temperature in Celsius\n",
    "- [x] `atemp` - “feels like” temperature in Celsius\n",
    "- [x] `humidity` - relative humidity\n",
    "- [x] `windspeed` - wind speed\n",
    "- [x] `casual` - number of non-registered user rentals initiated\n",
    "- [x] `registered` - number of registered user rentals initiated\n",
    "- [x] `count` - number of total rentals **(It is target variable. We will predict it!)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4518116b-4a81-49a5-807c-672c6a377a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>casual</th><th scope=col>registered</th><th scope=col>count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2011-01-01 00:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>81</td><td>0.0000</td><td>3</td><td>13</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2011-01-01 01:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>8</td><td>32</td><td>40</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2011-01-01 02:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>5</td><td>27</td><td>32</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2011-01-01 03:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>3</td><td>10</td><td>13</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2011-01-01 04:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>0</td><td> 1</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2011-01-01 05:00:00</td><td>1</td><td>0</td><td>0</td><td>2</td><td>9.84</td><td>12.880</td><td>75</td><td>6.0032</td><td>0</td><td> 1</td><td> 1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & casual & registered & count\\\\\n",
       "  & <chr> & <int> & <int> & <int> & <int> & <dbl> & <dbl> & <int> & <dbl> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 2011-01-01 00:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 81 & 0.0000 & 3 & 13 & 16\\\\\n",
       "\t2 & 2011-01-01 01:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 8 & 32 & 40\\\\\n",
       "\t3 & 2011-01-01 02:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 5 & 27 & 32\\\\\n",
       "\t4 & 2011-01-01 03:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 3 & 10 & 13\\\\\n",
       "\t5 & 2011-01-01 04:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 0 &  1 &  1\\\\\n",
       "\t6 & 2011-01-01 05:00:00 & 1 & 0 & 0 & 2 & 9.84 & 12.880 & 75 & 6.0032 & 0 &  1 &  1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 12\n",
       "\n",
       "| <!--/--> | datetime &lt;chr&gt; | season &lt;int&gt; | holiday &lt;int&gt; | workingday &lt;int&gt; | weather &lt;int&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | casual &lt;int&gt; | registered &lt;int&gt; | count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0000 | 3 | 13 | 16 |\n",
       "| 2 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 8 | 32 | 40 |\n",
       "| 3 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 5 | 27 | 32 |\n",
       "| 4 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 3 | 10 | 13 |\n",
       "| 5 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 0 |  1 |  1 |\n",
       "| 6 | 2011-01-01 05:00:00 | 1 | 0 | 0 | 2 | 9.84 | 12.880 | 75 | 6.0032 | 0 |  1 |  1 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp atemp  humidity\n",
       "1 2011-01-01 00:00:00 1      0       0          1       9.84 14.395 81      \n",
       "2 2011-01-01 01:00:00 1      0       0          1       9.02 13.635 80      \n",
       "3 2011-01-01 02:00:00 1      0       0          1       9.02 13.635 80      \n",
       "4 2011-01-01 03:00:00 1      0       0          1       9.84 14.395 75      \n",
       "5 2011-01-01 04:00:00 1      0       0          1       9.84 14.395 75      \n",
       "6 2011-01-01 05:00:00 1      0       0          2       9.84 12.880 75      \n",
       "  windspeed casual registered count\n",
       "1 0.0000    3      13         16   \n",
       "2 0.0000    8      32         40   \n",
       "3 0.0000    5      27         32   \n",
       "4 0.0000    3      10         13   \n",
       "5 0.0000    0       1          1   \n",
       "6 6.0032    0       1          1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read.csv(\"data/bikes.csv\")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2f92b7-b2a0-4c82-853d-7a962c659e55",
   "metadata": {},
   "source": [
    "Описова статистика факторів:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e626815a-ac97-4ae1-9be9-691d666b6ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   datetime             season         holiday          workingday    \n",
       " Length:10886       Min.   :1.000   Min.   :0.00000   Min.   :0.0000  \n",
       " Class :character   1st Qu.:2.000   1st Qu.:0.00000   1st Qu.:0.0000  \n",
       " Mode  :character   Median :3.000   Median :0.00000   Median :1.0000  \n",
       "                    Mean   :2.507   Mean   :0.02857   Mean   :0.6809  \n",
       "                    3rd Qu.:4.000   3rd Qu.:0.00000   3rd Qu.:1.0000  \n",
       "                    Max.   :4.000   Max.   :1.00000   Max.   :1.0000  \n",
       "    weather           temp           atemp          humidity     \n",
       " Min.   :1.000   Min.   : 0.82   Min.   : 0.76   Min.   :  0.00  \n",
       " 1st Qu.:1.000   1st Qu.:13.94   1st Qu.:16.66   1st Qu.: 47.00  \n",
       " Median :1.000   Median :20.50   Median :24.24   Median : 62.00  \n",
       " Mean   :1.418   Mean   :20.23   Mean   :23.66   Mean   : 61.89  \n",
       " 3rd Qu.:2.000   3rd Qu.:26.24   3rd Qu.:31.06   3rd Qu.: 77.00  \n",
       " Max.   :4.000   Max.   :41.00   Max.   :45.45   Max.   :100.00  \n",
       "   windspeed          casual         registered        count      \n",
       " Min.   : 0.000   Min.   :  0.00   Min.   :  0.0   Min.   :  1.0  \n",
       " 1st Qu.: 7.002   1st Qu.:  4.00   1st Qu.: 36.0   1st Qu.: 42.0  \n",
       " Median :12.998   Median : 17.00   Median :118.0   Median :145.0  \n",
       " Mean   :12.799   Mean   : 36.02   Mean   :155.6   Mean   :191.6  \n",
       " 3rd Qu.:16.998   3rd Qu.: 49.00   3rd Qu.:222.0   3rd Qu.:284.0  \n",
       " Max.   :56.997   Max.   :367.00   Max.   :886.0   Max.   :977.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb77ac4-98d8-456f-9789-be34b8e29aa4",
   "metadata": {},
   "source": [
    "## Minimal data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4aca2e2-41c7-463a-9404-7e00360f790f",
   "metadata": {},
   "source": [
    "Перевіримо вибірку на наявність пропусків за допомогою функції `md.pattern()` з пакету `mice`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9765e1a5-bb1f-4d5f-a69a-2cec631b4cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anyNA(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fefc1370-e66f-4475-b473-be25cdce4223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " /\\     /\\\n",
      "{  `---'  }\n",
      "{  O   O  }\n",
      "==>  V <==  No need for mice. This data set is completely observed.\n",
      " \\  \\|/  /\n",
      "  `-----'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>casual</th><th scope=col>registered</th><th scope=col>count</th><th scope=col></th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>10886</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row></th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & casual & registered & count & \\\\\n",
       "\\hline\n",
       "\t10886 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0\\\\\n",
       "\t & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | <!----> |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 10886 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |\n",
       "| <!----> | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "      datetime season holiday workingday weather temp atemp humidity windspeed\n",
       "10886 1        1      1       1          1       1    1     1        1        \n",
       "      0        0      0       0          0       0    0     0        0        \n",
       "      casual registered count  \n",
       "10886 1      1          1     0\n",
       "      0      0          0     0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(mice))\n",
    "md.pattern(data, F)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c24022a-2147-470b-b77c-81a2b2b7a250",
   "metadata": {},
   "source": [
    "There are no missing data! Great for us!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ecbb846-fdac-4503-a17a-a6b45f51eb26",
   "metadata": {},
   "source": [
    "Перетворимо категоріальні змінні до факторів:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57fa1dbc-a746-4091-9b5e-44924acfa487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>casual</th><th scope=col>registered</th><th scope=col>count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2011-01-01 00:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>81</td><td>0.0000</td><td>3</td><td>13</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2011-01-01 01:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>8</td><td>32</td><td>40</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2011-01-01 02:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>5</td><td>27</td><td>32</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2011-01-01 03:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>3</td><td>10</td><td>13</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2011-01-01 04:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>0</td><td> 1</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2011-01-01 05:00:00</td><td>1</td><td>0</td><td>0</td><td>2</td><td>9.84</td><td>12.880</td><td>75</td><td>6.0032</td><td>0</td><td> 1</td><td> 1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & casual & registered & count\\\\\n",
       "  & <chr> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 2011-01-01 00:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 81 & 0.0000 & 3 & 13 & 16\\\\\n",
       "\t2 & 2011-01-01 01:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 8 & 32 & 40\\\\\n",
       "\t3 & 2011-01-01 02:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 5 & 27 & 32\\\\\n",
       "\t4 & 2011-01-01 03:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 3 & 10 & 13\\\\\n",
       "\t5 & 2011-01-01 04:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 0 &  1 &  1\\\\\n",
       "\t6 & 2011-01-01 05:00:00 & 1 & 0 & 0 & 2 & 9.84 & 12.880 & 75 & 6.0032 & 0 &  1 &  1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 12\n",
       "\n",
       "| <!--/--> | datetime &lt;chr&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | casual &lt;int&gt; | registered &lt;int&gt; | count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0000 | 3 | 13 | 16 |\n",
       "| 2 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 8 | 32 | 40 |\n",
       "| 3 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 5 | 27 | 32 |\n",
       "| 4 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 3 | 10 | 13 |\n",
       "| 5 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 0 |  1 |  1 |\n",
       "| 6 | 2011-01-01 05:00:00 | 1 | 0 | 0 | 2 | 9.84 | 12.880 | 75 | 6.0032 | 0 |  1 |  1 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp atemp  humidity\n",
       "1 2011-01-01 00:00:00 1      0       0          1       9.84 14.395 81      \n",
       "2 2011-01-01 01:00:00 1      0       0          1       9.02 13.635 80      \n",
       "3 2011-01-01 02:00:00 1      0       0          1       9.02 13.635 80      \n",
       "4 2011-01-01 03:00:00 1      0       0          1       9.84 14.395 75      \n",
       "5 2011-01-01 04:00:00 1      0       0          1       9.84 14.395 75      \n",
       "6 2011-01-01 05:00:00 1      0       0          2       9.84 12.880 75      \n",
       "  windspeed casual registered count\n",
       "1 0.0000    3      13         16   \n",
       "2 0.0000    8      32         40   \n",
       "3 0.0000    5      27         32   \n",
       "4 0.0000    3      10         13   \n",
       "5 0.0000    0       1          1   \n",
       "6 6.0032    0       1          1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data$season <- factor(data$season)\n",
    "data$holiday <- factor(data$holiday)\n",
    "data$workingday <- factor(data$workingday)\n",
    "data$weather <- factor(data$weather)\n",
    "data$holiday <- factor(data$holiday)\n",
    "\n",
    "head(data) # preview data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a841d26-fe3d-4065-925b-55702690cd7a",
   "metadata": {},
   "source": [
    "У наборі даних наявні 3 вихідних параметра: `casual, `registered`, `count`, де $casual + registered = count$. Здійснимо прогноз `count`. Видалимо змінні, які використовуватися нами не будуть:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d99e67e-1aad-4892-bd14-40f3cc48e2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2011-01-01 00:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>81</td><td>0.0000</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2011-01-01 01:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>40</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2011-01-01 02:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>32</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2011-01-01 03:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>13</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2011-01-01 04:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2011-01-01 05:00:00</td><td>1</td><td>0</td><td>0</td><td>2</td><td>9.84</td><td>12.880</td><td>75</td><td>6.0032</td><td> 1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count\\\\\n",
       "  & <chr> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 2011-01-01 00:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 81 & 0.0000 & 16\\\\\n",
       "\t2 & 2011-01-01 01:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 40\\\\\n",
       "\t3 & 2011-01-01 02:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 32\\\\\n",
       "\t4 & 2011-01-01 03:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 13\\\\\n",
       "\t5 & 2011-01-01 04:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 &  1\\\\\n",
       "\t6 & 2011-01-01 05:00:00 & 1 & 0 & 0 & 2 & 9.84 & 12.880 & 75 & 6.0032 &  1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 10\n",
       "\n",
       "| <!--/--> | datetime &lt;chr&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0000 | 16 |\n",
       "| 2 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 40 |\n",
       "| 3 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 32 |\n",
       "| 4 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 13 |\n",
       "| 5 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 |  1 |\n",
       "| 6 | 2011-01-01 05:00:00 | 1 | 0 | 0 | 2 | 9.84 | 12.880 | 75 | 6.0032 |  1 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp atemp  humidity\n",
       "1 2011-01-01 00:00:00 1      0       0          1       9.84 14.395 81      \n",
       "2 2011-01-01 01:00:00 1      0       0          1       9.02 13.635 80      \n",
       "3 2011-01-01 02:00:00 1      0       0          1       9.02 13.635 80      \n",
       "4 2011-01-01 03:00:00 1      0       0          1       9.84 14.395 75      \n",
       "5 2011-01-01 04:00:00 1      0       0          1       9.84 14.395 75      \n",
       "6 2011-01-01 05:00:00 1      0       0          2       9.84 12.880 75      \n",
       "  windspeed count\n",
       "1 0.0000    16   \n",
       "2 0.0000    40   \n",
       "3 0.0000    32   \n",
       "4 0.0000    13   \n",
       "5 0.0000     1   \n",
       "6 6.0032     1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data$casual <- NULL\n",
    "#data$registered <- NULL\n",
    "\n",
    "# or with dplyr\n",
    "suppressMessages(library(dplyr))\n",
    "data <- data %>% select(-c(casual, registered))\n",
    "\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c880801-f783-47b5-b80f-86c183a30b42",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b721645c-ab8f-40b8-9822-57a47241c6fc",
   "metadata": {},
   "source": [
    "Оцінимо звязки між числовими змінними та вихідним показником через кореляцію:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0b8e315-1a19-4511-8b45-7a50dc5f30e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>temp</th><td> 1.00000000</td><td> 0.98494811</td><td>-0.06494877</td><td>-0.01785201</td><td> 0.3944536</td></tr>\n",
       "\t<tr><th scope=row>atemp</th><td> 0.98494811</td><td> 1.00000000</td><td>-0.04353571</td><td>-0.05747300</td><td> 0.3897844</td></tr>\n",
       "\t<tr><th scope=row>humidity</th><td>-0.06494877</td><td>-0.04353571</td><td> 1.00000000</td><td>-0.31860699</td><td>-0.3173715</td></tr>\n",
       "\t<tr><th scope=row>windspeed</th><td>-0.01785201</td><td>-0.05747300</td><td>-0.31860699</td><td> 1.00000000</td><td> 0.1013695</td></tr>\n",
       "\t<tr><th scope=row>count</th><td> 0.39445364</td><td> 0.38978444</td><td>-0.31737148</td><td> 0.10136947</td><td> 1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & temp & atemp & humidity & windspeed & count\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ttemp &  1.00000000 &  0.98494811 & -0.06494877 & -0.01785201 &  0.3944536\\\\\n",
       "\tatemp &  0.98494811 &  1.00000000 & -0.04353571 & -0.05747300 &  0.3897844\\\\\n",
       "\thumidity & -0.06494877 & -0.04353571 &  1.00000000 & -0.31860699 & -0.3173715\\\\\n",
       "\twindspeed & -0.01785201 & -0.05747300 & -0.31860699 &  1.00000000 &  0.1013695\\\\\n",
       "\tcount &  0.39445364 &  0.38978444 & -0.31737148 &  0.10136947 &  1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 5\n",
       "\n",
       "| <!--/--> | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;dbl&gt; | windspeed &lt;dbl&gt; | count &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| temp |  1.00000000 |  0.98494811 | -0.06494877 | -0.01785201 |  0.3944536 |\n",
       "| atemp |  0.98494811 |  1.00000000 | -0.04353571 | -0.05747300 |  0.3897844 |\n",
       "| humidity | -0.06494877 | -0.04353571 |  1.00000000 | -0.31860699 | -0.3173715 |\n",
       "| windspeed | -0.01785201 | -0.05747300 | -0.31860699 |  1.00000000 |  0.1013695 |\n",
       "| count |  0.39445364 |  0.38978444 | -0.31737148 |  0.10136947 |  1.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "          temp        atemp       humidity    windspeed   count     \n",
       "temp       1.00000000  0.98494811 -0.06494877 -0.01785201  0.3944536\n",
       "atemp      0.98494811  1.00000000 -0.04353571 -0.05747300  0.3897844\n",
       "humidity  -0.06494877 -0.04353571  1.00000000 -0.31860699 -0.3173715\n",
       "windspeed -0.01785201 -0.05747300 -0.31860699  1.00000000  0.1013695\n",
       "count      0.39445364  0.38978444 -0.31737148  0.10136947  1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    " select(temp:count) %>% \n",
    " cor() %>%\n",
    " as.data.frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2366e08f-8558-43f2-88a6-ed75f36c1c05",
   "metadata": {},
   "source": [
    "Щоб оглянути інформацію про частоту спостежень по сезонах варто скористатися записом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5a3e614-6b53-44a9-8ed7-a08c0e310861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 4 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>season</th><th scope=col>n</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>2686</td></tr>\n",
       "\t<tr><td>2</td><td>2733</td></tr>\n",
       "\t<tr><td>3</td><td>2733</td></tr>\n",
       "\t<tr><td>4</td><td>2734</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 2\n",
       "\\begin{tabular}{ll}\n",
       " season & n\\\\\n",
       " <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t 1 & 2686\\\\\n",
       "\t 2 & 2733\\\\\n",
       "\t 3 & 2733\\\\\n",
       "\t 4 & 2734\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 2\n",
       "\n",
       "| season &lt;fct&gt; | n &lt;int&gt; |\n",
       "|---|---|\n",
       "| 1 | 2686 |\n",
       "| 2 | 2733 |\n",
       "| 3 | 2733 |\n",
       "| 4 | 2734 |\n",
       "\n"
      ],
      "text/plain": [
       "  season n   \n",
       "1 1      2686\n",
       "2 2      2733\n",
       "3 3      2733\n",
       "4 4      2734"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    " group_by(season) %>%\n",
    " summarize(n = n())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47ccf69d-e208-4c71-9ef4-8eb9da48b3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6HiWa7t98yzliSkMXR3N2vkIjqdOx0qnuXafl8+bMtfov8HEtJmurubNXIRnc6d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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "# llok like we have almost the same count by each season\n",
    "ggplot(data, aes(season)) + \n",
    "    geom_bar(aes(fill = season)) + \n",
    "    theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edc34b17-4af4-433c-bb78-74dc4f7c6fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>holiday</th><th scope=col>n</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>10575</td></tr>\n",
       "\t<tr><td>1</td><td>  311</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " holiday & n\\\\\n",
       " <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t 0 & 10575\\\\\n",
       "\t 1 &   311\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 2\n",
       "\n",
       "| holiday &lt;fct&gt; | n &lt;int&gt; |\n",
       "|---|---|\n",
       "| 0 | 10575 |\n",
       "| 1 |   311 |\n",
       "\n"
      ],
      "text/plain": [
       "  holiday n    \n",
       "1 0       10575\n",
       "2 1         311"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5anr5HuHwr/fPYt7fluvzT67lYxD5Hx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\nQ56qxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Holidays plot\n",
    "data %>%\n",
    " group_by(holiday) %>%\n",
    " summarize(n = n())\n",
    "\n",
    "ggplot(data, aes(holiday)) + geom_bar(aes(fill = holiday)) + theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39a66f9b-1262-4db2-b6ce-fdf119adafb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>workingday</th><th scope=col>n</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>3474</td></tr>\n",
       "\t<tr><td>1</td><td>7412</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " workingday & n\\\\\n",
       " <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t 0 & 3474\\\\\n",
       "\t 1 & 7412\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 2\n",
       "\n",
       "| workingday &lt;fct&gt; | n &lt;int&gt; |\n",
       "|---|---|\n",
       "| 0 | 3474 |\n",
       "| 1 | 7412 |\n",
       "\n"
      ],
      "text/plain": [
       "  workingday n   \n",
       "1 0          3474\n",
       "2 1          7412"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mNc41ZWzWVIXuiOcY1bWTWXIXmhO8Y1\nbmXVXIbkhe4Y17iVVXMZkhe6Y1zjVlbNZUhe6I5xjVtZNZcheaE7xjVuZdVchuSF7hjXuJVV\ncxmSF7pjXONWVs1lSF7ojnGNW1k1lyF5oTvGNW5l1VyG5IXuGNe4lVVzGZIXumNc41ZWzWVI\nXuiOcY1bWTWXIXmhO8Y1bvXm5pbl12fqF7vp13zrb+YXGdI+6I5xjVtlSKwMyVnjVv9+SLu8\n2PMLv+238j8ypH3QHeMat3q9uc/L+/Xpl+Xz+vR+ffrt/bK8//Z03sbX0911IQ/rj5+fWZZv\n98vp4fyPfbtb3n2+/OT63P31xb7cL+effVzenV/i+ZsXZEhe6I5xjVsVzS2np/NUzvNY5/B4\nWlanx/N37taNnRdy+cnrkC4/u37v+mLnH7s8d39+7vP1hx4uu3t6+rR8eO01Nn7jb/xDucmQ\nnDVuVTT3fvn69HQ6z+nrOpyH5e7p6e66nOdtPVwWcR3S3ePTx/OLflhf7PHuurIfz71bPp1/\njWX9v/XXWN+8fXnlNWZIXuiOcY1bFc19XnfyZXlYs/+4viV5t6zv1n07v1e2LNd38K5vX368\na/f83POLbZ5bv/n84TKp+/M2L2/pXnTTkC7/9Gn10re//UO5yZCcNW5VNbe+/XhYHte3P/fL\nj0cNlp+eW5aPv/7Qy8+tb8ie3937un7UdP3Y6+VXeMNv+DKY63j+99sb/lBmMiRnjVtVzb1f\nHk/3T/enpzX/Fxby4fTP26F6SO+Xdx8/X982rW+mnt+QveSGIZ2eMqQxdMe4xq2q5tb37daP\nbj6t78N9etq8a3f+yfXpp/PAfhnNS+/aXX728fL08/Jwen0uvx/S82gypBF0x7jGrcrmlvPH\nPusClsenzYMN1587f/fTr0N6frHl8rDD4/W5Zf0w6/qww3lelwccXnl9v/3t/nZI/7n4Hnug\nO8Y1blUO6f3lMz7vLun//PD3j9V8PX9/O6SXHv5+WH782OWh8E9vH9LpKW+RBtEd4xq3Kpv7\nfHmg+8M1/Z8+Ifv04+mH508o/fRD50/Ifro+d//jE7LrP3n35flrHK4P8L3sd0P6Zy8Z0gi6\nY1zjVprmXn+E+8urX9bwdMOQrjKkIXTHuMat9m7u/PHQ+r7cq49w373+mF3j80gZ0gi6Y1zj\nVns39/zx0Cvvvi3VQw0Zkhu6Y1zjVrs39/Hd84dSLzldHjB/Vb6ywQvdMa5xK6vmfhnSj7+C\ncXr1I65XWP2hahmSs8atrJr7eUjPD6P/94HzBqs/VC1Dcta4lVVzPw/m4087+tj8daz+ULUM\nyVnjVlbNvfKuXZvVH6qWITlr3Mqqufx9JC90x7jGraya+3VID6d8jESiO8Y1bmXV3C+DeciD\nDSy6Y1zjVlbN/TKYU/tRhmdWf6hahuSscauquf973b/9V/SyPNjghe4Y17iV85Duz38P6i0y\npH3QHeMat3Ie0rfT3et/5aKSIe2D7hjXuJXzkPKVDTC6Y1zjVhkSK0Ny1riV85DeLEPaB90x\nrnGrDImVITlr3Mp5SHnXDkZ3jGvcKkNiZUjOGrfaYUhvKP0VL/4y3+5e+49XvCpD2gfdMa5x\nq38/pOWfJ//ay7/K46v/GZjXZEj7oDvGNW51gCH1v1QoQ9oH3TGucasDDOnT6/8rea/IkPZB\nd4xr3Mp5SP881vDQ/HUypH3QHeMatzrAkE7dHWVIO6E7xjVu5TykN8uQ9kF3jGvcKkNiZUjO\nGreyHtLjw7tleffQ/ltJGdI+6I5xjVs5D+nb8//2yan7t5IypH3QHeMat3L+yob3y/kv9n27\ne/2/bfGKDGkfdMe4xq28v9Zu++3NMqR90B3jGrfKkFgZkrPGrZyHlHftYHTHuMatnIeUBxtg\ndMe4xq2ch5SHv2F0x7jGrayH9FYZ0j7ojnGNW1k1lyF5oTvGNW5l1dyvQ7q/frL3XT5GYtAd\n4xq3smruf/9rFJcfzaN2ELpjXONWVXP7vIaG//mvUXw5f/M1n0eCjAXrqnEr5yHlE7KwsWBd\nNW7lPKT75f3j+THw5a7562RI+xgL1lXjVs5D+ucTsl+bv06GtI+xYF01buU8pB+fkG3/t10y\npH2MBeuqcSvrIb1VhrSPsWBdNW71tw+p+OqNv0N1nLFgXe3U3D6voSFDmlcdZyxYVzs1t89r\naMiQ5lXHGQvW1U7N3fwa9hpAhjSvOs5YsK52au7W17DX/2RDhgSojjMWrKudmrvxNSx5i3Rg\n1XHGgnW1U3M3v4YM6biq44wF62qn5m5+DRnScVXHGQvW1U7N3fwaMqTjqo4zFqyrnZq7+TVk\nSMdVHWcsWFc7NXfza8iQjqs6zliwrnZq7ubXkCEdV3WcsWBd7dTcza8hQzqu6jhjwbraqbmb\nX0OGdFzVccaCdbVTc/u8hoYMaV51nLFgXe3U3D6voSFDmlcdZyxYVzs1t89raMiQ5lXHGQvW\n1U7N7fMaGjKkedVxxoJ1tVNz+7yGhgxpXnWcsWBd7dTcPq+hIUOaVx1nLFhXmub0MqR51XHo\njnGa5vQypHnVceiOcZrm9DKkedVx6I5xmub0MqR51XHojnGa5vQypHnVceiOcZrm9DKkedVx\n6I5xmub0MqR51XHojnGa5vQypHnVceiOcZrm9DKkedVx6I5xmub0MqR51XHojnGa5vQypHnV\nceiOcZrm9DKkedVx6I5xmub0MqR51XHojnGa5vQypHnVceiOcZrm9DKkedVx6I5xmub0MqR5\n1XHojnGa5vQypHnVceiOcZrm9DKkedVx6I5xmub0MqR51XHojnGa5vQypHnVceiOcZrm9DKk\nedVx6I5xmub0MqR51XHojnGa5vQypHnVceiOcZrm9DKkedVx6I5xmub0MqR51XHojnGa5vR2\nG9L3m9Ed46rj0B3jbu/o+585pNtflO4YVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lO\nL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdp\nTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hin\naU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Y\np2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfu\nGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H\n7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcd\nh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pX\nHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0Oa\nVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9D\nmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4v\nQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lO\nL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdp\nTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hin\naU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Y\np2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfu\nGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H\n7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcd\nh+4Yp2lOL0OaVx2H7hinaU4vQ5pXHYfuGKdpTi9Dmlcdh+4Yp2lO7/dDOq2qb68ypNtVx6E7\nxt3e0cGGdHp+8tq3zzKk21XHoTvG3d5RhvS3q45Dd4y7vaODDekiQ9pTdRy6Y9ztHf1xQ/rP\nxfeb0R3jquPQHeNu7+j78YZ0espbpB1Vx6E7xt3e0QHfImVIu6qOQ3eMu72j4w3p9POTDOlf\nq45Dd4y7vaPDDen036cZ0h6q49Ad427v6GhDOv30TYa0h+o4dMe42zs62JBOp+cvYchXNuyl\nOg7dMe72jg42pBtlSLerjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0\np5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zT\nNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM\n0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3\njNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45D\nd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauO\nQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2r\njkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHN\nq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5ch\nzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeX\nIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSn\nlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0\np5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zT\nNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM\n0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3\njNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45D\nd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauO\nQ3eM0zSnlyHNq45Dd4zTNKeXIc2rjkN3jNM0p5chzauOQ3eM0zSnlyHNq45Dd4zTNKeXIc2r\njkN3jNM0p7fbkL7fjO4YVx2H7hh3e0ff/8wh3f6idMe46jh0xzhNc3oZ0rzqOHTHOE1zehnS\nvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ\n0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6\nGdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1z\nehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhN\nc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4\nTXN6GdK86jh0x7HWTWgAAAdkSURBVDhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0\nxzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4\ndMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzq\nOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK8\n6jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnS\nvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ\n0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6\nGdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1z\nehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhN\nc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4\nTXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTH\nOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0\nxzhNc3oZ0rzqOHTHOE1zehnSvOo4dMc4TXN6GdK86jh0xzhNc3oZ0rzqOHTHOE1zehnSvOo4\ndMc4TXN6GdK86jh0xzhNc3pvH9Jp9d/vZUi3q45Dd4xrFPhnDOn0z5OLDOl21XHojnGNBDOk\nv1x1HLpjXCPBDOkvVx2H7hjXSPBPG9J/Lr5HzPrThnRh9Yeqfad/A9YOdB2r5jKk2DjQdaya\ny5Bi40DXsWouQ4qNA13HqrkMKTYOdB2r5oCvbKAdKBXAga5j1RzwtXa0A6UCONB1rJrLkGLj\nQNexai5Dio0DXcequQwpNg50HavmMqTYONB1rJrLkGLjQNexai5Dio0DXcequQwpNg50Havm\nMqTYONB1rJrLkGLjQNexai5Dio0DXcequQwpNg50HavmMqTYONB1rJrLkGLjQNexai5Dio0D\nXcequQwpNg50HavmMqTYONB1rJrLkGLjQNexai5Dio0DXcequQwpNg50HavmMqTYONB1rJrL\nkGLjQNexai5Dio0DXcequQwpNg50HavmMqTYONB1rJrLkGLjQNexai5Dio0DXcequQwpNg50\nHavmdhtSxLSd2t3FXkM6EKv728l13iZDio1c520ypNjIdd7mLxxSxP4ypIgdZEgRO8iQInaQ\nIUXsIEOK2MFfN6TTiv49OMtx3uZvG9Lpnyfxkvx/mTfKkOInp9zmjTKk2Mht3iZDio3c5m0y\npNjIbd4mQ4qN3OZtMqTYyG3eJkOKjdzmbTKk2Mht3uZvG1K+suE3cpy3+euGFKGQIUXsIEOK\n2EGGFLGDDCliBxlSxA4ypIgdZEgRO8iQInaQIU1bll+fqV8sDiH/uqZlSH+k/OuaduNCMqRj\nyb+u3X1e3q9Pvyyf16f369Nv75fl/ben8za+nu6uC3lYf/z8zLJ8u19OD+d/7Nvd8u7z5SfX\n5+6vL/blfjn/7OPy7vwSz9+Eowxpf8v5K6gflvM81jk8npbV6fH8nbt1Y+eFXH7yOqTLz67f\nu77Y+ccuz92fn/t8/aGHy+6enj4tH+A/WbwqQ9rf++Xr09PpPKev63Aelrunp7vrcp639XBZ\nxHVId49PH88v+mF9sce768p+PPdu+XT+NZb1/9ZfY33z9gX+k8WrMqT9fV538mV5WLP/uL4l\nebes79Z9O79XtizXd/Cub19+vGv3/Nzzi22eW7/5/OEyqfvzNpf8XSFfGZLA+vbjYXlc3/7c\nLz8eNVh+em5ZPv76Qy8/t74he3537+v6UdP1Y6/wlCEJvF8eT/dP96enNf8XFvLh9M/boXpI\n75d3Hz9f3zatb6ae35CFpQxJYH3fbv3o5tP6Ptynp827duefXJ9+Og/sl9G89K7d5WcfL08/\nLw+n/Lsyln85Csv5Y591Acvj0+bBhuvPnb/76dchPb/YcnnY4fH63LJ+mHV92OE8r8sDDmEq\nQ1J4f/mMz7tL+j8//H3+ueuHPOv3t0N66eHvh+XHj10eCv/E/YHidzIkhc+XB7o/XNP/6ROy\nTz+efnj+hNJPP3T+hOyn63P3Pz4hu/6Td1+ev8bh+gBfmMqQrLz+CPeXfFmDtQzJxPnjofV9\nuVcf4b7LY3bWMiQTzx8PvfLu25KHGsxlSC4+vnv+UOolp8sD5uErQ4rYQYYUsYMMKWIHGVLE\nDjKkiB1kSBE7yJAidpAhRezg/wPnXfW8MHKt6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    " group_by(workingday) %>%\n",
    " summarize(n = n())\n",
    "\n",
    "# Working dats plot\n",
    "ggplot(data, aes(workingday)) + geom_bar(aes(fill = workingday)) + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142d5e7d-289c-496f-9f8f-4384a15404f6",
   "metadata": {},
   "source": [
    "- [x] `weather`\n",
    "    - [x] 1: Clear, Few clouds, Partly cloudy, Partly cloudy \n",
    "    - [x] 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \n",
    "    - [x] 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds \n",
    "    - [x] 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4727a2d7-cdae-4687-9cdb-8893204a13ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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krR2VbHs1e0IYKQYoSUorOtjmevaEMEIcUIKUVnWx3PXtGGCEKKEVKKzrY6nr2i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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# weather plot\n",
    "ggplot(data, aes(weather)) + geom_bar(aes(fill = weather)) + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac065ca-2280-4c60-b468-86f1692c141c",
   "metadata": {},
   "source": [
    "Переглянемо також залежність між числовими факторами та кількістю прокатів байків. Для температури це матиме запис: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92f1f82c-c5d3-498d-8bf9-1398ba150d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aSALQ60unqTxTU1nYsUqGI2iAcZ+zC6lGsG0Nen6Z7YkUjT9Plz+Rfl3egD2d4o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\nKZEIwP8BKleUXrbmqgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temp\n",
    "plot(x=data$temp, y=data$count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02309391-5634-4c60-8336-a8872b33bf06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UVKICVoWdfO4LlBooLA+zkSh5WxmuG2xqqCj6TrA1lkKpCytcxmwyATuZYeu4NE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\nEkiSlCCBJEkJEkiSlCCBJEkJ+n9+QOWT2KkmjAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(temp, count)) + geom_point() + theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb1e957d-d12a-47c3-a488-b023a343746a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gQJNUPerXaXRQHSGjNQgVmAVNObgWSsQAKe2uZzYwykwrJ+RYhqRjEYhkc9XgHS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\nQQFSKDRBAVIoNEEBUig0Qf8Pj+6kJLDTPAkAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# atemp\n",
    "ggplot(data, aes(atemp, count)) + geom_point() + theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01dead12-440d-4570-bb14-77a77b30b9dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZCO4Vue2tXE1f59OxJtB2Ykz4gLiQ41GAa47Xi5kKK0Z+gohTSVtV07RcFp9vv/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\nN6U6+XisiRsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# humidity\n",
    "ggplot(data, aes(humidity, count)) + geom_point() + theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11b1d8c7-8eb9-415c-823c-6d9b40dfd7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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frzWN055jCRaLdwFV1X/UBWImGU/jWK99Lz7toFXvNhkVLKs60tUf3vI0kkjMK/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\ngEQSgoBEEoLA/wGJtVqxChtzrwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# windspeed\n",
    "ggplot(data, aes(windspeed, count)) + geom_point() + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e25282-cf0e-4cf0-a11a-1de67189e1ad",
   "metadata": {},
   "source": [
    "Переглянемо також загальний розподіл частоти замовлень байків на гістограмі:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43568ab8-2c6a-43b7-99d6-1c40c52fdc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChIy1c7PAJpcBSk5asdHoE0OArS8tUO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\nP5Y+j9edMgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(count)) + geom_histogram(bins = 25, alpha = 0.5, fill = 'blue', color='black')  + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1863785f-849c-411f-b0e6-62132dbc0d77",
   "metadata": {},
   "source": [
    "При спробі побудувати графік залежності між датою та кількістю замовлень байків ми отримаємо помилку або графік, що не відповідає порядку дат, оскільки на даний момент R не сприймає поле datetime як дату. Перетворимо дату за допомгою функції `as.POSIXct()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "45c18344-fe2a-47be-8604-501117a73885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aldlUkW92d4dEIemqO/h9W1F0DaC0PQqZWVEqFZqx4SqQFENINIh1pTPxQ2nY8c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\nN+bb9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data$datetime <- as.POSIXct(data$datetime)\n",
    "ggplot(data, aes(datetime, count)) + geom_point() + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c856eb4-06b1-4ef4-aa7e-b48fdcc17d05",
   "metadata": {},
   "source": [
    "Чи можна прослідкувати зміну кількості замовлень в залежності від пори року з цього графіку?\n",
    "\n",
    "Покращити вигляд графіка можна додавання параметрів у `geom_point()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e3c801f-77aa-4fc7-96f9-0719b9553dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JA0SaZKE0HSJLF+U00fCYF0fp6d5sDTFjSov217FZAO0xpfNJEwRkEiE/T0zrHy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XLR4ku2kfTo0BCWy6scXwmh4k1n3z3zbz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P08D/tRMbpSzJq+zxiRdsvFfLUhSNK0o6c4\njI4l6Y4Q/O40PTIYoIu4WCgkfgYbXvIQQ4KkHxqQ2BFJ4gdHLKnbvYWSJpD4mRJD7wVBAqPw\n0c0JCB83hVW5S7ZMjhSSnE84EknutW7yQ7aPxE9YRzISMpvJLzlI83l8MYR7cFLnoUjix9lR\nOZ/3JIIxns724+tPKuMfHv+qGunpuTUffDdIKgjSJECaUlmb1h1Jo0iOIiR5LXFEO1/azcM3\n1NKj3n8+lUKQLtRLmruWXfhgOyyY/X7eRUn0ODnCRi8UZ+cyX+9okefxuF1dN3szABaKNEop\nbTQWF/FplUTjbDzp7FoiNOANSAW6LoXrIwVHFPKAYKGOX8GGgYkhQQIPHRiHJPyQOoo3d7wz\ntYAI0vFYmQp+gw0Gs3knPpYtkPh0CiQb8MGH/tS2MWq3b4Mkl6AWSGb7RRy8wUDMa2cy+wES\n9sFhR5gCMBe0eXxteeynH/9iSM/1oGdbdrQZxZcgqSBIUxqLqzuQVOoU0oSyNvwVJI5gDsWY\nrkc+JwlIlx/7/dyviKhvO9wqQ5N/t9mEaiYuRwIk7HhJEatbB6mmxdLHbYAUXgjzr/yw3O4B\nabuk3BGmEcWB3f27kHo0orJY2BURzwNa21bghuJXUuD3GRL+lqaYRiOJ882nZHAWQ8dsPh+r\nIz2d5Mjn05RjaSHx5hvSR4qQDvmkgECifujOQWqtkaRsSDJLlpRejOqiwTA5SpIY0ijU1MFR\nOGdpqPfXh/Skjno9O2oHSJ0vX4pivaiWsWE/oO3tCFJ34vY1riFJIVFoW4REjT2aEiVIC0ky\nwMeGHO1ySLsdGnUH6gPZmJZvPHckji4OUr0nR9vVyr0YdUCk4wZJdhxkOqXtxfpcoKmWkddi\nSbS1Yx7wNKDsFOToqWsh4byAy6AfHUkZ6xlIbgAMb1xxM/hYmRp+JZBaNphdRkm8X0EAIj+E\ntt35gxqJx3N2HI8l1830kfDSro/EJ48krdy9R75ndBQlDWaAVAx65YjzoERJvzwkllRnveb6\n8bGLTtMXFCOTIJKnDUIhCXfeid+ONbzYeUFFZCJBonwXo25TEESzJCs3aMaJpyn1dxPSTiG5\n6LBvXEgY0uVysY5QECjtR4AUPq2FRB8fi2a3biPJitL7KCTq8cTRBkiSsalsXHpQ9shdGyQq\nX7RF8dgUscFdSLU6ms//GUgkac+Q0g/dapaU5/urRZJC2tLgDj+RRu1kryYzaicrPOKb1vot\naYq6BVL4y6yD2lijE1M9/stD6nRCcagfMkg0HRuqaZonHE0ySCVB6jYg8bSnBBWlqDGqpGi6\nMZvG4ZmOH7Rnve8jyZqIl5d9BumQQTr62X2GtF25OEtyBE05JNSVcMQtVxk5iK/FBaUNEi0Q\nFkhd17LjfezuQOq1QbrVCql2kFa8dXTLjudcqHlsxUdccWE95/n+6ED7bx8XlpvLRpBWqMVl\nvCOVjTp+IOv1PiT+6+eEJCMMD/GeyY8zJDIDSGm5hEwbhDbQZDRqgYRGHw9CxFsbSs0iQJpM\nMkiXy2u4OQZI6P4u524WiSW9nPf7JiSkKj68HLFQKWvDhMJAn4jKa5RcTsjROLTqtkgUlyqk\nqip6Amk6ze4lnKGupWn35Qvm9Cng4elpQJL0qQipj/EDW8L4lRt9JAeptn2kIIkmEO5JkthE\n2+aKhfXoTlk8YgZO3yIPkKhlt2dH8XzeZEccTFm7ii9c476UlnBiqLNYy9eMkEhS6Gh/Okgk\nKWVWIkgsZrio2BE3d7lt1xsglozjKbWMocRR74nml2JjG3HYgISySrskJUhwtCYNO59STSKU\nCdLe9ZEQ8YCLjez5MiuSJMXbrTgSSUGKdZSKZ4IUjnU4A7T9BL8Wag+GlAezE6QhRvRo5Q3+\njM/xisdhP5SuKUmSapEkhdq/Gf4ZClhFcVX0gcyo3Wqu5JvxQQnSfuUTqaTz14BE27rgDNX5\nK8pIgzjS83mjoBAeffPNTOTxIEl0YtIQzUAlIZmzxHg0Pps5vrY89tOPf9FGYx2VFOvqJCkU\nsQdELXfsJOKXLzTaQDt+uxopPDjnfihCXtLwD9YzBEhcq21txmBydD5DUpbkk8vB7ng8v+LK\n2zJxOJ3YUZB0bELixHbqiCWVJa1VqJYKybQ6+d4JR+uqR8soDKQgqTfMIc2+BEnDCClbWMyS\nMNHCrUltX6Ju6nTQlsyCbTBwUWnciAseEEfLRqTd1rTt9vYJytiQzl+jbXeiNSaHRtieDqU2\nIKkjkZR+gze8WSwoFDGTFOqwnkQ8G0m/NqRpVyXVGSREDgmkjoWETbbKYgpIA1RI4RzzdcHJ\nrIooyUMKkiKklMMekChk7ppDkrVFRySFz3dPUEhxEUUGaYdhOwep9pCoyya/Uclio2q+liR2\nJS+kKAgSnRJiFEtE+FJfkMmCID0O/BJ9VGHhNx8x82/vPsSk0xlg3MVDKnkWi7qWta+rBNKW\nxwbSCdBeHiWmtblkJPdJOn/uYEhtTb5a6qPl5j6kiXOEKikci2EGSSZiS0AqPxGk6fNdSLRO\niUKZO51nCp2jMrFEIWJIE0BKwawWUm0h1YA0rxKkyCJBui59RNmWKyGClHXEUB4ASddQWGRS\nI6FYOEj1eM5BOIgm4yV/8huYOQ2QqmGAVPT7qXscKpZqyudkAEcx+hubcQBSqIwQ/eEgce9b\nIA2do8mk30EYbDz4N0qGxIOdeWCoQvJrh3W4hEe0zUmLWYRwNLjQUpRte+epFkebTdZHkh5S\ntiMnXWv0A4cZJOoI0sr92LL7LJBUUvhHr9Pl4F08I5sm4WRgxcBS92RcogyNRlO07QYYP4jn\nmM7mlP5cYkFSmpBYLNYo0irJ3NoCEoG0W22dI7EDSdut24aEHGHtBOXKpylZlYSWzlIchVoH\nkRTyzA2SEP6tjswi9DEcDeerCqkZKBEKfR2U+tBpCjDG4wVHrdKZmVFpmVKzLoOko1idUIww\nqu4dTZChNnPEOw9MaMFGP48/32xIUr4KH/kVIiR7+3GQ8uPCi7qkzewXyIZDHJEkfQz3Tkp3\nMcE8kglOFjrFhL4wTyvipPFYDW3XR5sWfp4+UoDEkvAPbIhEQfD4h0B6ZkhL3SU4/G0ISOFk\nYVTKQqLzSRlnwg9jf77giMno0k+S5MIK6tflkiTt0IBZx8olVUJc5diCpJDwA0cJbZA0oRQi\nFCHZCIob7UdRGUdS+CKk4WoVSgNvYUPPEKQ+ourCF82CVglS+N4BEmeN4R09IqTOo4tzFUg0\nQ9tSIyGlKxxlkOibcWcIswOxNUqJSv43kC4KyddwnK546beICUf4Nw92uh0RcDhIA0n7V8dZ\nZxqiGnyqUbtqmm6nyCxF5ShBwpq/bugFbGL0d3hwOKwIUomZIlvrlwWnu4ajmclJX5k11LT7\npZkdwY+cz3/ul7oUhkn41lx2S2ZI/BNHc/Gx6eYyQXKxSLiKoYiSIwcJ65HKMUNa2cV4GHAL\nxfs5VCPjQceqEEj43qFtR7u0PeSQOgxJRr/JUZ8jJwZtVRI7ypYWxq8mjpIk+QI5pNRHatY4\nta5OUkcp+puHLBqQ8E/ryGVaTZDsUnMZEudtyD7VPFJFkipKThyuvl2L1i3hKJSUFkhVhDS3\nrWectek0/HCoi8Z21jNCUkcpLwGOP/cGkpEU075nbRuShB/YZ5A2p2WS5CHR6DdBmntIvNUw\ncjOsKBuinAAUhemUIIXy7yCJJDh6fn4c0FZtskI2SSJIcR6JIPU5aiiDRGetFkhL20PacB4f\n+v6uw1drKxhzrG6JxVXayTva/tNfa12dlEHSLV2iJPv2m3ZINX0RGpAZihl+vAVS/J1fHFJV\naTs/g8Qt3MfHHiBtYs0Tuk1FRZJCh4LbazYuMcjcoEIaO0gqibZjdmUiQfJVUq2OqDh4SMjt\nfcRA7n6/3TUhYSYJV91dfXbEkOY8QJj2FYalisqLhYTBglC+YWLQMX2kCUlCkBAq7dEAkmL2\nk1CMxhi5QgZNuxnzhEbWKR6gpkFDG0o9qAuF5KZfJR8WToCHVNuhOrtUyTlqkcR/aYckksy7\nbzZt+5jqhxZJPtSuN6EN6D4dpDo6eux3WyBRcuJBbyaSatyLnosgaTTsS5Vko4JvoYyMAWls\nIUmIEIWEL5Z5qpxw8f7cW0n6Winte8PR7kgjuTRGZ+6i1LZLIXveEUEK77Gcr3h7Y3k5NEj7\nEgkT+taxZTcciqRQIw17tNZcOs44E2WENMKIg5YjvAgtakOEXieVsfDjT32eegl12dSegAGv\nxxBJbnCM8qJwfGIOqf1gR9ddy3bU7mi27Px0lJxNLHzhPqT0OuNz6Ztlwd/h89OWqFFSer1f\nGxJiUzmxXT9K4icY0v/QpZ+xJDzMjePRkCFBkl1qHhzR+nsIkzwAACAASURBVIpxViMxJN5p\nLCsTcGAlxde6A2m/f2dIJ4YESVooNBGRlIt0E42QlltamBDeTJ+Co8FAHA17+v15ZBeSQjHH\n2lYzAOUhuX1kQ51EP4uzlkF68o7kDAwEEklauGFmCU/khSd/x9HfhuT6UMd2R8h9siJJ8MD7\n4dhl+C5QNf4O3WTGLKl2jn55SOIo1EhdbtzpQgpMOP7PHxjO7Jk5oaJYDOEILWG/rK8GJIRZ\nzTho1VVIgFTJ4tSGow1vEkwpA9JrcTYBzHl4SO+HuJpt73Pv7zJI6SAllSygcHGe7IgkwVGK\n445zagOqX55tauIbTaOGyplPXZIU3mJI5ghSJ5a28ONYU4xHx22QaKGcGwOlRf3JUbYg6M4h\nkK5/Cckce1rYh3VNWVDfmvuaq5WMK1pJvj2XDnQcZyXZC+WicCmmfnFIXYX0mKZm+UAU2f/8\n8RAgFT1KIcUPLwKhEc8fZJC6RXEUSCqJHveQllQ64kVDx4YTmKwIktnIDuGpPHtotbzzsJNC\n2pubayg/4d+8TiCLgqH4b5KkWVlSJHOfmiHYjsUErXZ1A2ZMo9LichMHdEMwRPi6QdIfFhK+\n6LArkDDkoIUN81MMaewhST+KlnAsGpDmDInwu+AhPfb7jeu7ZJI+vPLxJfb7EznCGTGcMBlx\nD1IdHeE/idR0GvrH2jlK8Q580lre/Ovf+YT/t8e/po/0IaRBgPQQIBUp+HuxCFWRhySSQtE7\nAtJqNhFJ8kITkVRVvDqVlnrrB0AW1TNl5ZKsavrELSaJpJi7o3LRB+KqAFsjURg5Odq6nRDq\nciwBO6uY34g+AvKO9Lk5PyNIyChG34Ynp4MjcPBxQCWcANJw+EcOiSfzHyn5xyDVYmgyP61W\nPZ7eNLFLJTuahDqxBRLt16nDeU1J7MhIQtVOI+B/21FtHPnhDkqmRy/eAqlOjmzlhIR9KslG\nDvEFbR5f/9ZH/L88/iWjdiUGFKR50rVJ9LGeevD09D8PD+OxWY6EpKHhHEHSqHSSyNFxgi2w\ns5ASCSRLksxtb7njLN6BADlKjbIbZ9bCHCIc2dXUMblHnbftOB2BODKSSo3wXiFq1ELCd+gD\nUigAQ3LEv8aOhnDUeX52kEpuuYHa4x8U3ZBadlDUoUkDdiSSRpw1GV33GSJuMNqpH4wcTQYT\npIYw5w07JdFcAbVG83JMhzqKkqiJTIMxf9Gyc2FVpwySk7SVT1k33x8DMQMviSDxCGfvU0Eq\nyy8REvKtdkxP4Pk5QAqScAvNIQ1GCG8oR0ZSKFbB0WQyUkgxNDW54kthW+ME6f28kylW071B\nbhPObXc66hZZ9LiBtN9kO/3tFJIdf6zFC74ASqRIwuPDkoYZ+gxpNky/JjNCfXI0tJBKabr1\nh8OCvDym9UhcIXUYkgk1kwqJxsBm7j4jsXaANF7s9+n+I1ENy7k27O5CiuczOqLTebJbwfvD\nRwFHSOtsBH7tWsgtjlohdT8ppC8REi33TJLCeaKsWwFSmSDVPGHPcUKjURNSNV3RLdesPEBB\nwSmd5nuDABUgvVtHLIk34zsJpCxTzkEdbTYZJJIkkMzblbrSUEqklpYhRhmwDK/kTQk9pNA5\n6pOj4dBsHVVSa7jX7w+LLwTJLgZkO9QcNJAQEP0kkFbc9I31tUBCa9iNkmw2M4W0akCiU7tj\nSKnsn7XTuHO3nsbRjALerdogtRV9OST6O4fUC5K6LKnXQ46xz9JHCl/4S+eR+0h9cSSS6Dw9\nDYcPDyuqkOJYZlk+897wyRG17YbDJUa+q9DZUUdJUiHhQ9Ns8ClcwNPr67tGKlhIhwTpbCFF\nScaRQMI6WwRLEyS72FwdaSNJ357WDSHme8jbezpIoesU6h2GNEyt3pJ6lTS9imxlflktL1HC\nhpgO0gBDDU+rXo/nNt1s0U3r7Gy8cSY10ny5WmWjz3Rqd+s1JJnCT6MvvK21u/XIcdZ/Z5Ag\niSFBkgkNvw9Jgr8x1Njtmj4SBve7LKmm/mBclPOLQ6rLR+4DhAqpCQkVz0OAtGJHLGnQrwCp\nBCQ32jAslphDAqR5Bqm2jjJJq9Ur0qlS4JyHdFRIJw+JshkzpFZHLMmuBqrF0QwjHa6Jgq+I\nhp1sk8t9JEIx7AzKCi04gmTHYcoAqdvv9wCpVzUgDRhSz/SRBuhuUoOw4KkZu7UHChjJyiDF\nth0PgHtH6zU5CpIcpNNe94dvQuKTWEsfMlvhxUODoQNrF1lQzIT/sPFyDsgRbS/QMwPhvTTl\nJo6ipF8bErJCSph3aMlZSA8BEjkiSDHeI5y0CkWrLPtwRNFY/FIFIBWTcrdrhTSJjuZ22fZq\ntUXuR4Zk5wZj3l0ajfCOuOu0zxwlSDgsJMqUT5DkY6WwGr4nKKRZHLWrO52yqqaas8RCQiRv\nt0+Jr7/0ZMcXgcSOBs9UI6GUSTs5QKKelTpyk2+1ZI9rQJKBb5N8mY69bBlPkHyNJAOgbZD4\nFKLlezhkjvg6LJFOwy5XovfIPqxeTp76Yke2aQtJNX9tdaSSfnlIPV0v4ftID12BNOiEZpJC\nQjGpIKkcA5KNF+a+ZVWWHGLpVmcjj0OE5J7BWB0kMSQ052SsScYTuDicz6ZQKKRTNtJAkDYG\nUihfAqkUSLPoKEpiSNJHMmUCkLBEtofaJYMELpwj/ktBCRz0l2TDpOenTm9gO9sl1U7DYaGO\nUuHkSF88MPQzyRszJLcyAXXREUOys8uy/VqSZK70WR1hx75wftLavhRQ5SBFR2Y3p3g570LC\nERoziBX7ZJCeAqSnTuep08VmJekKP4S7LiANKNzBQiJJRTukqqLOc17v1EiIIpB8XUWD3kij\niv/adoWO4HJxiFu31LrjC5bWNCHtNklSKmGYaOd9cJOjWDJ5Vexg6B3VaNnBPjXT8J+UzPmh\nS12jJ5oq+UKSvvBTPZ4/wvinhURxet1xGyQemJdmEAVKxe8ZIfm8MAnSmiDZeCeZ21ZJ9kqf\n1VG+iSzz5Zdqg+T2RdPL+REkdlSVnwkSJgLFUWfAjuIl7iL0GY4AaRNbduNHWglQjItiNHUr\nWMhRNcYglIuC4XcKBXi6aEJKPd+tXwb9wVQIS2quUKstJJeJjQeZZzPOvJJB6suCh5kvE1hG\nwYB68l9+fDp9oNtJqMDL8mH4hQ9+rpckPbVAoj6Sg0SnVR0Nh27QTCTVWToLA0kkrUw4SHSU\nrUiijqYk/W464iNKoicoQn6sSWyywW+RhA7RHUdJUvxszeNry2M//fjXQeowpOeOu8a9CKmo\nAhYZaxiPMUdSTYtRgCSS5MUIEqZvJ9MGpFCONcqO/5tqq9Rgd5Bu6uh6bTToNZVoY830br/c\ntUGiBlQAviVJOaQ+Q6pySAMBNHUVkkBC8By2ZR4aR0bS42NagU1csK1FL0mK56Us5xFSNvzM\nYbc0nEabxeqXjN+ATtDKp+O6CylI0uz5NmnD3ksyuR5oHe44OsqmkUhSTyTZJwQSTislVDKf\nrXF8bXnspx//PpCGpuIBpMfQCx+OgqNiMrUrWIoJHFEcRAYJq9wmC8lHku2UmqbZHaS9dZRL\nOiqkPI1HguQyfMtVrOQ5txhuIBMiRRPSQCHZLlL4dhScx44e/hgaR/Wkx8MNlMtMV2CTl36Q\nJG/ge0jz+eAuJByh4bam0x8/MUtarehW436ev+YdSLFt58+YOfBv4xKQyuQo39R8MOC15Tac\nt1ZIvPzKTsn+0pD+//bOp8VxJOn/3fCDgYaCOhgMPuiggw46GHQQAoFBINBBBx9s8MHgg8EH\ng8FgMBQUFDTsC+hXMTP793mmRm/wF9+IzFSkZPfuPHi7asqK3e0tW7Ykp/KTGRkZfzjcEyCN\nJ0jNIIFpFqR4mqRBgGhPeop2yVOEEXGUV9iPhSVuV2pfoLKQndCZD4vEXVuQLsXWCDRdjs6O\nI58k0fr4076j987mtdtbV29zgJ+iJIjccw67NhgOleynSMFciwFOVRpLOBddB6SmojmZOSKQ\nxg8PvGttWyCObFKUsRfYJuWRit66nDlKLEmXQULyYj/DucQtWo7UF0xn7eNiSGpAUqPb7OnJ\nB0lriq/Nwbh89ECy27CdABMWmoqYo7sCqcFSOggf4bI6CUSnNSCFUYIdfSIpE45AEpfahUEh\nTRxHlqQUoSjE0byqOsESPDQTSPU1kAw1beZ3l0KVQVKJgiBiGX8xHHmGrqMlyfgvtAm22cdl\nvd6vvKzgkESi95BLDBxpkGB/AEmdWobIU8YgWY4MSWXJvSoIuxyVXBAzknSrWgxICVvtumsk\nE/zaA8kYIRxHfV+EPkeq0qzXZpc4cvH5h0MuJF0FKYn9nFvmR+V3BxIbdyeP43GEXPrayjKz\nIKVpljkLgQMpYZDaEHQwVvIu7Ww+rzphR9klktRdnD3BOx5I/nBs0ui3HLlewRyJteFQ+89e\nQKIut/I2aln4oTdY4HnOYYYj7A7E4s5rk9c5kMZdkLhbBQDJOkDLVwQkz1HaiAGJ3+7OR0YD\n7YK08UHaqMb5jlOPE7/NNEjHDkgHAemSy2rrGoRf7LdaYkGaeu35oUHKY1h3AwEp9ECKGKSU\n/WM6IEV50oLEcQH4QpqepNXFymx8l+U6xu3KeBUs6BP9Ccloawoktnmb4paKJP7QC0A6dEBi\njjAhURfwx1B6igvmqAeSOI1N8wSst6sa4YhACsYcR6E4AkggKQBIWQ+kGItOgMSFuMw+0iy3\nPg9hJxzOkuQVTmGxNpHVdntpQmKShCN37LWfF7IVs4XQ2ff1QFp3QNqUdlrt2xqmDiTFS2JA\nyu3odC9Oq9BcpuxqF6ZpqEAKI0MSS2uzTrMorjhOfzbLy50UcGGQEguSkCRZQl3xSb0/3tuZ\ncBzRw2x1O1hyj09P10E6dEA6WJB4jeCNoa/NYlFeAsl4X+ZJkWPSRAVUOWBAAkeww0HHlQME\n0ogmqYzmai5hrdZIeYxQJCSSeTAcGZJoos7FCU+XcjO3xiT5hVNYGCPYGbditbPvW47YN9Wf\nrV4xTV9Obfdky793QNIkrdeeE8XhwEH1eXnhdA0MNBx2EikNzsxShqTO77xwkuWlM99afhBI\n8ZRV+keaevQaCSCFiSEp227t3k+GXZUZUlRhl7ZUdr4kOTmSsmyl+4W2BpqacJYkebiixomm\nZq0NhqOnpy5IWCM9v7y8gBavT9CrNfzvGCRVqLXhcZ+zV6+4DmfrIiTel8xRLhyZjk7wYECI\nwdHCy5SfVpPRiJZSQYqSCxmTZMbdHDmvoQw+jEZ2DOJhideOLnLS1yxfpYEMSMp2YzgCSFLt\n0Ly/sxxBfJCYI7gu9vbfzFatI8kdOHJJD6mNtOb6pO2u1MGEaHppGYxwKheYlqaXQRJvoc7v\n7Mrywns3lx+zRoqnDiTqKoWzNZAmwykHOLqNQAJJ6HxZHBBIvOiZzby0DUlLEnG0uALS2gcJ\n+VKfGxM8o8fKIyYk47Xa4ah5xmwlIB10n6BX6/VO5iPmyHWKnEEqxfm7Ui5CmZCUJy1HQlIc\nJ3k9DwmkOF4sJIhYSKqqySQOLUhjvQ7IkfMaJGF/aaoVZakz5DhSJAEQbh3LkSZJDJYrUzXU\nkMS+Q9ZhtweScGQU5HYppEDyrHaNKY1jOdJN/dqYUOf+0g4gmTDIPkjSK7oWyg8NUs7ezaTS\njx+CQLLzFqbJ8CpJoPHlcdZuYWZBYkHKfZDQioVdYy+ugLT2QeK8w3vrUadAEs3Oun93OGKQ\nnl/62yUm0SRNSZYjISk3IJXsRK03ZDMhiUGqaw+kGG84kNo4WALpIQyzMEzjCYpI0QncpmOO\ntPpE0gWQIC1HjiTwgUNVVViOFEn0Y+bzcr/xUi8ZdA6aJPOFE6pWO5uNmnoMSP0QpbPiaL3w\nJ38EeDiO+iTFEpKmbAqyGa040iR9bJBygIRQiodJIOgkHZDCFDFatudFEUBKjBkuNySZswFD\nB1JbFq/pViJtJyQZccERnMNakHiY5IDPfnza87Mh6bm3XXK0BftQH0mlWhX/AQZp44MEkhCA\nxBxpkEAS3ogR/7rQuYKq6OFBPpdOJmPPnawoIok8Z5Cm2kEGvbGIeyBRO5kyShXmpA5IDXNE\nt+3nMDOdnedjUvv0HKJB0hO8gHQh2M98+BpIjYDUjq9K4oRBkjqfts3uFqQMII2vgZTiEEcN\nm44nIKWcPsqR5E6HbUUHEgI8rUkBbjG8xlpbkuR9tlQjCk2YaCckY7U7XoiYfua3zxc4sjPS\nrgMS6J7TOwzSpvRdhEy+b7gJzufaGCAkxQFI0iAlDBLUwIJmpM60EyVT5ItMHEn21symSx8k\nRqjif12sbPuLmKNeMkDT15kjz0vjtbkCEq+RTGgFeyRaWM7PCqT1RZAyyfrXT77FpZBSLvTp\nmgwzOX/6vkCqUMJQSCKQxoGawrFGSqNwHHoba7ytWJVEUj9LuknZC8N4li/0Skhc9FuS3BfE\nUn14slqaWyGBpMPlJB62LlJfs8MaaXfg7VuQpEGaz6W+F6JT/TAKw1EEjpgkd7ooyypunJgX\nQxakmkD6KaQFVQpvqQ5I1t/oYTTixZc9lzMVbzZenehF1QpmxQ5HDFI/GaCR4wWQmsatka6A\ndPCy09rWNM+ls0biH5X5/uqtwHTrzcgNm/8byVl8V2ukasQgBRNS7zDqhi1HE1iCiabODnUW\ncz0TxGHnWYcjm1R9Nsu9QmOWI+4K/l6JLTTRheLocg/0QGo6ILmdE7wikLgvtCQ1Hkj1BoqS\nCuxreFDNIwOSssuDo8rORSpXUB1FP/2EYGoGaZwil518gzqbyWTw8PDgb+tzx6qYIybJXiTX\nGOWcbanUGzbz2oC06nN0BaTGWu18kEx05MFyZHh5ViThtV6O8tnK6yDRr1r7IBknh9y6D+kP\nf2iQOA42DCa8TsK85EZkBVKh+0QcgyPYtGT3MlfpLTLJqV7PitUlkC4Nqs0VkBoJ2rsIUuOB\n1PrnSR/R3cRa7Vo3F1qGbHi/2J2MlRNsl3ZAwj5jFRqQElVxQUCK8iDFPtJE7/uSascgPVwA\nidMZbDZeEXLU1bEcZXk/eRz1a0KrE3fSHsNg43Hkd1YNElRkkHS4CtKFiBUTBF/2QJK2oEZZ\n+yA5d6FL5omPDVIUJ9NgwiBNxpL8RkgCSHFO/yLBvOoUNMyAI1NeqtB5YjIBCaEUq5Wu2Ccg\niZaPbqR7hdBwwSv50JLkDZOQU0uSRHuezDeOOyf6885hDB41nYmH8wUBpE0tkX2uZbK6wuiC\nghxJrCbfmkFi5ynDkev7aQSSgj5InBaEQfJ1W2xrFczRVZAkFWCPo5Ux3GmOpLNy1QmMLmpC\nOvNiU5rs7LXQ90HitDVlnyNRPQgkJsk2zQBSEHBWu1CvkQBSboL92l4BkGIpy5ZlfgPjEwqk\nleutFfWffGU52njj69rggn89X8uDI6npgSHxSAISG+nMlATPhp5rHsncknQVpBwg1Xp9HLGt\nYToOkTU19paD48/RjAsappM+SNE0okUUr5G0g4wFaY16kpUjCdtasDWY6b0X9rPbdVNqGlmt\nuqnIIOisXAdJ5ul2w8iwYoYmr4VakLrTHkDiX8Gbsu7OWo4A0trE6nObqbxJPXv5hwYJfisA\naRIgY0OoBxIGaURvsilTkzThalKVKeHQksQ28TSp10WBJN9uJVARSHk59ziyj2zNhR3bx9x2\ni1f3qj/FnGxQXycjzvHIFdB7HHEW3blcrgtSbobUfGPjkVz+hbqOpmxqiH2QPhNIWFnShMQg\ntdNYarqS4Uh5RQtI0YY5amnJJbkBg2RI8p/Qbuf5iFhZXQWp5cjzuvNAOrNnb+fYWR6LounV\n1eajF1hxyr0xR6YuEm5MtU3rCN7fd/rYIOWcjWsSjKMJDa/ejMwgYZ7iwE7boMgaCZCqPkgI\nO4rSWbVeF6SP8O4Iv80r6XI+Z5A210Hy+8UrxyjhWN9F6HQ8nU25l0sg2Tds7wNHfJ80IRTF\nxucodyR1QGKSptPLINF8xZqdVGzukTQa5cKRT9I2ioSj1g9HSOKKxn6WZSvzayCt/j1Il0jC\nv6aZXHs6jPzlmAZJLDe1NJkBCXsgPkgNW6LcNbv5+3qyvPDezeUHgjSiJfWEu4Wekah7jIJA\niijYBgVHQRSn1QWQEiw1ZgQSahFJ9VEmCSChSkHNIK27IDmSOiC55953WjVuLY3V7FyfOJ4U\nSK77zW19SwaJSVItwP2iuQJSbYoa6TUSgfQ5YvWWrRD+l4ikxIDUidSZTmk5E0Vuh6u9AdEu\nO1mWjczr+hpIKzHI6MDG16enlqMnV6LPiG1RN9zY9jz5HNmH89qsHUjaLdyAZOcqb5DR9nvf\nlv/BQcIW4Yg9YKhbTDRHxE+ESsMTJHeTVVIBkJBmFN4NcZLSULr1MsRFs3TGKZygr2Cs5SMA\nqZKaFOt2R0mkJakTId2On+fd/uxlETq6aKXGz9G2k0xTO44DVP3PcITCDt1ZVDjKI1ar+hwV\nxRQTsJBkvxN9/hwkrN/2QWo46ohAyi1IhiTmiAYfvcHF8gqOFguOLdZpllmsieTCGklI8mwN\nHAb78vIXx9EFkrBRq6dt0ZNXF0Har4WkRrayW9dF4khp+12O2GVc53w3v7Mvywvv3Vx+EEiR\nAWkCkrCXKJuIYSQgjSbxaoU0A2yjo+5AHJUEUsJDMnHkBlEPpEoq5BmFv8oq2cZZLVZCUpuv\nw/WRjagqZuY5KJCYo5YkHySVo20nIJn5qwcSq5a1MKWaAN7tGbLsWpDUGok+OuF4R70nwrF7\nQRImQpIPkgnfwxrJA6kASFLP7AJICwNSL3pXrPb4Gd4B292PvvVbdlz/8peWoz5JzX8O0p5A\nglvlrOyA1GSJVVLY9N0OMo4jSTZzPyAFCDEnzU7mJIlxY5IQ5wmQHuNiRSsCN5ATSGU55YF4\nZjgyJHFC8BYkZc5iE6rhaOU/kVaMQ7NR4Q4KJMNRS5ICSad52AlIVhPsec2KsaP2OWqYI3T6\nCyA5jlLOtSrvJ8xRABNnxltMPkcm3m2Uebodtmq3tsJmDyTxXJ35aZZZXAIf78BqtZXe3tmP\nNRwxSR2QVJnzvmZ3OjqCPI5MVY+ytCSZk2UOpNQfZEqun2B0vfsCaUIgTQJTkaIDUjgLAwGp\nqlyABUAqp1ECH/pyqxR7jrjIkxmTZGYgBqlsQYJ0OcIT5YJ9ei3U2sQvgGRJ4r/bJfWOSbIg\ncfiR5zUrIG1rf5OeOZIS7h5IsQ9Su1ftQAoz02RasZtOUzsjKWsD/OimU7eq9JrgVVzAQZIP\nEn7FwqSU844IR+jvyJjV6snPDqS/nI4+SO3eNlcq8G0NiAOTDfPV4tCezgOJyzuZA1kWJ7Ju\nNhy5sYc5MiCF0R2tkRpODjkObEGKFqSwBakASG7/egKQIlgVZqkHEkg6wNNutaJucx0kVu7a\nO8Dj4sqXnk1BtpDgl3p+6YPk5iTvt/DXLUglhx+50uVstQNIvWzWAAnPHV2+Fl8xFh8k5Ree\nJAYk5GxQLcYCkFL2WR1JqQaj2OEKLUjdXHQLQ5KHC/+MxQIcLRY+SFs7fwhHtuuLExCD9Jcj\nkYQFk21Ow9HZVPzw8jLxO7LPp1epGiTPEoIkMUxSKRxBP5E2Y44iw9FdgYTtooBBAkkx94rY\nvE8gocvAFbNyniBRMEkMSGnaaeDkwNacFYqRFYtWs2OS6pYkvfFnOXru2ObwQDlS4uV4PJw7\nHNm4zkskNUTSfuurFUbXoA5pOPJIshzRk1fGAYBUMUkdkHIh6TJIjSs9MBopGyhf3hk6/Vj7\n18Z6Txle1I8hkrjdroC023k2UPFLfQZHBz8CxYFkK+d4UxJHmbccWZJqXiNxrcG6BxJIMiC1\nFqo4JpCiqFAc3Ylqh3RcYSggTbD3yIoKv48N2SRkkDIEynK0bBPwWtqCZEkyjVUybAkmJOhR\n6ADyviPpD4P0/MLuqyCp3S+hjiIWKC/Us0HnQy9AAkg8QbXtyZ4NcMe5AFKTCkc0a3pmNuoo\nHCo0idI00CDlyZcvAlI2ZldvDRLaR0BKW5Ay4ciQ1Phbwq8rUaswLx19xY5E5nUPMfp9l0Fq\nEID/jPahsceWxDGtKRyd1ISkNg04W0MXpJrLJYGkpgNSY0pwZCU98VjvmSAIEill7xQkOH4H\nk4nZxBeQkKsA/qxBVEBHkV5hQKro3dmMEzwwSaUHElGU84Kk9VEtbeHLHkfXQDr4IHkbj6K5\nnL0iMPprSKQqHCmnFuFofQGkQiakrAtSk0nvRRmJQJXXpU9/gciU1PGFYaPdVHPkgSSN64Pk\nlvnEkfpBaIy1KcJsSGpbzKyRuiDZRdFx7yqNmSqHAtLpsNccOWuDBWmtWrrebm0Fv3xT155l\nPjMZAJkkTrDpKo1VDFJR3BtIcLPjCklwazAkyZExgTT9aTTiLlbbTkEIJRWBxEXGhCS1AUev\n6zxH6PSsk7yOzoGu3OPo+VlI6jqAmTWSgHS8wNHxrMuStd9CN4Ehq/Sc4NjTDj5u/TWSBPVy\nKpyLIKGsrqR1tvm48viLkBQbjlqSOOfWdNqZkBxIGLTiproGkv+DpM6Y/RltW+6hcYGk/f4M\nktol5wuvil4agPLkgcTz9xOcqvaKI2f+PhNISDm4nnNL850JSHSZNbtP1doMwga7EKtLeviJ\nhI6YQxVSr2KzhEnSg8yHBimcjEGSAYmT3DkDlAIpciA1QdiCxIUNS52yGBzlYsrtczTvz0iY\nc1Dx0jiAtd4Lxl9VkeQOWY4AEn3FB+loQdr63qTz+UJAWqtMfCyFyzPhSLI3zdMAg+Tl0Yrj\nkEGKi0sgRehYDJJsKfH7skbiSPN1JQVm3BqpNTx3RgaZDuRXKBs3OFpzJDCPPcoC+MLkvBwv\ng/T0JN6JnQnpCL+G45FQWSB0GRw5kHjiYz/EjbdIY46msnEQchGC3Gm31IMibs3QK4r5sUGC\nxY7mpMmYQYq9xLzhJIrRIeIAUW+uw4QEUllNdRlmd4aoqAAAIABJREFUJ0nJ5ZGIo3w2wwhn\nu7FwdAWkZ70t5ORgd5IOL0ffCdUqduejBE14WYQYut0FkDg5AmakbjR34fJM+Ds/zXTKJIXo\nEH4yB/rry5c8tiDZiL/GOG1OmaRUn8sYhMERQAJJ9g4QHSUbmgdeq6x9kKRUJ5Ssrff+WnNk\nSHqy9dckDYYmiaMobD2pDkgwQWwW4kChEN8IRzVzpMtxG46mZltgOtXuU5w1xjg9+KPMhwap\nrsdIbzgeTUaT2AdpTCAhGw4X8zMkNWyEKKsKjcfxxLrEaAIPzlz82hZePzYgrf4ASNa14eAF\nRrMQSGeerbpOeKy38Oe3WySIbC8kpc8qGBvYKq6dKYUk7h216vywuyD1RBNyij/M2gYkbiSU\n7ZYSN23sbKNJ4mTfboK3IK0FJIWycU2n7ntYHz1nIMZIrNz8/ta9j4wYLUj4lw8IOCdkP4ci\np0E6nttiFL6nL1p/v98sOBHtbj1vG3Rz8EFa2wKIYAQTEXOUTD1fO1o98aiENdRdgcQkjUbI\nedgBKWCQaLIqIkMSf0VAQpJvKDx6VkJlFCJpxnufF0BateLu4DpIr6LTH/rO30SSaH3dI1AS\nDXjEUdVeSfYJQZJdQesrmV8Au2ILUhw7y3ZglpEmQ2QsHCG35nj8+KiyOZgaAyBpxNUnZE3U\nqBlp7UiyV7ccoVK0555KSxb8AChYiP7YbOxNM0ctSG0bPHGNao4vwf95IB3lYB+kkwVpgdwn\nKrFdVQEk1GAsCKRcFUC0ICUEUuJyd5hD2I81toj7AWlW14/haPJIIHGyHK3ZMUjx+BH7SFHA\nxm45MCKQ4jxpObIkJQUqOsz6IFUVk3QJJPZGNZlOGi914ZGH1jaKwgsxknRcfZDwla3miC9V\nlpakuXDk2TtcSlBEFrUTkpCUiRuDFWmBEBmJQRJx1AWJUUIEP5Ek7Ylz55aktSWpvfrccMQg\ncX/mty1Hi0W5Q2LLdsXJHBFJPGR4iX+AyrOJ1DL1QW0KJiJJpviTI4nfx8csSZYjPhtNQYfD\nCtN0wVOShhyIhFNrlvRAEscGNo5jqL2TNdKsqEMGifdjvRmJx+BpTD2FuBCOhCTS9sqYQQo7\nIDVwbEWGpmK2MC5C/DZ06ywruNZLBySZiLbWLqfX2scTbyBdBglJcagLdN6H++pVkCrpf1ur\nMLVfs6ZK9rWzrg0xk9ThyJjtwocHXlUBpIceSGinEVuE2/a0fY1J8leWhaRmqar52mzmcGcF\nUJajnXBkSVosJIwfkfV+Bi1wxI52AtJJc2Tm/cPpbEiSAwLSebXarFa7hQ8SoZpxeW2Q5M2W\niSdas6O7ZLU3DhBfRQddXMjHBqlAWN/jY8C+dn2QpgyS5YhJwrKJhpu46IGUwupJINUzgAT7\nswvsqzK4vwhIyj1GTG9brGgwvGqr1YlAenpqA6M7M9LTM3ur7ry4tj2TRCDt+yDhJsy434HZ\nbZ75Tqs8qEYGpIU3Iz08BIEsqB8fHh70Gsl6Uo14o9bbxWXNjv7iQI7WkoxXSLqHoK212jNY\nCPBIEEv6W62OHGCmZpDYNqFah4g4PrPL6vHgV4pFsz5pkFy7MW5ncVpd+yBRax5MeW2S74DU\nJoYB5VB8kf8cAVbUhncBUsggBaKfhBYkIYk7DoFEtASzirMAGpAKAamA/7PiKEaxvwwGB4DE\nRmYHUia5RFfCkevFlqOtrGwUSCjMfBC1vrPDhIo+WEpLtDketj0AjpBqdbuDj5i/RqoEJNil\nOiDlV0CC97cFadGZkOhPswxAlhPFUV3bebsDEodQcuoDbpfWkoz2Y2fVjDcOsAdnQYLVjEHa\n+CAd2E49x/5tF6QjgwTPhoNXKZZBUiS1Xg1nJsmBpNdIaM/SgNRZWzqEYt/830ihgDwBSPnM\nC9T62CAhBjZ4HDmQ+PHzMXScaOJA4oW39JECRU0QWJEkUqtAJCaOHEiq3EtTQrFDcuuitByZ\nbgyONgLSzgPpaEBqdTt3z0+ipFiOfCdLSVm83cOZtq7aZUUmIHFfmHsgiT4fs8Wx9EP0GoCE\nHevFJZBka99kC5IDrM6aNRZ72undJ+GocBwZkgoTPpFlZgfONqgBqeYIBh+kA0BaLPZSQkIb\nGwiXZ95m8/ewG+N5Z0nS/kHnEw1L7P29WhuS5MAGH1rPYD/KeWdOc2QMCYV42rXO7wxSXgtI\nfqTWhwYpZPv2iLNVh+FYsp9YkCJw9EgcBUGlnDPHRUkkxXERJ54hV6L/0CHrWe3pfCVAqrEN\nCpB0P6auwCCd1j5IO6TYEg3kAkcOJFMJyRyyHAEl4kgtKzIBaX0JpNRx5E9IJHWZISdMB6Sq\nwiwuHJliFIakqjZ2P8zbqSHJtbXjyAOpKTiejzna7XSzrdm1m/BGEVm9RmKSiKP9Dn8wSa5x\nnr5yxvTOHjZMNw6ks8Q+tiTB1nBkXYFJsl/ZbMRplX5UJ+zJRJlPoY9wiznVjkFCiCTSjs7u\nB6SmgDMLCvY9jrpZhJqxgIQJCKulFqRxWdISAd1P4r0EJET/icWTWr6U8B87WSG6lkEqGSQ2\nhfOBI+y6xNFhDYcYBdJub3OwiubijOOSad9Yefeeut8okHa1HsRh6qjKSqzIPkg2DiCv112Q\nQlKruIzYYjFXE1LFJBmQ4g5IkU1+MrJuvu5sAtKq6oCUojpOlU3zfO+DxElPNrxPSg2grHbN\ngcu87Hmx4y1daG75ajgi8VzmFUeINNFTEkDCfCRJ99wSlv4Q7++67IJkOGLFPtb5LNiDnnMh\nTePZ3YE0MiCNOyCNL4IUjc2ARDOQjpuEuQsJwen7+6owXqvmMiuOU6ePL9arjZicbPKT3Z5I\nOnF/2GuQWMk/2JSP7TYTQHoxm7U7l/BYTrZ1IO22PkgyEXAv2S7miqPExgEQSGv/wYfxgqbS\nKZK/zLmSrHkfDAThJZCIJOHogRrVq0XRJHlcCke1sXxYjkqAlE97ILGqRTdcr7u57RtJt8kg\necY0+AF9/Wpb0Q/iUhydhKMWpJMDyYvdZZDWHL95ASQsAkktjXWGJXAEkDCLhbPZ/ayRSENH\nVoaHh/Hj4yTpgUQ97PHxYZQ5khpxb86yVEAqVMMDJOqPNCkh77bv/b1iGxfB5bxQrB8K9REi\n6YSRdbfft0a7g6yWD8dnx5GrnYUSY8QRkhN7IG23G04QCc2l1CBx7gX0Xxpyt9tqoVIWJyZD\nW8FmKd5JM0fCOFlwHnzmSHcJcBQKSSMTcW3bs2KO4i+jkWz8Ww804mgy4ZIcdVkJSXIgg/88\nzUhwWKP7bpeW8ClgkMoeSDYBtAGJVzd8gD1Tv+r5SJNkApIAkuf8fYSOzNh2ScIaqaoIpMJP\nDZFakJJZ7IMUczprOIiFAGmmFeUPDVJTJALSGCCNdabV0YhBehgFGUia0aOGJZNBSjmvvg8S\nUpMySNT8Ve6BVKyw61KWDNLKAwn9Y388Y0rasLHgKI+3BenYBUmKXgpIXoTFFko9OsgGAbJq\njVRnzBHNn+DIq4lJ2teMOQqzbF1myqMOlWOlLmbKINGIY79UccIGmqnhihgrjjgOPSGQvoj9\n27ZlIiBtihlAqpTTKkAq0zJDhDq1XOtRSxwR9tCSeY20U16uNEsBnsXxDJCUnnrWJHVBkqP7\nHkhi/uyQJN/Ak4TQPRFH7eZXmmYp12JmjpIeSMhCEIZZJ7/YxwaJWiMgkEbj8QjqC0gSFQYq\n39SClIauUTDtpCaxfjzz2l1AShCgXGqQYMyTVBjYZPRmJB5oecWDp6iGSbEznA7niyCxHYJB\n0qnd8H0HkgTxCMh1ljNI8FmtKp3eqMmpM/BGSJZtNpmyV3MJ5owDHRkk1oFtqwEkaLwjJil2\n74tZGDNSPNWzOym8DFJEOk9Z6TCKrEjLlEDKuOTUvrXeMEi1BWmr/cU3JrCLloouHkmR1Hy9\nBtLTk+y0aReh09HkkmSSVh2QKguS558ss/hUQEqiqF0jCUik8U1DTkZwTyCNggAgwc1botcs\nSPDrRaGfIEjDFqQmngAkjnlNZzPV7DQj7ZOaVkh7hPqJlxAf4FReeSmTxEp7GB+hre/2/Pzn\nNO7uW8cG4HGGafYZn1GueEfkPzT87Dw7bwvSZnPwff2Y46oGSDq9EZKGzpj/CyCFaUKq3nRM\nIM2FI0fSa8AgJfImLSTb5oyQqIFA4q5mw3SwcozrGiAtiCQ9lEhUFynEstZyVtCFgLQCM4Yj\nN4sLSNBjOyBhY+7561eO1Dr1El0YkNBw2tXuxAkibYZI9UCrCiBlfUd/ASlKARLSUkS5l44r\nLlDSnHOFQjG03/rQIM2oLwRj5ohA0huIxFEgFUrAkQYp0SCBJNNUAKkmkOYMUtl216KSLoLH\nD5BAkij1DNLBgmTs3+bWDtaXtQvS8XSy9t2jp9vxvscGgeabgw9SmedzAmnF/jkeSGk6i02N\nGg+kKf6/xpJpzDOSDxJK9jmQHh8dSQVAiqLggUFqszlkBFLAtvEZjS+lxiIzJBmQMrubQJ8T\nFQ7/64GEHk/N1QPphSair7zNTXR0OIK5QUA6ejMSByS1qVY1R3vmSEhScwuDRBwlxYpBUi5C\nJqsdgxTpWMCPDtIMySEfAdI49EAKBSSarIIEQdUqWRBIKqMIuZjQxLZ9qUOmACmZ07s2QJX7\na8UdhMOReEpy5trjcWVAOp3mW89FqHllKzdck82UZUg6ihMe79b61gaUvMRSi0E6aI7wbOdw\nUTIgoX/yEfRh1u2QzNyShAPTqQEJuZzTOPRAegBIQSEgPT46kuDeG02iIPjy5SFO1I4BzYgB\nQKIlOPugK44q8YaoGSQ6aNdbNCPVG9e3/ZhaVKiA0ze03nWXoxf2u2JtzUsPyVY74chL2WDS\nbToi7cnYRWgvz505avdjxTUXINFMajgyJLUgTSPPyn83ID1cACkGRwAJG5q8b4gj0PxThFYg\n052a8gUkmu9ncCdTIBXVxoHEJDlrHny89uLzf9h2QNqaV/Tw9Rrp6el4Eo6eFEcGpN1+s94c\nBKRDuxCSh8sgbQqzVJfewj8B1oYUj51IsmamKUiq50gMwyDxcmhkLOAPD5/RPEVGb9UeSHE0\nIZCiL4RaIqOygJQRSFNwJFG3nh8iQKLXRNJit3NMrFYmDJLeI6V3Xrcc0QAiExIM2bSuAU58\n4IVAegFIZ4B09hKtnsX8vZdgfG21OwlI7CKroGSQ1jIh0WgzU+MfzJEWJJqSNEgRaXa5IYmT\nvSR3AVJEeLDT6viRBlmd4QMgUY8gjh5GsF6p0YWX0QKSpzwDpKROhaNcaVAVjawCEjuAb1pz\nHisUu8PzSZy2NUhbCXc9sm7ngfSVoysYpIMHEpF0Om0OskaCtK527DhQzWWvRI27DFJsQNqY\nwi78HUBAqhjKGE4AUsA+INJqzFFEw0txCaSUQcKM5GIy5piRptM4zFaWo9YznkDi1wlzZFW7\n1UZA2u3qkqCp2xkJu7AbgIRJZ6NdhF6eCSSkvzgcLUeOJAOSaVW9jyRh+7CnIvTJWdkB0ppB\ngu458zZ+02mCOlCwxnhWu0ZiGQ1JzNF9hFGkmGgC+AcxSDAw2AwfAQJDCSRC6TH0QKL2K6cV\nq3b+KhSr9pImpCKrFhYkvA98OENkVS0sR0LS+Qk9Zfd82gMk5dnQbLcHFE3AM36hiUecxHBA\nQAJH7CKjQdpu92La3QhJauLheCg423VAoiPs15TnUS0c2TCKSEBCNWqUE+CqHAJSHBFHQUSz\nC3bhatHtROmrCSSoPcRRHE9VuG2eIWwWIDmOTNvQE5BXWW44EpIIpI3hqFyBoxpFbnEEHG02\niw1A2mz0HtPz0+n48vzMtJx9kE5nE9gHtbjDEeeRgT2VOXK0VDXn0GfP/8IDKSOQkinqbiQc\nbOI4Qt4T2VniRObetuTHBolIQj47aPoP/jogkBBzAulxFob4oOUoIo7CKsqyNCo0STCMl1ky\nq4ssX+gqjgxSKSAtNhqkRkA6EUicMqd1/jYg7dEfdjvuAzb7wNevFqS9PyEBpKMBabst9MTD\nOYyqSrZKNEgoO4jpihbPXLHPdX0AJX4K8OWNwdFkwl+Jg8/IUsYgFUgGCY5Mq9Ws8MZdkOoc\nO1L1BZCaajafo7EyBkk5L8qNCkcEEn9DQFqjEUknPp2OGy/RM4F0Oj5/PbPF86RBOpyMZ8MB\n07lX1NDmkSG9d2sS5Qku9fpAIBETxFH1n4GE/EHgiFZUcXBfIKXIZ/fAbkI+SOy0GhFfj7NZ\niNWlXTZGAXEUEEhx5IFEHBUAKWGQVMbipnAgET8bHXTTbFZ7AWnPIK2dY8N2e+S1MXFEGpwK\nmj4fvxJJBBJRdvBLbW2xoSsgEUeVp8ERRwUbG9ZlrdZIXL8zS3NaJtUbGyDLfZ84krjx8RiB\n5pOJI8mBFDJItbfDVBJI0RQgcb5iORlzMsWKK8LUru2G2ayew6UGGwS7w0WQMNRo001DjYi4\nLvq1Ox8kJGw4MUhsbdCpvw1I1GbYRlLfOXPOBvb+xnJLbA7gpV7v9msEd5Qpp3L3QZpKzlBq\npUjlbKAesVoYjqJ7AilKS3QRmBTG3B+U2z88nKePjz89znhyr63yTiBRP6qqnHpd2gGpIJDy\nej/LveQnRbFisy7nE92sdXVzbMLuNlKLZc0ppuRRHfZbHin3J+Jo2wXpBUEBkuXEd1oFSEQS\nOAI2dBmrwRFH8JiF75i22uXwF4vSzIAUub4fW5CIoyBONUhNMPocpDGRNCtyLG9gcjB3gGTO\nU/rPw8iB1DBIRAqBFM1mnL21hcKANGOb3eGo3ek3ksTnIkg1g8TuVT5IT6enr1/PAtLTc5uy\nmFM2YOY/nI4XQVoISO3oQyuzA4NEz3dD06iaktJ0Gk8TTEucoEKDVC4YJNKFo4xIQnYy29k+\nMkg0gPKM9KA4MiSNJa7vJ9LsZgE4Mj5gSQiQYmph6mu5qsvHIKGGIzpNUai6wvQCA1stKa5X\n0sHl+gCJ1jMAiR5kq7of2FsIugiDJEEAPFkRSC8ngHTYtyAZkgxIu812B5AYWL5QVjiQNjNv\nRmKzCYGUXgIpoTeYo9gDKf/y5TNWU9mM1tT+fhUS45FKR5P7YzshASSa+gSk2aILEul9sxLe\ni4sjwvictWHFOmhZrRAhexkkTOSdGenp+ZlmbMeRJQktRm8JSLsLIM15ul70QEoZpE2ZS/4V\nA1LMu4kJO0TpLEIEUuhAynQq9Y8NUlQgopxAGsOoMFERsohQCqLpTyOodkFQW2/KJByNx7TU\nzqIcRht2HxGdL0YRpdKCpNdIDBLPAzRewXEZ4Z1yAycsmg57OPajZ7Qkid/dmZ1Tt3sOSzNa\n3/HlxGtpBslz/0YQE+kjW+p4u0InTa5ShENxeYpN6a2RsiSbRejESVh5aySacKKEXodj4oj6\nDVaSY7NGypFkNU4jGh0eH0lhpTWSWe8ApHA6JZAeYwVSgyDTMKgBEljRVBBJWVog9W++1SDZ\nEMiyYr8djet+sabGhGa793M2nJij56+S+eRZzeMHBO6f2PpNup3nA8tbdfvjfLFerRd7H6RD\nRY8TuU/KPBdHPAYpSeMknEK/y/EzVV67GCDFRQbrTDdx7UcHaRyMvzxiH6kDUkgghT89Pnwh\nkDIXqCcgRRGnUkyytQTM4VySqTbF0A6FX3cWAQluzwCJU+AISafzgiakPc0wu42ZkAQkuISd\njidsMRFjWzUjNSd42hmQ9kcfpMNxvbYgVR5INSknZW1Bcjof3emMVjVJmuTVZlMpqx39QSCF\n8cSA1GZmiCcAidQ+Bumxaj0bqjKJkOgtCh9jDVKN+ktBQjMeg1Rw/l9rbMgJpJpBWh8Xytdu\nU7NJQYI/JOGqPDTov3VNrXVifVhTceIJ6SwTkk4QCbK4gPW+B5LEI+3nCyQ/2e/dGFOVmO8x\nLub5tkT6E+c9xCDRP9Np3vFsiEtaCOZFNo1pOfjnB+kbSftqef2DBNLjOLQg6ZwNNOvA2e6n\nxy+PszBIHUg02I7LACAhAWm+hhVKrDw0MhURLMk1afyzXCsjqwWtEOD1jBQ52vv7dCSQMOEc\nSdf3QTod8J/j+RkKveoSx6MC6XA4dkGCh9CaQKo9kDB1kmrHIHk6X5ISSPAAz6pNVQhHLUhT\nAWmxCFSuoBjmhzgKqMeMLEdCUlVRB0oSahkskVrLBYEEHzxojnNipZA82mLRzGtS66hp0ozG\nfA3SmkEipapae5tFHkgbP2cDg3SWbFw9kGyg+b6TcvOkQWpnpApnNiDRjKTLDCJVQwiLXZFP\nPc8Gemc+R4JIjEj5nx6kb+4fluX1Twb1mEFi1S7SWYQIJPpv/IVAIu2v3TGKwmDCINEaKUly\npSUpkGjxnM81SLSWLspqcQGkFfV7Bmm9W4t3Pz/gPZuXoMM9Y03tg8T7SJdA2lmQtnsfpMpm\njCCQSnWA1khpakFaVyihgK0jHMGfDNIk6IA0Hf8/bLIFYZ5eA+mBvb9bkGjOIyyx9zafF149\nsWpWldilg3rpgbQwIHGouTftwCyJBMMM0oYdG0gxxpEndrD7DkgnAxKvLm0n4IlKQNopkBph\nlEAnkKrrICU+SAVA4mwNeSlDyZ83QvYPgFQySA+XQMJSKP5p9OUx1SAFYZCUQRjlYRZHXZBm\nUc4gZRdBKudVHyRspx8EpA0HbrreckAyQ0QD0HsdkI4tSAcfpAPnPmGQ6h5IhYScdkGKAdKM\nQVJDKKGSAqRxD6R4/JlBCgBS6oFUxvEEiRoeHgmkqQIJM1JlQPJCzRmktKKZMYPbbAekGiDV\n2wsgMUm8Y8Y7cfIIYJc7PR8l2YW/Rjpw1i2Q1El0ISCd9/DdWu12Hki4LPzmL85IIIlBSnyQ\nQuzHGpDaCb754CAVpNBZY0M08UFC/joYGwikwKl2QTBWIGUdkDhNbV0wSPN2jbRGNHVRzpmk\n1ar1dsGO+mp3QPj4erHbaI3DI8lPvmtySx26vnYNQNpwPBKRVFWtmb2CNiUgrT2QENhHayBk\ntSSSSg+kMMUaKUbFvi5In6kbRQJS5rsIEUiRgDSN3IBMIOXBhG4gyhYdkDjUHCBx/L4hCe8D\nlQ3/t95u/bKglqR6YUhatVEUvBZikp48q50jyaaMUb2ASToLSe0+ksnVzwEogKlqAyyQNygS\nkjDzKqsdkRRakvJ8yh5XNkr4zwzSkuX1qoQGpMcHWiPFFiQcYZBiAmkkIAlJr68WpMwDiU8W\nRQApA0iOJLnMGtHUOaIHHEnmBkASNLfjcbsQkuyt7YWkA0eovXKXMEcukWQO7SxIRFLFJFVy\nwJAkIAlJcgB9InUkGZC4aeDOgTo3bG0IhST+CkD6TL0oyIkkBxKOdEGSAZkOWJCKfLGYl6VZ\nI/HZGKSUk8ZYkuTOZCdOQDIkqbbZyE+wJNnmPBvX75Yk+x00krwESXuvG4Ck19cVn5E9G+Sp\nKZJKQ9KrazXOu4WfN7UgSeNwuvNCpiQmaXq9+72+Lm/EynflB8xIUYF5Z/Lw+MggTbTTKtLq\nT2M4wDBIIKlGjebxJAmiMItC7CVkajhsItlXrAtLkguOZu/r3EZYt9+QzHayBvKSPEhwUesQ\n1rTJdyFC0hPviDTe8Lq2JO18J/7XgqdH9AxDmDlgSEolSKRdHKMl0tiSxEUNx6Zp4s+KJH9G\nAkkM0iNcVlsX2JRAmmAogsIpJBkbHECyJOlMizQnMUiGpK2yDnxHHEmnXpu1ohJHXpA2WsJs\nwFqSdL7yxKwAm8glbXDH2OXDkqSqgv6ZZySW5Xc+eoEke6hHkuhpF0hqT6dIyrzrtCRdugsm\nqfE4MiRdvXEdAuhJhyT79qs7vG78eDNLkiT8aY1MjqSQSNLlBQxJn5mkvMl0YF+fJPOVLklt\n1giPJP+3aJKutoQnr+wjf6Vp/rCsVdUOjyMW40rFBjrFkSGp0HlP5N4uXGF5k/v8N/IjQOqR\nNGkPCUnTliTztpCEnSROZKXP9upI8gqQNYakf3PfHkfN6/c4+k5naZ/+1fBMfUBEJc5ywuS4\n7qA4orMRR+5FpkLNxV+qJSlqv9MISY1iyFy7lNmwx5GISpX+7+VSZ/2/y390Nv9HWun3gA8O\n0m2b/pYne4M+8TZnu+dGW970CldkAOk+znbPjba86RWuyA/wbGiGPvH2Z7vnRlve9ApX5Iek\n4xr6xJuf7Z4bbXnTK1yRAaT7ONs9N9ryple4IgNI93G2e2605U2vcEUGkO7jbPfcaMubXuGK\nDCDdx9nuudGWN73CFRlAuo+z3XOjLW96hSsygHQfZ7vnRlve9ApXZADpPs52z422vOkVrsgA\n0n2c7Z4bbXnTK1yRAaT7ONs9N9ryple4IgNI93G2e2605U2vcEUGkO7jbPfcaMubXuGKDCDd\nx9nuudGWN73CFRlAuo+z3XOjLW96hSsygHQfZ7vnRlve9ApXZADpPs52z422vOkVrsgA0n2c\n7Z4bbXnTK1yRAaT7ONs9N9ryple4IgNI93G2e2605U2vcEUGkO7jbPfcaMubXuGKDCDdx9nu\nudGWN73CFRlAuo+z3XOjLW96hSsygHQfZ7vnRlve9ApXZADpPs52z422vOkVrsgA0n2c7Z4b\nbXnTK1yRAaT7ONs9N9ryple4IgNI93G2e2605U2vcEUGkO7jbPfcaMubXuGK3AykQQZ5r3Kj\nPv5duRVI35flD7nK/0GWb30D35HlW9/ANVm+9Q18R5ZvdeEBpHcry7e+gWuyfOsb+I4s3+rC\nA0jvVpZvfQPXZPnWN/AdWb7VhX8MSIMM8sFlAGmQQW4gA0iDDHIDGUAaZJAbyADSIIPcQAaQ\nBhnkBnJTkL6R6P+nvxr//y8e/O/LH7mxb9/aT73lvXl38Z4a7dKNDY12S5C+mX++tS/sD/nW\n+5D35n9Z/uCNNT+0u165t/7tvJNGu3JjzZ032n8VpG92nFBt3Dv4A+SP3VjzI7vE1Xu73Cfe\nQaNdBem+G+3mayRvnLgwWF04+GPkD9zYD759qIE6AAACy0lEQVSza/d2sb++h0Z7c5Ds9d5R\now0gNfqvt7ix5r31CSXv9saa93ZvtwbpW/NO++u7vbHm6r11/ng/jfb2N9ZcuzdlbBhA+q/I\nu72x5h3313d7Y831e/sYM9I3/x/vR4k18o2a/o/c2Ft0iaHRbnRv+OstGu22IKnbfl8D/x+6\nsR/cJ67d27fOR95No128sXfRaN6d/IlB0g39rkD6Yzf2Y/vEtXv71v3Me2m0yzf2Lhrtg4Dk\ntrffm2fDH7yxH9onrt2b7yvwjhrtyo29i0b7IJ4NgwxytzKANMggN5ABpEEGuYEMIA0yyA1k\nAGmQQW4gA0iDDHIDGUAaZJAbyADSIIPcQAaQBhnkBjKA9GbyyWv7f/i77/zy0/B0/jQyPKo3\nEx+TDjT8cgDpzyPDo3oz+fcgDfLnkeF5vYX89sunX4WV//n106dvfwM4/PL3v3769Nff7Uvz\nv1/pw7/9/OnX39UHBnlnMoD0BvL7N+LkV2Dyr08sf7Mg4cCnn32Q6IOf/vkz/fNX9YFB3pkM\nIL2B/O3TL83vvwCTnz/9s2n+10LT/J2QoqP/UGsk8PNPoPZPvGo/MMj7kgGkN5CfP/1G6p0s\ng377199/cSD9zG+RJqdB+g3//C6v2g8M8r5kAOkNRBDif38R3c6R03mpzXfyyn5gkPclwyN5\nA2lB+uunn//xr98GkP78MjySN5BWtRNTXUe1g1wB6efheb1TGR7MG8jfP/3ye2NWRv9jzA7M\nyt9gS/jnp1+ugtR+YJD3JQNIbyCt+ftvSpf7Zg58+vS/5mUfpPYDg7wvGUB6C/ntV7sh+9dP\nn375H/z1D5DT/MavG/OyD1L7gUHelwwgDTLIDWQAaZBBbiADSIMMcgMZQBpkkBvIANIgg9xA\nBpAGGeQGMoA0yCA3kAGkQQa5gQwgDTLIDWQAaZBBbiADSIMMcgMZQBpkkBvI/wfjROXgLRZp\n0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(datetime, count)) + geom_point(aes(color=temp)) + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2330ec-affa-44ed-ac14-ad845d0bc42d",
   "metadata": {},
   "source": [
    "Переглянемо як час оренди байка впливає на кількість замовлень:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3cd8070-41ba-4958-9d5d-4d730087fef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SR3z6bdTbUl6WFTbck9m3Yb2E8W2Lhi4Q9vrkOqM6HE2CDvyeXdubwnNzE2qEvN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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(format(datetime, \"%H\"), count)) + \n",
    "        geom_point(aes(color=temp), alpha = 0.5) + \n",
    "        theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1664fc-f951-40a8-a4a8-6ec9d368a03f",
   "metadata": {},
   "source": [
    "Also, we can add new column to our dataset and use it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e71c4039-98ce-489c-9a8f-43dad9216830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2011-01-01 00:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>81</td><td>0.0000</td><td>16</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2011-01-01 01:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>40</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2011-01-01 02:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>32</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2011-01-01 03:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>13</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2011-01-01 04:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td> 1</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2011-01-01 05:00:00</td><td>1</td><td>0</td><td>0</td><td>2</td><td>9.84</td><td>12.880</td><td>75</td><td>6.0032</td><td> 1</td><td>5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 2011-01-01 00:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 81 & 0.0000 & 16 & 0\\\\\n",
       "\t2 & 2011-01-01 01:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 40 & 1\\\\\n",
       "\t3 & 2011-01-01 02:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 32 & 2\\\\\n",
       "\t4 & 2011-01-01 03:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 13 & 3\\\\\n",
       "\t5 & 2011-01-01 04:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 &  1 & 4\\\\\n",
       "\t6 & 2011-01-01 05:00:00 & 1 & 0 & 0 & 2 & 9.84 & 12.880 & 75 & 6.0032 &  1 & 5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0000 | 16 | 0 |\n",
       "| 2 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 40 | 1 |\n",
       "| 3 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 32 | 2 |\n",
       "| 4 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 13 | 3 |\n",
       "| 5 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 |  1 | 4 |\n",
       "| 6 | 2011-01-01 05:00:00 | 1 | 0 | 0 | 2 | 9.84 | 12.880 | 75 | 6.0032 |  1 | 5 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp atemp  humidity\n",
       "1 2011-01-01 00:00:00 1      0       0          1       9.84 14.395 81      \n",
       "2 2011-01-01 01:00:00 1      0       0          1       9.02 13.635 80      \n",
       "3 2011-01-01 02:00:00 1      0       0          1       9.02 13.635 80      \n",
       "4 2011-01-01 03:00:00 1      0       0          1       9.84 14.395 75      \n",
       "5 2011-01-01 04:00:00 1      0       0          1       9.84 14.395 75      \n",
       "6 2011-01-01 05:00:00 1      0       0          2       9.84 12.880 75      \n",
       "  windspeed count hour_rent\n",
       "1 0.0000    16    0        \n",
       "2 0.0000    40    1        \n",
       "3 0.0000    32    2        \n",
       "4 0.0000    13    3        \n",
       "5 0.0000     1    4        \n",
       "6 6.0032     1    5        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- data %>%\n",
    "    mutate(hour_rent = as.numeric(format(datetime, \"%H\")))\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfc213b3-154f-4adb-bd0c-ceec5cc7d6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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y9IpyadMM6qZ+w5Np0/3hN7UG9yDt4n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UIvJWi+4nSxDkiZ6QjWdlSSYp8kRPiGQ4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\nnnvbm7Bqmtr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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the same chart as before\n",
    "ggplot(data, aes(hour_rent, count)) + \n",
    "        geom_point(aes(color=temp), alpha = 0.5) + \n",
    "        theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897f6873-a9c3-47bf-9bd7-76c28d4163ea",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11e412c-9d2d-4c3b-88ee-59aa2e428ab1",
   "metadata": {},
   "source": [
    "One more splitting method is based on date. So, we can use first 70% of dataset as train and 30% as test. This method often used for timeseries forecasting. We will use it for linear regression for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a425866-d914-49ea-a906-47031f80af6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "10886"
      ],
      "text/latex": [
       "10886"
      ],
      "text/markdown": [
       "10886"
      ],
      "text/plain": [
       "[1] 10886"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "7621"
      ],
      "text/latex": [
       "7621"
      ],
      "text/markdown": [
       "7621"
      ],
      "text/plain": [
       "[1] 7621"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "3265"
      ],
      "text/latex": [
       "3265"
      ],
      "text/markdown": [
       "3265"
      ],
      "text/plain": [
       "[1] 3265"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(10) \n",
    "nrow(data)\n",
    "\n",
    "train_count = ceiling(0.7 * nrow(data))\n",
    "train_count\n",
    "\n",
    "test_count = nrow(data) - train_count\n",
    "test_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6a154688-3bbe-46be-8005-048c0dd81fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data <- data %>% arrange(datetime) # sort by datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "84461a9f-411a-4065-ae41-dcae120e4386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "7621"
      ],
      "text/latex": [
       "7621"
      ],
      "text/markdown": [
       "7621"
      ],
      "text/plain": [
       "[1] 7621"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "3265"
      ],
      "text/latex": [
       "3265"
      ],
      "text/markdown": [
       "3265"
      ],
      "text/plain": [
       "[1] 3265"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data <- data %>% slice_head(n = train_count)\n",
    "nrow(train_data)\n",
    "test_data <- data %>% slice_tail(n = test_count)\n",
    "nrow(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fdbd40da-b418-40fe-84f3-fc02b35c581c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>7616</th><td>2012-05-16 16:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>29.52</td><td>32.575</td><td>45</td><td> 7.0015</td><td>446</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>7617</th><td>2012-05-16 17:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>29.52</td><td>33.335</td><td>51</td><td>12.9980</td><td>873</td><td>17</td></tr>\n",
       "\t<tr><th scope=row>7618</th><td>2012-05-16 18:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>29.52</td><td>33.335</td><td>51</td><td>15.0013</td><td>846</td><td>18</td></tr>\n",
       "\t<tr><th scope=row>7619</th><td>2012-05-16 19:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>28.70</td><td>32.575</td><td>54</td><td>19.0012</td><td>590</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>7620</th><td>2012-05-16 20:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>27.06</td><td>31.060</td><td>65</td><td>16.9979</td><td>459</td><td>20</td></tr>\n",
       "\t<tr><th scope=row>7621</th><td>2012-05-16 21:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>26.24</td><td>30.305</td><td>73</td><td>12.9980</td><td>393</td><td>21</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t7616 & 2012-05-16 16:00:00 & 2 & 0 & 1 & 1 & 29.52 & 32.575 & 45 &  7.0015 & 446 & 16\\\\\n",
       "\t7617 & 2012-05-16 17:00:00 & 2 & 0 & 1 & 1 & 29.52 & 33.335 & 51 & 12.9980 & 873 & 17\\\\\n",
       "\t7618 & 2012-05-16 18:00:00 & 2 & 0 & 1 & 1 & 29.52 & 33.335 & 51 & 15.0013 & 846 & 18\\\\\n",
       "\t7619 & 2012-05-16 19:00:00 & 2 & 0 & 1 & 1 & 28.70 & 32.575 & 54 & 19.0012 & 590 & 19\\\\\n",
       "\t7620 & 2012-05-16 20:00:00 & 2 & 0 & 1 & 1 & 27.06 & 31.060 & 65 & 16.9979 & 459 & 20\\\\\n",
       "\t7621 & 2012-05-16 21:00:00 & 2 & 0 & 1 & 1 & 26.24 & 30.305 & 73 & 12.9980 & 393 & 21\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 7616 | 2012-05-16 16:00:00 | 2 | 0 | 1 | 1 | 29.52 | 32.575 | 45 |  7.0015 | 446 | 16 |\n",
       "| 7617 | 2012-05-16 17:00:00 | 2 | 0 | 1 | 1 | 29.52 | 33.335 | 51 | 12.9980 | 873 | 17 |\n",
       "| 7618 | 2012-05-16 18:00:00 | 2 | 0 | 1 | 1 | 29.52 | 33.335 | 51 | 15.0013 | 846 | 18 |\n",
       "| 7619 | 2012-05-16 19:00:00 | 2 | 0 | 1 | 1 | 28.70 | 32.575 | 54 | 19.0012 | 590 | 19 |\n",
       "| 7620 | 2012-05-16 20:00:00 | 2 | 0 | 1 | 1 | 27.06 | 31.060 | 65 | 16.9979 | 459 | 20 |\n",
       "| 7621 | 2012-05-16 21:00:00 | 2 | 0 | 1 | 1 | 26.24 | 30.305 | 73 | 12.9980 | 393 | 21 |\n",
       "\n"
      ],
      "text/plain": [
       "     datetime            season holiday workingday weather temp  atemp \n",
       "7616 2012-05-16 16:00:00 2      0       1          1       29.52 32.575\n",
       "7617 2012-05-16 17:00:00 2      0       1          1       29.52 33.335\n",
       "7618 2012-05-16 18:00:00 2      0       1          1       29.52 33.335\n",
       "7619 2012-05-16 19:00:00 2      0       1          1       28.70 32.575\n",
       "7620 2012-05-16 20:00:00 2      0       1          1       27.06 31.060\n",
       "7621 2012-05-16 21:00:00 2      0       1          1       26.24 30.305\n",
       "     humidity windspeed count hour_rent\n",
       "7616 45        7.0015   446   16       \n",
       "7617 51       12.9980   873   17       \n",
       "7618 51       15.0013   846   18       \n",
       "7619 54       19.0012   590   19       \n",
       "7620 65       16.9979   459   20       \n",
       "7621 73       12.9980   393   21       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2012-05-16 22:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>26.24</td><td>30.305</td><td>73</td><td>11.0014</td><td>286</td><td>22</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2012-05-16 23:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>25.42</td><td>29.545</td><td>78</td><td> 7.0015</td><td>133</td><td>23</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2012-05-17 00:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td> 8.9981</td><td> 79</td><td> 0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2012-05-17 01:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.030</td><td>83</td><td>11.0014</td><td> 28</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2012-05-17 02:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td>11.0014</td><td> 16</td><td> 2</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2012-05-17 03:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>29.545</td><td>73</td><td>16.9979</td><td>  3</td><td> 3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 2012-05-16 22:00:00 & 2 & 0 & 1 & 1 & 26.24 & 30.305 & 73 & 11.0014 & 286 & 22\\\\\n",
       "\t2 & 2012-05-16 23:00:00 & 2 & 0 & 1 & 1 & 25.42 & 29.545 & 78 &  7.0015 & 133 & 23\\\\\n",
       "\t3 & 2012-05-17 00:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 &  8.9981 &  79 &  0\\\\\n",
       "\t4 & 2012-05-17 01:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.030 & 83 & 11.0014 &  28 &  1\\\\\n",
       "\t5 & 2012-05-17 02:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 & 11.0014 &  16 &  2\\\\\n",
       "\t6 & 2012-05-17 03:00:00 & 2 & 0 & 1 & 1 & 24.60 & 29.545 & 73 & 16.9979 &   3 &  3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2012-05-16 22:00:00 | 2 | 0 | 1 | 1 | 26.24 | 30.305 | 73 | 11.0014 | 286 | 22 |\n",
       "| 2 | 2012-05-16 23:00:00 | 2 | 0 | 1 | 1 | 25.42 | 29.545 | 78 |  7.0015 | 133 | 23 |\n",
       "| 3 | 2012-05-17 00:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 |  8.9981 |  79 |  0 |\n",
       "| 4 | 2012-05-17 01:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.030 | 83 | 11.0014 |  28 |  1 |\n",
       "| 5 | 2012-05-17 02:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 | 11.0014 |  16 |  2 |\n",
       "| 6 | 2012-05-17 03:00:00 | 2 | 0 | 1 | 1 | 24.60 | 29.545 | 73 | 16.9979 |   3 |  3 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp  atemp  humidity\n",
       "1 2012-05-16 22:00:00 2      0       1          1       26.24 30.305 73      \n",
       "2 2012-05-16 23:00:00 2      0       1          1       25.42 29.545 78      \n",
       "3 2012-05-17 00:00:00 2      0       1          1       24.60 28.790 78      \n",
       "4 2012-05-17 01:00:00 2      0       1          1       24.60 28.030 83      \n",
       "5 2012-05-17 02:00:00 2      0       1          1       24.60 28.790 78      \n",
       "6 2012-05-17 03:00:00 2      0       1          1       24.60 29.545 73      \n",
       "  windspeed count hour_rent\n",
       "1 11.0014   286   22       \n",
       "2  7.0015   133   23       \n",
       "3  8.9981    79    0       \n",
       "4 11.0014    28    1       \n",
       "5 11.0014    16    2       \n",
       "6 16.9979     3    3       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets compare last from train and first from test\n",
    "tail(train_data)\n",
    "head(test_data) # ok!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73cf1493-2557-4f53-8973-a37bb924838f",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70d008b-d89e-4a0b-a30a-8dd628a62033",
   "metadata": {},
   "source": [
    "## Model building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e716de2c-5bb0-456a-9b58-6cbc8ac93dcd",
   "metadata": {},
   "source": [
    "Побудуємо напройстішу модель залежності середньої температури за день та кількості орендованих байків:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f60743ed-f954-4791-b7af-6c61f469f021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = count ~ atemp, data = train_data)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-254.42  -93.91  -27.43   65.57  648.09 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  -2.0774     4.4973  -0.462    0.644    \n",
       "atemp         7.2602     0.1895  38.311   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 138.4 on 7619 degrees of freedom\n",
       "Multiple R-squared:  0.1615,\tAdjusted R-squared:  0.1614 \n",
       "F-statistic:  1468 on 1 and 7619 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_bike <- lm(count ~ atemp, train_data)\n",
    "summary(lm_bike)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cb784b-5c5c-4dab-a487-528e19e1c60a",
   "metadata": {},
   "source": [
    "Спробуємо порахувати кількість орендованих байків для середніх темпертур від 10 до 30 градусів.\n",
    "\n",
    "Створимо датафрейм з послідовністю елементів 10-30 з кроком 1 та порахуємо за формулою з інформації про модель прогнозовані показники:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6523d0b4-a0ef-497e-8f2b-2b0fcaf07005",
   "metadata": {},
   "source": [
    "Здійснимо прогноз за допомогою функції `predict()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2eb2656-fc6c-4d84-9981-e47c894187ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict_1030$count_model <- predict(lm_bike, newdata = predict_1030)\n",
    "#predict_1030 %>% head()\n",
    "\n",
    "# our results are very close, minimal difference is a result of rounding numbers in our formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a29da7a-b1d4-4710-aa33-7d0d7bb7062f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how it looks like at chart\n",
    "#ggplot(predict_1030, aes(atemp, count_model)) + \n",
    " #       geom_point() + \n",
    "    #    theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57e0b52-1544-4494-b93b-5742e04f8621",
   "metadata": {},
   "source": [
    "Побудуємо модель для усіх параметрів моделі окрім дати:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7d46e05f-f3eb-4cd9-946e-9a1492105099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = count ~ . - datetime, data = train_data)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-283.67  -78.72  -25.68   46.27  587.17 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  15.67996    8.85332   1.771   0.0766 .  \n",
       "season2       9.39674    4.59288   2.046   0.0408 *  \n",
       "season3     -40.29356    6.46891  -6.229 4.95e-10 ***\n",
       "season4      21.14562    4.40422   4.801 1.61e-06 ***\n",
       "holiday1     -8.46508    8.79408  -0.963   0.3358    \n",
       "workingday1  -4.89327    3.12276  -1.567   0.1172    \n",
       "weather2      2.76675    3.47283   0.797   0.4257    \n",
       "weather3    -31.30849    5.61763  -5.573 2.59e-08 ***\n",
       "weather4     85.56066  122.62585   0.698   0.4854    \n",
       "temp          1.34075    1.67470   0.801   0.4234    \n",
       "atemp         6.30809    1.48446   4.249 2.17e-05 ***\n",
       "humidity     -1.49856    0.08884 -16.868  < 2e-16 ***\n",
       "windspeed     0.35086    0.19131   1.834   0.0667 .  \n",
       "hour_rent     6.20760    0.21640  28.685  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 122.5 on 7607 degrees of freedom\n",
       "Multiple R-squared:  0.3438,\tAdjusted R-squared:  0.3427 \n",
       "F-statistic: 306.6 on 13 and 7607 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_bike <- lm(count~.-datetime, train_data)\n",
    "summary(lm_bike)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bff3f7e-958e-46b6-8020-f156f3e85bb0",
   "metadata": {},
   "source": [
    "Перевіримо модель на мультиколінеарність:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aeb26c9c-5231-4f64-b7d1-51f4b3d69c06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 9 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>GVIF</th><th scope=col>Df</th><th scope=col>GVIF^(1/(2*Df))</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>season</th><td> 2.941744</td><td>3</td><td>1.197018</td></tr>\n",
       "\t<tr><th scope=row>holiday</th><td> 1.076162</td><td>1</td><td>1.037382</td></tr>\n",
       "\t<tr><th scope=row>workingday</th><td> 1.072163</td><td>1</td><td>1.035453</td></tr>\n",
       "\t<tr><th scope=row>weather</th><td> 1.291207</td><td>3</td><td>1.043516</td></tr>\n",
       "\t<tr><th scope=row>temp</th><td>83.073739</td><td>1</td><td>9.114480</td></tr>\n",
       "\t<tr><th scope=row>atemp</th><td>78.281362</td><td>1</td><td>8.847676</td></tr>\n",
       "\t<tr><th scope=row>humidity</th><td> 1.594124</td><td>1</td><td>1.262586</td></tr>\n",
       "\t<tr><th scope=row>windspeed</th><td> 1.331723</td><td>1</td><td>1.154003</td></tr>\n",
       "\t<tr><th scope=row>hour_rent</th><td> 1.135437</td><td>1</td><td>1.065569</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 9 × 3 of type dbl\n",
       "\\begin{tabular}{r|lll}\n",
       "  & GVIF & Df & GVIF\\textasciicircum{}(1/(2*Df))\\\\\n",
       "\\hline\n",
       "\tseason &  2.941744 & 3 & 1.197018\\\\\n",
       "\tholiday &  1.076162 & 1 & 1.037382\\\\\n",
       "\tworkingday &  1.072163 & 1 & 1.035453\\\\\n",
       "\tweather &  1.291207 & 3 & 1.043516\\\\\n",
       "\ttemp & 83.073739 & 1 & 9.114480\\\\\n",
       "\tatemp & 78.281362 & 1 & 8.847676\\\\\n",
       "\thumidity &  1.594124 & 1 & 1.262586\\\\\n",
       "\twindspeed &  1.331723 & 1 & 1.154003\\\\\n",
       "\thour\\_rent &  1.135437 & 1 & 1.065569\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 9 × 3 of type dbl\n",
       "\n",
       "| <!--/--> | GVIF | Df | GVIF^(1/(2*Df)) |\n",
       "|---|---|---|---|\n",
       "| season |  2.941744 | 3 | 1.197018 |\n",
       "| holiday |  1.076162 | 1 | 1.037382 |\n",
       "| workingday |  1.072163 | 1 | 1.035453 |\n",
       "| weather |  1.291207 | 3 | 1.043516 |\n",
       "| temp | 83.073739 | 1 | 9.114480 |\n",
       "| atemp | 78.281362 | 1 | 8.847676 |\n",
       "| humidity |  1.594124 | 1 | 1.262586 |\n",
       "| windspeed |  1.331723 | 1 | 1.154003 |\n",
       "| hour_rent |  1.135437 | 1 | 1.065569 |\n",
       "\n"
      ],
      "text/plain": [
       "           GVIF      Df GVIF^(1/(2*Df))\n",
       "season      2.941744 3  1.197018       \n",
       "holiday     1.076162 1  1.037382       \n",
       "workingday  1.072163 1  1.035453       \n",
       "weather     1.291207 3  1.043516       \n",
       "temp       83.073739 1  9.114480       \n",
       "atemp      78.281362 1  8.847676       \n",
       "humidity    1.594124 1  1.262586       \n",
       "windspeed   1.331723 1  1.154003       \n",
       "hour_rent   1.135437 1  1.065569       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suppressMessages(library(car))\n",
    "vif(lm_bike)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f4ff38-8365-4a3f-add1-0d3f6e49a1d4",
   "metadata": {},
   "source": [
    "Виключимо корельовані показники з моделі за принципом - залишити той, що має вищу кореляцію із залежним фактором. У нашому випадку це `temp`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e4712d0e-c8af-447d-bd4c-0f56acb93548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = count ~ . - datetime - atemp, data = train_data)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-297.02  -79.11  -25.60   46.35  585.12 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  26.97221    8.45456   3.190  0.00143 ** \n",
       "season2       9.25257    4.59790   2.012  0.04422 *  \n",
       "season3     -45.22396    6.37114  -7.098 1.38e-12 ***\n",
       "season4      22.00064    4.40455   4.995 6.02e-07 ***\n",
       "holiday1    -11.34365    8.77778  -1.292  0.19629    \n",
       "workingday1  -4.86349    3.12625  -1.556  0.11982    \n",
       "weather2      3.02977    3.47616   0.872  0.38346    \n",
       "weather3    -32.19037    5.62008  -5.728 1.06e-08 ***\n",
       "weather4     88.33390  122.76150   0.720  0.47182    \n",
       "temp          8.34447    0.29736  28.062  < 2e-16 ***\n",
       "humidity     -1.47674    0.08879 -16.632  < 2e-16 ***\n",
       "windspeed     0.06683    0.17945   0.372  0.70959    \n",
       "hour_rent     6.20731    0.21665  28.652  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 122.7 on 7608 degrees of freedom\n",
       "Multiple R-squared:  0.3422,\tAdjusted R-squared:  0.3412 \n",
       "F-statistic: 329.9 on 12 and 7608 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_bike <- lm(count~.-datetime-atemp, train_data)\n",
    "summary(lm_bike)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f220d1-f218-47dd-a408-2a67387e8679",
   "metadata": {},
   "source": [
    "Далі побудуємо модель на основі алгоритму stepwise та порівняємо з поточними характеристиками моделі.\n",
    "\n",
    "Створимо моделі для проходів у два боки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0cb87029-7ab3-4233-b1f9-995ac8f9494e",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_mod <- lm(count ~ 1, data = train_data)\n",
    "end_mod <- lm(count ~ . - datetime - temp, data = train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0001af71-37f8-4134-bd65-29b7a635c7b0",
   "metadata": {},
   "source": [
    "Запустимо алгоритм функцією `step()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "74e892cd-cae9-496b-889c-01542f3d65d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=76488.12\n",
      "count ~ 1\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ atemp       1  28114587 145942663 75148\n",
      "+ hour_rent   1  27600736 146456514 75174\n",
      "+ humidity    1  15503642 158553609 75779\n",
      "+ season      3   9131936 164925314 76083\n",
      "+ weather     3   3537078 170520172 76338\n",
      "+ windspeed   1   2112392 171944858 76397\n",
      "<none>                    174057250 76488\n",
      "+ holiday     1     12170 174045081 76490\n",
      "+ workingday  1      3511 174053739 76490\n",
      "\n",
      "Step:  AIC=75147.53\n",
      "count ~ atemp\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ hour_rent   1  20603748 125338915 73990\n",
      "+ humidity    1  15576942 130365721 74289\n",
      "+ season      3   5178271 140764392 74878\n",
      "+ weather     3   2989985 142952678 74996\n",
      "+ windspeed   1   2889374 143053289 74997\n",
      "<none>                    145942663 75148\n",
      "+ workingday  1     34765 145907898 75148\n",
      "+ holiday     1        44 145942619 75150\n",
      "- atemp       1  28114587 174057250 76488\n",
      "\n",
      "Step:  AIC=73989.67\n",
      "count ~ atemp + hour_rent\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ humidity    1   8093815 117245100 73483\n",
      "+ season      3   2905720 122433195 73817\n",
      "+ weather     3   2861080 122477834 73820\n",
      "+ windspeed   1   1038961 124299954 73928\n",
      "<none>                    125338915 73990\n",
      "+ workingday  1     29661 125309254 73990\n",
      "+ holiday     1       313 125338602 73992\n",
      "- hour_rent   1  20603748 145942663 75148\n",
      "- atemp       1  21117599 146456514 75174\n",
      "\n",
      "Step:  AIC=73482.93\n",
      "count ~ atemp + hour_rent + humidity\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ season      3   2375796 114869304 73333\n",
      "+ weather     3    534048 116711052 73454\n",
      "+ workingday  1     32372 117212728 73483\n",
      "<none>                    117245100 73483\n",
      "+ windspeed   1     22790 117222310 73483\n",
      "+ holiday     1      6750 117238350 73484\n",
      "- humidity    1   8093815 125338915 73990\n",
      "- hour_rent   1  13120621 130365721 74289\n",
      "- atemp       1  22173444 139418544 74801\n",
      "\n",
      "Step:  AIC=73332.92\n",
      "count ~ atemp + hour_rent + humidity + season\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ weather     3    525600 114343704 73304\n",
      "+ workingday  1     42319 114826985 73332\n",
      "+ windspeed   1     31234 114838071 73333\n",
      "<none>                    114869304 73333\n",
      "+ holiday     1      2238 114867066 73335\n",
      "- season      3   2375796 117245100 73483\n",
      "- humidity    1   7563890 122433195 73817\n",
      "- atemp       1  12150238 127019542 74097\n",
      "- hour_rent   1  12266423 127135727 74104\n",
      "\n",
      "Step:  AIC=73303.97\n",
      "count ~ atemp + hour_rent + humidity + season + weather\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "+ windspeed   1     77129 114266576 73301\n",
      "+ workingday  1     30192 114313512 73304\n",
      "<none>                    114343704 73304\n",
      "+ holiday     1      3786 114339918 73306\n",
      "- weather     3    525600 114869304 73333\n",
      "- season      3   2367348 116711052 73454\n",
      "- humidity    1   5224113 119567817 73642\n",
      "- atemp       1  12055477 126399182 74066\n",
      "- hour_rent   1  12568342 126912046 74097\n",
      "\n",
      "Step:  AIC=73300.82\n",
      "count ~ atemp + hour_rent + humidity + season + weather + windspeed\n",
      "\n",
      "             Df Sum of Sq       RSS   AIC\n",
      "<none>                    114266576 73301\n",
      "+ workingday  1     28599 114237977 73301\n",
      "+ holiday     1      4334 114262242 73303\n",
      "- windspeed   1     77129 114343704 73304\n",
      "- weather     3    571495 114838071 73333\n",
      "- season      3   2382626 116649202 73452\n",
      "- humidity    1   4315759 118582335 73581\n",
      "- atemp       1  12116310 126382885 74067\n",
      "- hour_rent   1  12411506 126678081 74085\n"
     ]
    }
   ],
   "source": [
    "lm_bike_stepwise <- step(start_mod,\n",
    "                     scope = list(lower = start_mod, upper = end_mod),\n",
    "                     direction = \"both\", trace = 1, steps = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bb6a5212-152e-4749-bea5-19c2ace0299f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = count ~ atemp + hour_rent + humidity + season + \n",
       "    weather + windspeed, data = train_data)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-285.56  -78.70  -25.63   47.20  586.14 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  10.56655    8.35898   1.264   0.2062    \n",
       "atemp         7.46224    0.26269  28.407  < 2e-16 ***\n",
       "hour_rent     6.21766    0.21626  28.750  < 2e-16 ***\n",
       "humidity     -1.50051    0.08851 -16.954  < 2e-16 ***\n",
       "season2       9.95637    4.55753   2.185   0.0289 *  \n",
       "season3     -38.55100    6.14342  -6.275 3.68e-10 ***\n",
       "season4      21.22676    4.40003   4.824 1.43e-06 ***\n",
       "weather2      2.68041    3.47136   0.772   0.4400    \n",
       "weather3    -31.47986    5.60817  -5.613 2.06e-08 ***\n",
       "weather4     83.31112  122.62122   0.679   0.4969    \n",
       "windspeed     0.40641    0.17932   2.266   0.0235 *  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 122.5 on 7610 degrees of freedom\n",
       "Multiple R-squared:  0.3435,\tAdjusted R-squared:  0.3426 \n",
       "F-statistic: 398.2 on 10 and 7610 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the summary\n",
    "summary(lm_bike_stepwise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb8ddc7-c285-406b-91f6-810ffc790ba2",
   "metadata": {},
   "source": [
    "$R^2$ increses!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a021111f-b44b-4ed9-9a85-51bd3e07efc0",
   "metadata": {},
   "source": [
    "Об’єднаємо для наочності коефіцієнти у один датафрейм:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec9be769-d9af-4347-98de-f234b4084c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 13 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lm_name</th><th scope=col>name</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)</td><td> 26.97221255</td></tr>\n",
       "\t<tr><td>season2    </td><td>  9.25256711</td></tr>\n",
       "\t<tr><td>season3    </td><td>-45.22395805</td></tr>\n",
       "\t<tr><td>season4    </td><td> 22.00064078</td></tr>\n",
       "\t<tr><td>holiday1   </td><td>-11.34364536</td></tr>\n",
       "\t<tr><td>workingday1</td><td> -4.86348665</td></tr>\n",
       "\t<tr><td>weather2   </td><td>  3.02977126</td></tr>\n",
       "\t<tr><td>weather3   </td><td>-32.19036573</td></tr>\n",
       "\t<tr><td>weather4   </td><td> 88.33390060</td></tr>\n",
       "\t<tr><td>temp       </td><td>  8.34447012</td></tr>\n",
       "\t<tr><td>humidity   </td><td> -1.47674448</td></tr>\n",
       "\t<tr><td>windspeed  </td><td>  0.06683245</td></tr>\n",
       "\t<tr><td>hour_rent  </td><td>  6.20730753</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 13 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lm\\_name & name\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t (Intercept) &  26.97221255\\\\\n",
       "\t season2     &   9.25256711\\\\\n",
       "\t season3     & -45.22395805\\\\\n",
       "\t season4     &  22.00064078\\\\\n",
       "\t holiday1    & -11.34364536\\\\\n",
       "\t workingday1 &  -4.86348665\\\\\n",
       "\t weather2    &   3.02977126\\\\\n",
       "\t weather3    & -32.19036573\\\\\n",
       "\t weather4    &  88.33390060\\\\\n",
       "\t temp        &   8.34447012\\\\\n",
       "\t humidity    &  -1.47674448\\\\\n",
       "\t windspeed   &   0.06683245\\\\\n",
       "\t hour\\_rent   &   6.20730753\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 13 × 2\n",
       "\n",
       "| lm_name &lt;chr&gt; | name &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| (Intercept) |  26.97221255 |\n",
       "| season2     |   9.25256711 |\n",
       "| season3     | -45.22395805 |\n",
       "| season4     |  22.00064078 |\n",
       "| holiday1    | -11.34364536 |\n",
       "| workingday1 |  -4.86348665 |\n",
       "| weather2    |   3.02977126 |\n",
       "| weather3    | -32.19036573 |\n",
       "| weather4    |  88.33390060 |\n",
       "| temp        |   8.34447012 |\n",
       "| humidity    |  -1.47674448 |\n",
       "| windspeed   |   0.06683245 |\n",
       "| hour_rent   |   6.20730753 |\n",
       "\n"
      ],
      "text/plain": [
       "   lm_name     name        \n",
       "1  (Intercept)  26.97221255\n",
       "2  season2       9.25256711\n",
       "3  season3     -45.22395805\n",
       "4  season4      22.00064078\n",
       "5  holiday1    -11.34364536\n",
       "6  workingday1  -4.86348665\n",
       "7  weather2      3.02977126\n",
       "8  weather3    -32.19036573\n",
       "9  weather4     88.33390060\n",
       "10 temp          8.34447012\n",
       "11 humidity     -1.47674448\n",
       "12 windspeed     0.06683245\n",
       "13 hour_rent     6.20730753"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_coefs_def <- data.frame(lm_name = names(lm_bike$coefficients),\n",
    "                           name = lm_bike$coefficients, \n",
    "                           row.names = c() # cleaning rownames\n",
    "                          )\n",
    "lm_coefs_def"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eaed4e63-79ec-4183-aca6-7071793963f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lm_name</th><th scope=col>name</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)</td><td> 10.5665521</td></tr>\n",
       "\t<tr><td>atemp      </td><td>  7.4622378</td></tr>\n",
       "\t<tr><td>hour_rent  </td><td>  6.2176579</td></tr>\n",
       "\t<tr><td>humidity   </td><td> -1.5005074</td></tr>\n",
       "\t<tr><td>season2    </td><td>  9.9563690</td></tr>\n",
       "\t<tr><td>season3    </td><td>-38.5510028</td></tr>\n",
       "\t<tr><td>season4    </td><td> 21.2267554</td></tr>\n",
       "\t<tr><td>weather2   </td><td>  2.6804117</td></tr>\n",
       "\t<tr><td>weather3   </td><td>-31.4798561</td></tr>\n",
       "\t<tr><td>weather4   </td><td> 83.3111193</td></tr>\n",
       "\t<tr><td>windspeed  </td><td>  0.4064147</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lm\\_name & name\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t (Intercept) &  10.5665521\\\\\n",
       "\t atemp       &   7.4622378\\\\\n",
       "\t hour\\_rent   &   6.2176579\\\\\n",
       "\t humidity    &  -1.5005074\\\\\n",
       "\t season2     &   9.9563690\\\\\n",
       "\t season3     & -38.5510028\\\\\n",
       "\t season4     &  21.2267554\\\\\n",
       "\t weather2    &   2.6804117\\\\\n",
       "\t weather3    & -31.4798561\\\\\n",
       "\t weather4    &  83.3111193\\\\\n",
       "\t windspeed   &   0.4064147\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 2\n",
       "\n",
       "| lm_name &lt;chr&gt; | name &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| (Intercept) |  10.5665521 |\n",
       "| atemp       |   7.4622378 |\n",
       "| hour_rent   |   6.2176579 |\n",
       "| humidity    |  -1.5005074 |\n",
       "| season2     |   9.9563690 |\n",
       "| season3     | -38.5510028 |\n",
       "| season4     |  21.2267554 |\n",
       "| weather2    |   2.6804117 |\n",
       "| weather3    | -31.4798561 |\n",
       "| weather4    |  83.3111193 |\n",
       "| windspeed   |   0.4064147 |\n",
       "\n"
      ],
      "text/plain": [
       "   lm_name     name       \n",
       "1  (Intercept)  10.5665521\n",
       "2  atemp         7.4622378\n",
       "3  hour_rent     6.2176579\n",
       "4  humidity     -1.5005074\n",
       "5  season2       9.9563690\n",
       "6  season3     -38.5510028\n",
       "7  season4      21.2267554\n",
       "8  weather2      2.6804117\n",
       "9  weather3    -31.4798561\n",
       "10 weather4     83.3111193\n",
       "11 windspeed     0.4064147"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_coefs_sw <- data.frame(lm_name = names(lm_bike_stepwise$coefficients), name = lm_bike_stepwise$coefficients, row.names = c())\n",
    "lm_coefs_sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9d26163c-efd7-4492-80f3-1d4094a15208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>variable</th><th scope=col>lm</th><th scope=col>lm_sw</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)</td><td> 26.97221255</td><td> 10.5665521</td></tr>\n",
       "\t<tr><td>hour_rent  </td><td>  6.20730753</td><td>  6.2176579</td></tr>\n",
       "\t<tr><td>humidity   </td><td> -1.47674448</td><td> -1.5005074</td></tr>\n",
       "\t<tr><td>season2    </td><td>  9.25256711</td><td>  9.9563690</td></tr>\n",
       "\t<tr><td>season3    </td><td>-45.22395805</td><td>-38.5510028</td></tr>\n",
       "\t<tr><td>season4    </td><td> 22.00064078</td><td> 21.2267554</td></tr>\n",
       "\t<tr><td>weather2   </td><td>  3.02977126</td><td>  2.6804117</td></tr>\n",
       "\t<tr><td>weather3   </td><td>-32.19036573</td><td>-31.4798561</td></tr>\n",
       "\t<tr><td>weather4   </td><td> 88.33390060</td><td> 83.3111193</td></tr>\n",
       "\t<tr><td>windspeed  </td><td>  0.06683245</td><td>  0.4064147</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 3\n",
       "\\begin{tabular}{lll}\n",
       " variable & lm & lm\\_sw\\\\\n",
       " <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t (Intercept) &  26.97221255 &  10.5665521\\\\\n",
       "\t hour\\_rent   &   6.20730753 &   6.2176579\\\\\n",
       "\t humidity    &  -1.47674448 &  -1.5005074\\\\\n",
       "\t season2     &   9.25256711 &   9.9563690\\\\\n",
       "\t season3     & -45.22395805 & -38.5510028\\\\\n",
       "\t season4     &  22.00064078 &  21.2267554\\\\\n",
       "\t weather2    &   3.02977126 &   2.6804117\\\\\n",
       "\t weather3    & -32.19036573 & -31.4798561\\\\\n",
       "\t weather4    &  88.33390060 &  83.3111193\\\\\n",
       "\t windspeed   &   0.06683245 &   0.4064147\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 3\n",
       "\n",
       "| variable &lt;chr&gt; | lm &lt;dbl&gt; | lm_sw &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| (Intercept) |  26.97221255 |  10.5665521 |\n",
       "| hour_rent   |   6.20730753 |   6.2176579 |\n",
       "| humidity    |  -1.47674448 |  -1.5005074 |\n",
       "| season2     |   9.25256711 |   9.9563690 |\n",
       "| season3     | -45.22395805 | -38.5510028 |\n",
       "| season4     |  22.00064078 |  21.2267554 |\n",
       "| weather2    |   3.02977126 |   2.6804117 |\n",
       "| weather3    | -32.19036573 | -31.4798561 |\n",
       "| weather4    |  88.33390060 |  83.3111193 |\n",
       "| windspeed   |   0.06683245 |   0.4064147 |\n",
       "\n"
      ],
      "text/plain": [
       "   variable    lm           lm_sw      \n",
       "1  (Intercept)  26.97221255  10.5665521\n",
       "2  hour_rent     6.20730753   6.2176579\n",
       "3  humidity     -1.47674448  -1.5005074\n",
       "4  season2       9.25256711   9.9563690\n",
       "5  season3     -45.22395805 -38.5510028\n",
       "6  season4      22.00064078  21.2267554\n",
       "7  weather2      3.02977126   2.6804117\n",
       "8  weather3    -32.19036573 -31.4798561\n",
       "9  weather4     88.33390060  83.3111193\n",
       "10 windspeed     0.06683245   0.4064147"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets combine coefficients from both models\n",
    "lm_coefs <- merge(lm_coefs_def, lm_coefs_sw, by = \"lm_name\", X.all = T, X.all = T)\n",
    "colnames(lm_coefs) <- c(\"variable\", \"lm\", \"lm_sw\")\n",
    "lm_coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a84272e-7dcf-4259-938f-82ac825e2f6a",
   "metadata": {},
   "source": [
    "Як бачимо моделі мають незначні відмінності."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51a4762-e488-4bb2-87a4-94c5d6d9dc52",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a82c2b5-b36f-4888-99ca-a1d2b3499b66",
   "metadata": {},
   "source": [
    "## Model errors analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302674be-2390-466a-b37d-47935999e257",
   "metadata": {},
   "source": [
    "Запишемо у `train` набір даних модельовані значення `count` та похибки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3907623f-f2bb-4503-8080-915f0fda3ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th><th scope=col>predicted</th><th scope=col>residuals</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2011-01-01 00:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>81</td><td>0.0000</td><td>16</td><td>0</td><td>-10.534505</td><td> 26.534505</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2011-01-01 01:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>40</td><td>1</td><td> -9.692918</td><td> 49.692918</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2011-01-01 02:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.02</td><td>13.635</td><td>80</td><td>0.0000</td><td>32</td><td>2</td><td> -3.485611</td><td> 35.485611</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2011-01-01 03:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td>13</td><td>3</td><td> 16.947885</td><td> -3.947885</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2011-01-01 04:00:00</td><td>1</td><td>0</td><td>0</td><td>1</td><td>9.84</td><td>14.395</td><td>75</td><td>0.0000</td><td> 1</td><td>4</td><td> 23.155192</td><td>-22.155192</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2011-01-01 05:00:00</td><td>1</td><td>0</td><td>0</td><td>2</td><td>9.84</td><td>12.880</td><td>75</td><td>6.0032</td><td> 1</td><td>5</td><td> 32.793480</td><td>-31.793480</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent & predicted & residuals\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 2011-01-01 00:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 81 & 0.0000 & 16 & 0 & -10.534505 &  26.534505\\\\\n",
       "\t2 & 2011-01-01 01:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 40 & 1 &  -9.692918 &  49.692918\\\\\n",
       "\t3 & 2011-01-01 02:00:00 & 1 & 0 & 0 & 1 & 9.02 & 13.635 & 80 & 0.0000 & 32 & 2 &  -3.485611 &  35.485611\\\\\n",
       "\t4 & 2011-01-01 03:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 & 13 & 3 &  16.947885 &  -3.947885\\\\\n",
       "\t5 & 2011-01-01 04:00:00 & 1 & 0 & 0 & 1 & 9.84 & 14.395 & 75 & 0.0000 &  1 & 4 &  23.155192 & -22.155192\\\\\n",
       "\t6 & 2011-01-01 05:00:00 & 1 & 0 & 0 & 2 & 9.84 & 12.880 & 75 & 6.0032 &  1 & 5 &  32.793480 & -31.793480\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; | predicted &lt;dbl&gt; | residuals &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0000 | 16 | 0 | -10.534505 |  26.534505 |\n",
       "| 2 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 40 | 1 |  -9.692918 |  49.692918 |\n",
       "| 3 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0000 | 32 | 2 |  -3.485611 |  35.485611 |\n",
       "| 4 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 | 13 | 3 |  16.947885 |  -3.947885 |\n",
       "| 5 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0000 |  1 | 4 |  23.155192 | -22.155192 |\n",
       "| 6 | 2011-01-01 05:00:00 | 1 | 0 | 0 | 2 | 9.84 | 12.880 | 75 | 6.0032 |  1 | 5 |  32.793480 | -31.793480 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp atemp  humidity\n",
       "1 2011-01-01 00:00:00 1      0       0          1       9.84 14.395 81      \n",
       "2 2011-01-01 01:00:00 1      0       0          1       9.02 13.635 80      \n",
       "3 2011-01-01 02:00:00 1      0       0          1       9.02 13.635 80      \n",
       "4 2011-01-01 03:00:00 1      0       0          1       9.84 14.395 75      \n",
       "5 2011-01-01 04:00:00 1      0       0          1       9.84 14.395 75      \n",
       "6 2011-01-01 05:00:00 1      0       0          2       9.84 12.880 75      \n",
       "  windspeed count hour_rent predicted  residuals \n",
       "1 0.0000    16    0         -10.534505  26.534505\n",
       "2 0.0000    40    1          -9.692918  49.692918\n",
       "3 0.0000    32    2          -3.485611  35.485611\n",
       "4 0.0000    13    3          16.947885  -3.947885\n",
       "5 0.0000     1    4          23.155192 -22.155192\n",
       "6 6.0032     1    5          32.793480 -31.793480"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data$predicted <- lm_bike$fitted.values\n",
    "train_data$residuals <- lm_bike$residuals\n",
    "head(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8953a14f-e6c3-4ac6-b7b9-cb3c38221cde",
   "metadata": {},
   "source": [
    "Візуалізуємо відхилення моделі:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2f20166b-aaa3-4c21-b00d-561d57182f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data <- train_data[1:100,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e3b80713-6889-4615-9fc2-b4a9966d13f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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ekqsT0blBsTEcneX0QK6eEwZYpIh+Kh3zIpfyOSK+4ivfmmYbm63Ag9HKZMq9p5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\nQaTxY8o7ZxRpBCIJEMktt0QkNYgkQCS33BKR1CCSAJHccktEUoN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barWvRGx+I9VVhAeWYsMDUZOoMx+fyw5nkZqb/EaaDiIty01dpb6n\nqNqpxmdtTbseiJXV18tJl65qVz1YjsrfpbivpSt/XxXdb6RD/yZVu+kg0sK8ciG2m563I5VG\nkdqtrNVaXj2/Gt+V7Xakmwvx4GCDrCif97i5bDbInp5xUdfexVPam2xHmg4irUXQvqr9eSP+\nohFSF+zZEA4ircZwfzn/eeOJxL5200Gk1Xg8YcV9fDCLVLS4pLH393QQaT0GxxT5zxtNJI5H\nigAiAUQAkQAigEgAEUAkgAggEkAEEAkgAogEEAFEAojA/wP2qzeTc4ekYAAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot_data, aes(x = c(1:nrow(plot_data)), y = as.numeric(count))) +\n",
    "  geom_segment(aes(xend = c(1:nrow(plot_data)), yend = predicted), alpha = .2) +\n",
    "  geom_point(aes(color = residuals), size = 2) +\n",
    "  scale_color_gradient2(low = \"blue\", mid = \"white\", high = \"red\") +\n",
    "  guides(color = FALSE) +\n",
    "  geom_point(aes(y = predicted), shape = 1, size = 2) +\n",
    "  geom_hline(yintercept=0.5, linetype=\"dashed\", color = \"red\") +\n",
    "  theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af41310-1465-46ef-b241-a849a9cdcbda",
   "metadata": {},
   "source": [
    "Lets check the same for test data\n",
    "\n",
    "First we need to predict values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ba9d504d-3d08-4590-8324-3d7595f99ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th><th scope=col>predicted</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2012-05-16 22:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>26.24</td><td>30.305</td><td>73</td><td>11.0014</td><td>286</td><td>22</td><td>279.8139</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2012-05-16 23:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>25.42</td><td>29.545</td><td>78</td><td> 7.0015</td><td>133</td><td>23</td><td>271.5277</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2012-05-17 00:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td> 8.9981</td><td> 79</td><td> 0</td><td>122.0506</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2012-05-17 01:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.030</td><td>83</td><td>11.0014</td><td> 28</td><td> 1</td><td>121.0080</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2012-05-17 02:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td>11.0014</td><td> 16</td><td> 2</td><td>134.5991</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2012-05-17 03:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>29.545</td><td>73</td><td>16.9979</td><td>  3</td><td> 3</td><td>148.5908</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent & predicted\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 2012-05-16 22:00:00 & 2 & 0 & 1 & 1 & 26.24 & 30.305 & 73 & 11.0014 & 286 & 22 & 279.8139\\\\\n",
       "\t2 & 2012-05-16 23:00:00 & 2 & 0 & 1 & 1 & 25.42 & 29.545 & 78 &  7.0015 & 133 & 23 & 271.5277\\\\\n",
       "\t3 & 2012-05-17 00:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 &  8.9981 &  79 &  0 & 122.0506\\\\\n",
       "\t4 & 2012-05-17 01:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.030 & 83 & 11.0014 &  28 &  1 & 121.0080\\\\\n",
       "\t5 & 2012-05-17 02:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 & 11.0014 &  16 &  2 & 134.5991\\\\\n",
       "\t6 & 2012-05-17 03:00:00 & 2 & 0 & 1 & 1 & 24.60 & 29.545 & 73 & 16.9979 &   3 &  3 & 148.5908\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 12\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; | predicted &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2012-05-16 22:00:00 | 2 | 0 | 1 | 1 | 26.24 | 30.305 | 73 | 11.0014 | 286 | 22 | 279.8139 |\n",
       "| 2 | 2012-05-16 23:00:00 | 2 | 0 | 1 | 1 | 25.42 | 29.545 | 78 |  7.0015 | 133 | 23 | 271.5277 |\n",
       "| 3 | 2012-05-17 00:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 |  8.9981 |  79 |  0 | 122.0506 |\n",
       "| 4 | 2012-05-17 01:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.030 | 83 | 11.0014 |  28 |  1 | 121.0080 |\n",
       "| 5 | 2012-05-17 02:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 | 11.0014 |  16 |  2 | 134.5991 |\n",
       "| 6 | 2012-05-17 03:00:00 | 2 | 0 | 1 | 1 | 24.60 | 29.545 | 73 | 16.9979 |   3 |  3 | 148.5908 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp  atemp  humidity\n",
       "1 2012-05-16 22:00:00 2      0       1          1       26.24 30.305 73      \n",
       "2 2012-05-16 23:00:00 2      0       1          1       25.42 29.545 78      \n",
       "3 2012-05-17 00:00:00 2      0       1          1       24.60 28.790 78      \n",
       "4 2012-05-17 01:00:00 2      0       1          1       24.60 28.030 83      \n",
       "5 2012-05-17 02:00:00 2      0       1          1       24.60 28.790 78      \n",
       "6 2012-05-17 03:00:00 2      0       1          1       24.60 29.545 73      \n",
       "  windspeed count hour_rent predicted\n",
       "1 11.0014   286   22        279.8139 \n",
       "2  7.0015   133   23        271.5277 \n",
       "3  8.9981    79    0        122.0506 \n",
       "4 11.0014    28    1        121.0080 \n",
       "5 11.0014    16    2        134.5991 \n",
       "6 16.9979     3    3        148.5908 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data$predicted <- predict(lm_bike, newdata = test_data)\n",
    "head(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c588a90b-e26f-4483-b51e-7972f1937a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>datetime</th><th scope=col>season</th><th scope=col>holiday</th><th scope=col>workingday</th><th scope=col>weather</th><th scope=col>temp</th><th scope=col>atemp</th><th scope=col>humidity</th><th scope=col>windspeed</th><th scope=col>count</th><th scope=col>hour_rent</th><th scope=col>predicted</th><th scope=col>residuals</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>2012-05-16 22:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>26.24</td><td>30.305</td><td>73</td><td>11.0014</td><td>286</td><td>22</td><td>279.8139</td><td>   6.186142</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2012-05-16 23:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>25.42</td><td>29.545</td><td>78</td><td> 7.0015</td><td>133</td><td>23</td><td>271.5277</td><td>-138.527654</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2012-05-17 00:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td> 8.9981</td><td> 79</td><td> 0</td><td>122.0506</td><td> -43.050553</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2012-05-17 01:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.030</td><td>83</td><td>11.0014</td><td> 28</td><td> 1</td><td>121.0080</td><td> -93.008024</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2012-05-17 02:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>28.790</td><td>78</td><td>11.0014</td><td> 16</td><td> 2</td><td>134.5991</td><td>-118.599054</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2012-05-17 03:00:00</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24.60</td><td>29.545</td><td>73</td><td>16.9979</td><td>  3</td><td> 3</td><td>148.5908</td><td>-145.590844</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & datetime & season & holiday & workingday & weather & temp & atemp & humidity & windspeed & count & hour\\_rent & predicted & residuals\\\\\n",
       "  & <dttm> & <fct> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <int> & <dbl> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 2012-05-16 22:00:00 & 2 & 0 & 1 & 1 & 26.24 & 30.305 & 73 & 11.0014 & 286 & 22 & 279.8139 &    6.186142\\\\\n",
       "\t2 & 2012-05-16 23:00:00 & 2 & 0 & 1 & 1 & 25.42 & 29.545 & 78 &  7.0015 & 133 & 23 & 271.5277 & -138.527654\\\\\n",
       "\t3 & 2012-05-17 00:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 &  8.9981 &  79 &  0 & 122.0506 &  -43.050553\\\\\n",
       "\t4 & 2012-05-17 01:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.030 & 83 & 11.0014 &  28 &  1 & 121.0080 &  -93.008024\\\\\n",
       "\t5 & 2012-05-17 02:00:00 & 2 & 0 & 1 & 1 & 24.60 & 28.790 & 78 & 11.0014 &  16 &  2 & 134.5991 & -118.599054\\\\\n",
       "\t6 & 2012-05-17 03:00:00 & 2 & 0 & 1 & 1 & 24.60 & 29.545 & 73 & 16.9979 &   3 &  3 & 148.5908 & -145.590844\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 13\n",
       "\n",
       "| <!--/--> | datetime &lt;dttm&gt; | season &lt;fct&gt; | holiday &lt;fct&gt; | workingday &lt;fct&gt; | weather &lt;fct&gt; | temp &lt;dbl&gt; | atemp &lt;dbl&gt; | humidity &lt;int&gt; | windspeed &lt;dbl&gt; | count &lt;int&gt; | hour_rent &lt;dbl&gt; | predicted &lt;dbl&gt; | residuals &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 2012-05-16 22:00:00 | 2 | 0 | 1 | 1 | 26.24 | 30.305 | 73 | 11.0014 | 286 | 22 | 279.8139 |    6.186142 |\n",
       "| 2 | 2012-05-16 23:00:00 | 2 | 0 | 1 | 1 | 25.42 | 29.545 | 78 |  7.0015 | 133 | 23 | 271.5277 | -138.527654 |\n",
       "| 3 | 2012-05-17 00:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 |  8.9981 |  79 |  0 | 122.0506 |  -43.050553 |\n",
       "| 4 | 2012-05-17 01:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.030 | 83 | 11.0014 |  28 |  1 | 121.0080 |  -93.008024 |\n",
       "| 5 | 2012-05-17 02:00:00 | 2 | 0 | 1 | 1 | 24.60 | 28.790 | 78 | 11.0014 |  16 |  2 | 134.5991 | -118.599054 |\n",
       "| 6 | 2012-05-17 03:00:00 | 2 | 0 | 1 | 1 | 24.60 | 29.545 | 73 | 16.9979 |   3 |  3 | 148.5908 | -145.590844 |\n",
       "\n"
      ],
      "text/plain": [
       "  datetime            season holiday workingday weather temp  atemp  humidity\n",
       "1 2012-05-16 22:00:00 2      0       1          1       26.24 30.305 73      \n",
       "2 2012-05-16 23:00:00 2      0       1          1       25.42 29.545 78      \n",
       "3 2012-05-17 00:00:00 2      0       1          1       24.60 28.790 78      \n",
       "4 2012-05-17 01:00:00 2      0       1          1       24.60 28.030 83      \n",
       "5 2012-05-17 02:00:00 2      0       1          1       24.60 28.790 78      \n",
       "6 2012-05-17 03:00:00 2      0       1          1       24.60 29.545 73      \n",
       "  windspeed count hour_rent predicted residuals  \n",
       "1 11.0014   286   22        279.8139     6.186142\n",
       "2  7.0015   133   23        271.5277  -138.527654\n",
       "3  8.9981    79    0        122.0506   -43.050553\n",
       "4 11.0014    28    1        121.0080   -93.008024\n",
       "5 11.0014    16    2        134.5991  -118.599054\n",
       "6 16.9979     3    3        148.5908  -145.590844"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# resulduals calculation\n",
    "test_data$residuals <- test_data$count - test_data$predicted\n",
    "head(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "909f55fa-77db-4049-b947-75ba310edda0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data <- test_data[1:100,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "62b927cb-9861-4133-90a6-37758e58c9c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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yabHnOL5LuSnQxmTH9QMFv52/+W5CCS5ZElx02PjC/a04v4YkPv939Yc5hUIE5c\nxVhEchQN5DBj+j8Rs+yQfYRIwswl0nGoVJmXX079oj29iBRpPIwa1hwuUv4O7TsQwMxgYIpI\nIlJSRwSZU6R68nKTtC/a0wu5SN5Lag+XZNXiyChWkYyKSI9URPrNbxxvQSRR5hza1UtxJ1Ly\nlrW1F1KRPBsi0yX5rCLFnWNuhR0AjxSGdr9pYn0LIokyZ7Gh9qgxaT+pzNM8laJFGu0ysvSw\nuxk2ceIqxlpsSP11GRYbHikUGxApN7OJ1C4lL9frpNqnmntOkdoFzLshMlmSzytSSeXv3/zG\nYRIiSTOjSE1eblVquTkfR6xIzaoosCGifOVSW/785wFbZ8G0fhWIJCIldUSQRJHEP6anYsPt\nOUU6FQ2NvFl9kf7cpMcuVCSGdtlJEilqX+LLL5vmg7g959DuuCpKvNjVgZsdRHIHkVL2Je4X\n5tuzFhuGRy9ENXHi5ubPf+5MKlwkyt+5SREpaV+ifa+nPPHFhui2Ni2SvU/bEen587wWUkUy\n0f+5W6NR8pWKlNLOhod2E+jpLZsQ6XmTnBZSh3bx/7fb1BltvRCIlNzEiZsdRHIHkRJXLLep\nqzJbL2YUSelo9UOWUf6eQk9v2YJIz59nm5QkUtL47FblQM25RVI7Wr0PNSORlA6XGj5CJBHJ\n2pELiJS+DByGdpmnDswukjnezCZSxolIQ+bwESKJSPaOPH9eVecc2mUUC47FhsyT2WYWKeu0\nBBd0OHma8WsyZA4fIZKIZO9IrZH27pnuP/iPf0xeyygWNCKdpfyd00TWaQku6HCSM74dMoeP\nEElEsnekqsvf84j0jybD13KKBQeRMisBYZEyV3o5pyU4oUbtQxzAho8QSUSydkTvx00uUnJ7\n5xIpbzMs57QEJ3QweZqzWh/Aho8QSUSydkTvx20i0j/+0Zk0HNolD+/PI1J+YVC3/G0vNmTW\n7RApNuGhXW4jkSJlFJzOJ5LJEil9wRRAH1H+PqYskbR+3GRDO5Nb/jazi6R38MSMIikxh48Q\nSURydUT/WFC/SOmf6fmKDTqH8yGS2ZJIbTOax4L6yt/GfPGLSc2Ys4mk9aOSO/yyQhHplCJF\nSm/LI5LltS82SWrofCIlZ7gxmHmxFgvUIFIvBYqkeywoIpnms8y8WIsFalYlUu7vTKEiPTqH\nSF/8YoZJSxLpcGSDhkkrFSm/zFWgSLrHgiJS+73dIpJPpO4mNUWKpHksqEOkhr+loV3mxVos\nULMekRQOBShRpDnK38MnD6puR6Tsi7VYoGZdIqVuTRxSpkiKy6ddpOPgcQHl7+RM18AKWadI\nWQeLtdmkSAo/QEsT6djf3EzsXIlI2WcnblYkg0iZ0HbhyxfppZdsLVD+FpFCHZl/aJf7v5ld\npNxv9lOfGrBnEKkZDuWK9FKTaQtnE+m11zwzyLNRkbJ3G8wtUu5Y41NNemx1kTTGx+YiIvU/\n19eaOGeQZ5siKWx8zy1S5tbveUQy2cvESy+NTZpbpOFPFCLZIxYpOzOI9Oqr5ihS7s/9pz7V\nmTTn0M5kD+0uINLxJ+rf/z561JiESJb5lijSq016IpnSRWrGx0vbRjr+RP27CSI5shaRsssh\n8w/tjE75+xIimU4khnaOnE2kT386jzbp6KuHnIoNWdtxnUif+1z7xBwi1Vlc+bv9ifr3v/ur\nJOcM8viL6IwAABeESURBVCiLtNvnON31Ho9JoY4ULtKnm+TQQiIplb8/16R+WK5I9hbmLDbs\nP9ujSIWWv3fHm93o8ZgU6sh8IqkcKzODSKehndbRPHUQafyWY/n76JHWD+zGRMo/FKTOpz/d\nmaQukk4PD/nc5zqTEMkcRWpStkhNdid3yhPpWPvMyiwiHcrfOj08BJEmbzlxW40KF+m4iWSO\nN69r8lSaW/E7rfMNJ6fXqtMkK61H0ybkuXVMtHp4SOtRr4nc3A4mcyQZfWub+NiCGVQTKVJf\noNLWSDp74+fZRmonh/0yuquk+h5rJDNaI0lmkEd/jbTr3SlNJI2jVduol7+PE7UeHlJ++dve\nwtlEOm6QZtai1EXa9e+VJ1L+0aqHPB20FB23SGo9HAaRJjuQh9WnzA9fW6Td6bZEkfQW0rlE\n0i1/n4JIk/OnhtWnzOGA9g7Z02RYbBiRQh2ZTyStzCfSPFmKSJ/8ZCo3KFLjzFCkajIxiSYp\n70caH9FQ4JENSkGkNrr9/WSTNG5ApOkB9UN1Jj7FZXvH2ikFkdosSiTTX4abIfRpMDcc6EUH\nkRKDSG1U+/vJT3YmzTK0650/dagynMoLw9KDLd6NV0RKDCK1WYRI7cpncP5Ut/KRlr8DFT1E\nSgwitVnA0K5ToFf+tm4xjeYbJlDRQ6TEzCBS82Ujkr5I3Y0ZiGRiRApV9BApMeoiHcbn2xZp\njvL3UIGn/Wf7C7RAJF9FD5ESoy9SO2jfukjp3EiRxps8gqGdr6KHSInRFun4i4dIqdyooZ2Z\nVA7aGf73f134wIli2xFJ+cibOUQySxGp91kuQaT+ysfJrWf43yaud1D+NlqnxvYyx9DOLGNo\nN/gsFyCSTPywSN4vezMi+Ue48Zmj2GCWUWwYfJaLEEnCfXT0yG0SImldqLqX7Za/h5/lWkSq\nTzBDpPF8VpFM6SL1uerREalZbR7utpN1iHQ45Zmh3Wg+69BO1aNNinQ8Pq25f3gOkXzcQdYh\nkvaJp5sUqd04klXBMnNWkU6XhfJohEhtCi9/j7jqURDptHG0rPJ3mNu7vpqghTaj5Wk7Iiln\noyKZ8VKwCpGMyCPnvik3dxBEsmWDIlk3NLcqUnfj5Q6CSLZsUiTLhuY6RDpdX03QgrGtnBEp\nMVsUybahuRaRIlowtv2SiJSYbYo0zfZEOl7r4RFDO40gUputiXQY3k6O3dyASPNccBGR2mxO\nJHO42Vr5e65LACNSm7JF0t2mezSoMgy/+vWLZEx+xy1BpDYli6RdZXw0qDJsS6TqqfnSl+Yw\nCZHaFC2SMZOvXmFo17tYeBR32SJ9qQkibVCk/Zc+/RFVKDb0/nxFFHfRIrUmMbTbpEi2H1Gl\n8rfZmkhfqvYf5/5fIs8dRGpTsEjWH1GFHbLWuuDqRWo0+hIibVAk649oFNd6oaFtimTa1Xsi\nzhNEalOySLYf0Qhu4NJ3iKQSRGpTsEjW7z5GpOPNaKtocyIdLiYyh0a6IvV/9hAplast0nHf\n6/Avv0wmcu5CRZr5Wtp6Ig0P2ypTpOfPXa+ULJKx/IjGifTo6NCWRWr3nC1ApG4XX4+rnhyR\nnjexv1a2SKnc3hHe04OCtiXS8fekeJFGp7YgUipXT6TBEd7Tg4K2J5JZikimaJGeP/eYtEqR\njjejU482KdLc19JWHdqZood2WxNpNJibHBS0NZHa9fMCRBrsrihQpK0N7SaDOcrfE65etlT+\nfv68qhYmkvMUNOHQzjeY25xIFq5eVHfITrnqyRGp1si1ZBYpkueP+AiLDb7BHCJpZlMiDQr0\nw5QpkrvDEeVvg0iINEmGSNNrT/UyU38Trg7g3KMwiLC/AoMQSSUbE8mcVaS6SBStknOPwiCI\nJErxIv3pT4P5FiGS97oX84hUc9NF8nUYkUQpXKQ/NenNtxCRPFdimqO/h2pr5OLm3KMwCCKJ\ncr5y8pZE8m2yzCXSren9/RjRME+v/D2GbVSk8xxNnSLSn/7UmbQwkdyZa2h3e/qLZsJLEwb2\nxB6CSKJMa5+I1GZZIlXm9vQ3Nn2baL0gkqUjGSKd6WjqbQ3t3Jmx/D0svgUXPkSydCRTJINI\no3zwg/Nw5xR/eMwbItlI06gO7c5wNPWiyt8fbDIHedY16GGQ7juwohdEsnQks9hwhqOpU3fI\nhmpLiNSmEaktG3mOnOsnUK47BJFEKbz8bQbfgnUBmaO/H/zgfCbNu00XW/4OO4dIopxv411D\nJNuQBZHaHIojsvFa957wKBCRRrH/7ixJJPtGNEO7NkkiCeoSiDSIax/d0kQyiORIskgGkXoJ\niuTozrlFes97YucbDu2m/wnK322iRTpeqCSwmCJSP85fnvOK9J4mcfMNiw3T1erMa9DFcCNF\nOhX4ApUJROrHORZelEjnK38vkOsRyWZKV2Wg/D14JBjaWaszZxXpPe9JMCn0E7u0Bf7sInUr\nnb4y0qMfEGkU1/4CRFoL1y1SezMcxEmPx0OkSUoof2cP7RzcObI0rkukozKjsoLwCHFEmmbw\nlv/8xzUfIi2T6xPJUugWnrOESNP03vKfJvb5Ci5/e7j6WRrXJtLob0QMN4qEF0tBpHGKECk+\niCTLVKTB34iQHhI+CSKNc/qR+s9/OpMQKYGbcA05ETcrFpHM4eZYs5McEj4JIo1z+pE6ibSA\n/TLFiSTeuojkZmYiUm+ryLkTThBEGqf3I3Xw6GynJeRwyxOpu9HlZsYq0qO+SJKFZBpEGqf3\nI3UUyTZsRiQ/V74HJo6bG+vQzvo3IuKCSOMMfqSacZ3p22Xn6mVNIk0/tXxubqzFBuvfiIgL\nInWdqE5vGfxInfG0hAxuaSIll79C3Mw4y9/jJyODSIcudJvGgx8p9zH0iNTn/ve/kxdSy18D\nrn6sO2R9S7IwiNTrQm/93i+FLuK0hEuK9N8mk5cWUv52TeKCSP0ejEo3vmPoEenEHYs0Giol\nc+cIIolI08hFmtRAvdvLiNRx9xJV1cmk4Sg5gztLEElEmkY+tJvUQL0VXETquI1G+3+TTUpE\nmmT9ItlqoL59ioh04taro/8etyWno+Rk7hyRi/Svf8VwEanrxLQGeu4/hJXDvaBIVbuNdDiV\nZzpKTuXOEqlI/2oi5yLS+LXB5Lx/CCuHe0mR2vJ379S4R5MC6DjBih4iiblLEEnK1csCRTLD\nVdFwlGxdqwsOaL2wSP/6V6RJiDR+DZGi0oh0OKPnuCoaHSQy/YIFB7QikpiLSJrcS4o0OZUn\nUPmUHNDK0E7MLUUk+5FWiCTLgDv9KK374iQHtCKSmHsBkd75zsnrk92HiBQV6w9Vv/LZlR56\nERzQemmRKH+P0uvIO5uMXp/sPkSkqARFsh27Kjig9fIiBReBQbYukv0gO4NI0oREWmr52yDS\nMKeOvPOdFpOsB9n1JgKubtYnkr1Pl/p8EUlEmiYgkvNEY0SSBZHabEgkxzaS40RjRJIFkdps\nXiTK33kRizTaKkKkUPNibhnlbxPzM+riKmalIk0OCkKkUPNi7iV2yFpTrkgRJ2uXLpIxw+8Z\nkULNi7mXE8k+yihOJOufvnKlbJGmBwUhUqh5MVdRpKcxqar2X3uzz61ncslU7U3XX29uB5NL\n5dYxqU6T4TvPnlvxZJYuRjQvzqXWSIdRRuDYoMuvkXrn9wg+orLXSNODgspfI8VdBGkdayTP\na5OOTJfPgkWyjoqsKV2kwZUCTfkixV6Wb5siDZfPQkU6/IpLDpU2xYvU29qTXWHo4iKNV6GB\nbE+k6fJZrEgxf/qqeJFGxbvCRRL+fJ2yRZHGy2epIk3Ol/NlESJNhwFCrl4iRDIFiCQZXV64\n/B04NqgIkfqjokAWI9KjRYgkORt+kFlE2n/xT8Nf/gV3yA6Xz+JFCnbGhJUrQqTpMEDI1UtE\nsSGqbjePSDW3fJEiRqpCrkJyRPINAgsRaTwMEHL1sqTyd9VyQ/1ApDB30BnvNzv8vQ9x9RIn\n0nLK37GR9fejH5U236wRTdNfREqJc8EMjDWGWyAhrl5iRbr053tJkT7aRNL86QJntwzt0uIW\nyRjbJ9X7hfdWmhCpzUJEMuZw9M3tAooNRX7RrgXTbsnwuFZfpQmR2lxQpI9+tDMp0Pzpyy67\n/F3yF+0TaTpuO/1yOf+0oJWrlwH3s59tpwV/vosR6VG3jRQKIoW5w7X9pJLQO27Q+acFrVy9\n9LifbVLfK/jzXczQ7vBlI1JifMWGSW17uIPT+zEiUpuFiNR92YiUmHD5uz85HTdoLi7SZz/b\nmVTw57uk8ncdREpMaOP9WBjtJofjBkOfIiK1uaxIx8g/IERKTFAk09x06ozOThBy9cLQrg0i\nFfZFB0QajuJOR39e7BLAiNQGkQr7ogUiTSbxXL1Q/m6DSIV90YKh3SNjOZsqjquXxM/3M59J\n4SoEkUSkaVYnUrfvVXgYtZWrl6TP9zNN4rkKQSQRaZq1iTSugiNSXBBJRJpmfSKlfe0lifSZ\nz4RNQqR2gkiJQSQPVyGIJCJNg0g2rl4Y2rVBJETKCiK1QSREysrCyt8f+IC7T0WJ9PnPt48Q\nKTEbESmNm5sPNHH26UIive1tkyc/36R+hEiJQSQPNzclivS2JqMnESk7iOThZuYDH+hMKluk\nz3++MwmREoNIHm5mShTpbW/rTEIkzSCSh5ubAod2VpGWMrQ7lowQKT+I1GaD20innRiIlJ9F\niVRi+dsq0iLK34ikmYWJdOSWI5K1/N3leIaXLxcSqXegFyLlB5EGXGGsLU2fPJ1z7AsihbmI\npB1EEpGmYWhn4+oFkQZcYTwiveMd3RO96zL5gkhhLiJpp3CR3tHk8EThIi2o/P3SS4POFLxg\nDlN+fxchUuFDu1MKF+mlJtM+lbhgDvKxj7XTgvtbqEjveIdllRSCIVKAu1CRPtakvldwf5ch\nUtHl717KFumllzqTEEktZYs0GtrJuIjk5y5UpI99rDOp4P4ikp80zXJFWujQDpEEcYo0KH8L\nuYgU4C5TJIZ2gnhEiuciUpC7yPI3IoWDSCJuTha6X2YQyt+hIJKIm5M1iHRMwf1FJD8priOI\nlB9EGnCFQSQRNyeIFObmBJFEpLiOIFJ+EGnAFQaRRNycIFKYmxNEEpHiOoJI+UGkAVcYRBJx\nc4JIYW5OEElEiusIIuUn9vP9xCfyuTlBJBEpriNLEenjH2+nyxfpE01yuTlBJBEpriPLEOnj\nTep7iJQfRBKR4jqCSPmJ+nw/8QmxSYgk5iKSiPvxj3cmIVJ+wiK9730ZXFne/OZBu8b2SM5F\nJBF3TSItYGj3vibJXEne3KTXrhn0IpqLSDLuioZ2iFQHkaTcnKxbpOLL3+97X6JJ8v6++c2d\nSYhE+VvCzdmGRyQxF5FiuYiUn5KGdm984+B5RNIMIoW5OSlHpDc26T2PSJpBpDA3J+WUvxEp\nyM0JIoW5ORHskM3hyvPGN45NWqVI731voM1SFsxjISKHm5NlivT2t7ePEMlBiuuIU6T3NvG2\nWcaCeSqN53Bz4uG++93ttDiR3t6kfnRBkTYxtEMkcZzcdzep7yGSNRsQ6b3vDZtUhEi9w4dy\nuDlZoEhvf3tn0iVF2kD5G5HkcXHf/e7OJESSZbEifehD7ZShXUYWKFIhQ7tJFirSh5rU9xAp\nIwsc2iFSmBTREZ9IlL/FWaJIRZS/p1mmSB/6UGcSOzgzssTy9ymI5CDJO4JIOhFwC+5vSSK9\n/vWJ3KKHdsEgkphbcH/LEen1TZK4iBTJ/chH2mnBC6Y1BfcXkRykqI64y9+CnH3B/EiT+l7B\nC6Y1Bfe3GJFe/3qHSUsQ6RhEyggi2bjRQSTtuLgf+UhnUsELpjUF97cYkRY9tDvkuMuo/Rjf\n+lZZm4gk5pbb3ze8oZ0i0pgU35HTQQz1x/jWJpI2GdqJuaX29w1N6nsFiLTc8vchyxNpVLzL\n5WYmzH3Xu9ppcf0tTKRkbhEi9Q70fnT0SGTSpcrfkxVTPjcrIe67muzvvP/9qtzUnLhveENn\nEiKNSdEdWZJITTqRPvxhVW5qhCK9v4kiNzWIJCLFd2QxQ7s2x5rDh5vocZMT4L7rkAJFYmjn\nIcV3BJHyIhPp/e+PNQmRxNwyRFpK+fuYvkcxJl12aFekSEWVvzO4pYh0zDI23pcpUonbSL20\nX/2b3qTNVQgiJSbMPZQaljG0M4fyd/kivamJLlchiJQYIXdBIh1SXPn7lLe8xSDSkJTUkTb/\n8z9RbV58wVxI+bt87luavOlNeSYhUpv/aRLR5vIXILhtNi3Sbh8bKakjdRBpq9y3vKVvkh5X\nKfOKtOtuRqSkjpijRzEmLX4BgtsGkRAJrga39chssvytLhJDu+1yO5GUuTo5l0iva/LKK688\n3eeVjGntUc78TJleaFrUGml55W+4cNsUJlJk4MIthItIcOEqcBEJLlwFLiLBhavALerIhujA\nhVsIt6hj7aIDF24hXESCC1eBi0hw4SpwEQkuXAUuIsGFq8BFJLhwFbiIBBeuAheR4MJV4CIS\nXLgKXESCC1eBi0hw4SpwEQkuXAUuIsGFq8BFJLhwFbiIBBeuAheR4MJV4CISXLgKXESCC1eB\ni0hw4SpwEQkuXAUuIsGFq8BFJLhwFbiIBBeuAheR4MJV4CISXLgKXESCC1eBi0hw4SpwEQku\nXAWuokiEbDhqInklm5E9R+jvvFl1fxHpFPo7b1bdX0Q6hf7Om1X3d06RCNlMEIkQhSASIQpB\nJEIUgkiEKASRCFHIfCLt9pkNrpxjX3cL6fSxn0vq7rG/5Xe47WF/mRDMNJtIu1OXik/X14X0\n99jPJX3Gy/l8d6ePdSf/jBHJINI5spi+7gwiZWW3nO7u+tOFdHpBv1OIlJXdUobwvU0kY5by\nGS9pExSRcrKkBTP2Sy4gu+FN0UGknOwmdwrPAkUa3Ss1iJSRJX3RbRYl0s56t9AgUnp2p9sF\n9Hl5Q7tFfb6IlJxeOXkJXd71/i2iwz2RFtDdskRayl73OruFHSkQu9f98jn+Ui2ivwfrCzmy\ngZAtBZEIUQgiEaIQRCJEIYhEiEIQiRCFIBIhCkEkQhSCSIQoBJEul8f3m8lNyndwmLdNNSA8\ntO2I973l/uPTLUkMIl0sT9ql+aZK+A6eDEwYEqw871t2T063JC2IdLHsHta396sUkdp5j8kV\nqV1BWddkRBhEOnPurqvdTX3n5nBI5JPDUl1Pqur4clW9uLvav3vv2f0787iqR3JPqnr0db2/\nbebdz3BdXd0dZj2+df9gpMndVXXdvuXJddXQD285Ptz3ondLkoJI582zXb0UX+/vVK1O3eqh\nFal5uXnyai9P++7dM1PV5txUrWKHeffetC82s57eOhKpeeG6fu5xVbX09i3dw5r8rLslSUGk\n8+Zmr0ezDnpQHTfue8v9Xp9n5mFtzXEB36+VrvZ371cv7tcY9Qsv7gHtvM2br6qDit1bx+O2\n+oVnV/WT96oX6vmrw1tOD/dOPehuSVIQ6by5d/zVv67uDk8NRDqO1Np79+rbu+pes4w/qW6q\nJ3vNHh/mrWq56hdNa8ld79GgxeaF5sm7xw+uOpFOD/cvX3e3JCmIdN50S/lpcR+IdLw93Ts8\nvqpHXvsVzrXtxWo6q4W5X2FV7aiufa57OCSSlPDRnTepIt2vnu2uzfXO1GuNRJHuV/cePr7r\n3nJ6iEj54aM7b7qhnUik03htP7bbb9K8sB/dvXB6W/PilXho17zwrLt3eohI+eGjO2/qyluz\nge/YRjretvdOFYS60nZXL/a1iMdtpKaK8CBQbHjQ1iQa5pND2eEg0vEh20j5QaTz5q6pUt+z\nVO1s47Oupt0MxEy9etnrcqra1S+aUfnbtM91OZW/b6rTNtKu/5CqXX4Q6cx58ardb3rYj2S8\nInV7WeulvH5/Pb4z3X6ku6v2xcEO2bZ83svd9XGH7P4dV03tvX1L95D9SPlBpEsl6VjV/ryK\nWzSt1BVHNqQHkS6W4fFy8fPqicSxdvlBpIvlScaC+2TnF6nqIqFx9Hd+EOlyGZxTFD+vmkic\nj6QQRCJEIYhEiEIQiRCFIBIhCkEkQhSCSIQoBJEIUQgiEaKQ/we//Q0eNFx3cQAAAABJRU5E\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot_data, aes(x = c(1:nrow(plot_data)), y = as.numeric(count))) +\n",
    "  geom_segment(aes(xend = c(1:nrow(plot_data)), yend = predicted), alpha = .2) +\n",
    "  geom_point(aes(color = residuals), size = 2) +\n",
    "  scale_color_gradient2(low = \"blue\", mid = \"white\", high = \"red\") +\n",
    "  guides(color = FALSE) +\n",
    "  geom_point(aes(y = predicted), shape = 1, size = 2) +\n",
    "  geom_hline(yintercept=0.5, linetype=\"dashed\", color = \"red\") +\n",
    "  theme_bw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "46d981f7-d3bd-4d02-9a9d-8b448df6326f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>R2</th><th scope=col>MSE</th><th scope=col>RMSE</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>train</th><td>0.3422443</td><td>15022.59</td><td>122.5667</td></tr>\n",
       "\t<tr><th scope=row>test</th><td>0.2854741</td><td>40401.94</td><td>201.0023</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & R2 & MSE & RMSE\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ttrain & 0.3422443 & 15022.59 & 122.5667\\\\\n",
       "\ttest & 0.2854741 & 40401.94 & 201.0023\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 3\n",
       "\n",
       "| <!--/--> | R2 &lt;dbl&gt; | MSE &lt;dbl&gt; | RMSE &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| train | 0.3422443 | 15022.59 | 122.5667 |\n",
       "| test | 0.2854741 | 40401.94 | 201.0023 |\n",
       "\n"
      ],
      "text/plain": [
       "      R2        MSE      RMSE    \n",
       "train 0.3422443 15022.59 122.5667\n",
       "test  0.2854741 40401.94 201.0023"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(modelr)\n",
    "resids <- data.frame(\n",
    "  R2 = c(rsquare(lm_bike, data = train_data), rsquare(lm_bike, data = test_data)),\n",
    "  MSE = c(mse(lm_bike, data = train_data),mse(lm_bike, data = test_data)),\n",
    "  RMSE = c(rmse(lm_bike, data = train_data), rmse(lm_bike, data = test_data))#,\n",
    "  #MAE = c(mae(lm_bike, data = train_data), mae(lm_bike, data = test_data)), # if needed \n",
    "  #MAPE = c(mape(lm_bike, data = train_data), mape(lm_bike, data = test_data)) # if needed\n",
    ")\n",
    "          \n",
    "rownames(resids) <- c(\"train\", \"test\") # give names for rows\n",
    "\n",
    "resids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9708bde8-254d-465b-9641-579c7fc9f091",
   "metadata": {},
   "source": [
    "**Conclusion:** Test set has less RSquared and Bigger Errors variation. Train dataset is closer, because it built on training data and model include information about data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3de1dfc-909c-4507-8174-60765f7965af",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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