{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#your id here\n",
    "#6204640178"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exam Instruction EE522 Fall 2022:\n",
    "    \n",
    "1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
    "\n",
    "2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
    "\n",
    "3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
    "\n",
    "4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
    "\n",
    "\n",
    "5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
    "\n",
    "6. You have to submit your code on BE-moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 [50 points]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Information:\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
    "\n",
    "Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\t\n",
    "=========================================\n",
    "Dataset characteristics\n",
    "======================\t\n",
    "hour.csv has the following fields:\n",
    "\t\n",
    "\t- instant: record index\n",
    "\t- dteday : date\n",
    "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "\t- yr : year (0: 2011, 1:2012)\n",
    "\t- mnth : month ( 1 to 12)\n",
    "\t- hr : hour (0 to 23)\n",
    "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "\t- weekday : day of the week\n",
    "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\t+ weathersit : \n",
    "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\t- casual: count of casual users\n",
    "\t- registered: count of registered users\n",
    "\t- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.1 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
    "\n",
    "Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
    "\n",
    "\n",
    "[20 Points]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "\n",
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\n",
       "      <td>2012-12-31</td>\n",
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       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the scatter plots to visualize the relationship between temp and target values (cnt)\n",
    "def var_scatter(df, ax=None, var=\"temp\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"cnt\",alpha=0.4, s=1.5, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the heatmap to represent the correlation matrix between features and target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.178056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>-0.010742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.120638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>0.394071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>-0.009585</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>-0.030927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>-0.002335</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.030284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.014524</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>-0.142426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.404772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>0.400929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>-0.322911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.149773</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.093234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.694564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.178056</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.394071</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.030284</td>\n",
       "      <td>-0.142426</td>\n",
       "      <td>0.404772</td>\n",
       "      <td>0.400929</td>\n",
       "      <td>-0.322911</td>\n",
       "      <td>0.093234</td>\n",
       "      <td>0.694564</td>\n",
       "      <td>0.972151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              season        yr      mnth        hr   holiday   weekday  \\\n",
       "season      1.000000 -0.010742  0.830386 -0.006117 -0.009585 -0.002335   \n",
       "yr         -0.010742  1.000000 -0.010473 -0.003867  0.006692 -0.004485   \n",
       "mnth        0.830386 -0.010473  1.000000 -0.005772  0.018430  0.010400   \n",
       "hr         -0.006117 -0.003867 -0.005772  1.000000  0.000479 -0.003498   \n",
       "holiday    -0.009585  0.006692  0.018430  0.000479  1.000000 -0.102088   \n",
       "weekday    -0.002335 -0.004485  0.010400 -0.003498 -0.102088  1.000000   \n",
       "workingday  0.013743 -0.002196 -0.003477  0.002285 -0.252471  0.035955   \n",
       "weathersit -0.014524 -0.019157  0.005400 -0.020203 -0.017036  0.003311   \n",
       "temp        0.312025  0.040913  0.201691  0.137603 -0.027340 -0.001795   \n",
       "atemp       0.319380  0.039222  0.208096  0.133750 -0.030973 -0.008821   \n",
       "hum         0.150625 -0.083546  0.164411 -0.276498 -0.010588 -0.037158   \n",
       "windspeed  -0.149773 -0.008740 -0.135386  0.137252  0.003988  0.011502   \n",
       "casual      0.120206  0.142779  0.068457  0.301202  0.031564  0.032721   \n",
       "registered  0.174226  0.253684  0.122273  0.374141 -0.047345  0.021578   \n",
       "cnt         0.178056  0.250495  0.120638  0.394071 -0.030927  0.026900   \n",
       "\n",
       "            workingday  weathersit      temp     atemp       hum  windspeed  \\\n",
       "season        0.013743   -0.014524  0.312025  0.319380  0.150625  -0.149773   \n",
       "yr           -0.002196   -0.019157  0.040913  0.039222 -0.083546  -0.008740   \n",
       "mnth         -0.003477    0.005400  0.201691  0.208096  0.164411  -0.135386   \n",
       "hr            0.002285   -0.020203  0.137603  0.133750 -0.276498   0.137252   \n",
       "holiday      -0.252471   -0.017036 -0.027340 -0.030973 -0.010588   0.003988   \n",
       "weekday       0.035955    0.003311 -0.001795 -0.008821 -0.037158   0.011502   \n",
       "workingday    1.000000    0.044672  0.055390  0.054667  0.015688  -0.011830   \n",
       "weathersit    0.044672    1.000000 -0.102640 -0.105563  0.418130   0.026226   \n",
       "temp          0.055390   -0.102640  1.000000  0.987672 -0.069881  -0.023125   \n",
       "atemp         0.054667   -0.105563  0.987672  1.000000 -0.051918  -0.062336   \n",
       "hum           0.015688    0.418130 -0.069881 -0.051918  1.000000  -0.290105   \n",
       "windspeed    -0.011830    0.026226 -0.023125 -0.062336 -0.290105   1.000000   \n",
       "casual       -0.300942   -0.152628  0.459616  0.454080 -0.347028   0.090287   \n",
       "registered    0.134326   -0.120966  0.335361  0.332559 -0.273933   0.082321   \n",
       "cnt           0.030284   -0.142426  0.404772  0.400929 -0.322911   0.093234   \n",
       "\n",
       "              casual  registered       cnt  \n",
       "season      0.120206    0.174226  0.178056  \n",
       "yr          0.142779    0.253684  0.250495  \n",
       "mnth        0.068457    0.122273  0.120638  \n",
       "hr          0.301202    0.374141  0.394071  \n",
       "holiday     0.031564   -0.047345 -0.030927  \n",
       "weekday     0.032721    0.021578  0.026900  \n",
       "workingday -0.300942    0.134326  0.030284  \n",
       "weathersit -0.152628   -0.120966 -0.142426  \n",
       "temp        0.459616    0.335361  0.404772  \n",
       "atemp       0.454080    0.332559  0.400929  \n",
       "hum        -0.347028   -0.273933 -0.322911  \n",
       "windspeed   0.090287    0.082321  0.093234  \n",
       "casual      1.000000    0.506618  0.694564  \n",
       "registered  0.506618    1.000000  0.972151  \n",
       "cnt         0.694564    0.972151  1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.copy()\n",
    "df1 = df1.drop([\"instant\"], axis=1).copy()\n",
    "correlation_data = df1.corr()\n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df1.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Also, calculate the median of cout of total bikes (cnt) \n",
    "#separated by weathersit, season ,and holiday. \n",
    "#Is there any interesting thing you found? Explain carefully."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Ans. The interesting thing that I found is that the count of total rental bikes including both casual and registered is highest in the weather type 1 (Clear, Few clouds, Partly cloudy, Partly cloudy), in season springer, and in the weather day that is not a holiday. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cnt\n",
       "weathersit       \n",
       "1           159.0\n",
       "2           133.0\n",
       "3            63.0\n",
       "4            36.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('weathersit')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cnt\n",
       "season       \n",
       "1        76.0\n",
       "2       165.0\n",
       "3       199.0\n",
       "4       155.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('season')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cnt\n",
       "holiday       \n",
       "0        144.0\n",
       "1         97.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('holiday')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1   0        0        6           0           1   \n",
       "1  2011-01-01       1   0     1   1        0        6           0           1   \n",
       "2  2011-01-01       1   0     1   2        0        6           0           1   \n",
       "3  2011-01-01       1   0     1   3        0        6           0           1   \n",
       "4  2011-01-01       1   0     1   4        0        6           0           1   \n",
       "\n",
       "   temp   atemp   hum  windspeed  casual  registered  cnt   log_cnt  const  \n",
       "0  0.24  0.2879  0.81        0.0       3          13   16  2.772589      1  \n",
       "1  0.22  0.2727  0.80        0.0       8          32   40  3.688879      1  \n",
       "2  0.22  0.2727  0.80        0.0       5          27   32  3.465736      1  \n",
       "3  0.24  0.2879  0.75        0.0       3          10   13  2.564949      1  \n",
       "4  0.24  0.2879  0.75        0.0       0           1    1  0.000000      1  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   dteday      17379 non-null  object \n",
      " 1   season      17379 non-null  int64  \n",
      " 2   yr          17379 non-null  int64  \n",
      " 3   mnth        17379 non-null  int64  \n",
      " 4   hr          17379 non-null  int64  \n",
      " 5   holiday     17379 non-null  int64  \n",
      " 6   weekday     17379 non-null  int64  \n",
      " 7   workingday  17379 non-null  int64  \n",
      " 8   weathersit  17379 non-null  int64  \n",
      " 9   temp        17379 non-null  float64\n",
      " 10  atemp       17379 non-null  float64\n",
      " 11  hum         17379 non-null  float64\n",
      " 12  windspeed   17379 non-null  float64\n",
      " 13  casual      17379 non-null  int64  \n",
      " 14  registered  17379 non-null  int64  \n",
      " 15  cnt         17379 non-null  int64  \n",
      " 16  log_cnt     17379 non-null  float64\n",
      " 17  const       17379 non-null  int64  \n",
      "dtypes: float64(5), int64(12), object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp  \\\n",
       "0       1   0     1   0        0        6           0           1  0.24   \n",
       "1       1   0     1   1        0        6           0           1  0.22   \n",
       "2       1   0     1   2        0        6           0           1  0.22   \n",
       "3       1   0     1   3        0        6           0           1  0.24   \n",
       "4       1   0     1   4        0        6           0           1  0.24   \n",
       "\n",
       "    atemp   hum  windspeed  casual  registered  const  \n",
       "0  0.2879  0.81        0.0       3          13      1  \n",
       "1  0.2727  0.80        0.0       8          32      1  \n",
       "2  0.2727  0.80        0.0       5          27      1  \n",
       "3  0.2879  0.75        0.0       3          10      1  \n",
       "4  0.2879  0.75        0.0       0           1      1  "
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df1.drop(['cnt','log_cnt','dteday'], axis=1).copy()\n",
    "y = np.log(df1['cnt'])\n",
    "df1['log_cnt'] = y\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.772589\n",
       "1    3.688879\n",
       "2    3.465736\n",
       "3    2.564949\n",
       "4    0.000000\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the dataset into 80 % train data and 20 % test data\n",
    "X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use two features which are atemp and weatherlist to train the Regression algorithm \n",
    "#and make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model1: log(crt) = 3.2629 + 3.2355 atemp + -0.1868 weathersit\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "from sklearn import linear_model\n",
    "\n",
    "# construct the model instance\n",
    "model1_lr = linear_model.LinearRegression()\n",
    "\n",
    "# fit the model\n",
    "model1 = model1_lr.fit(X[['atemp','weathersit']], y) \n",
    "\n",
    "# print the coefficients\n",
    "beta_0 = model1.intercept_ #or can use =model1.intercept_\n",
    "beta_1 = model1.coef_[0]\n",
    "beta_2 = model1.coef_[1]\n",
    "\n",
    "print(f\"Fit model1: log(crt) = {beta_0:.4f} + {beta_1:.4f} atemp + {beta_2:.4f} weathersit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0791817202855527 MAE_test1\n",
      "1.0751794813426003 MAE_train1\n"
     ]
    }
   ],
   "source": [
    "MAE_test1 = metrics.mean_absolute_error(y_test,model1.predict(X_test[['atemp','weathersit']]))\n",
    "MAE_train1 = metrics.mean_absolute_error(y_train,model1.predict(X_train[['atemp','weathersit']]))\n",
    "\n",
    "print(MAE_test1,'MAE_test1')\n",
    "print(MAE_train1,'MAE_train1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3665913209333318 RMSE_test1\n",
      "1.3681873756766816 RMSE_train1\n"
     ]
    }
   ],
   "source": [
    "RMSE_test1 = metrics.mean_squared_error(y_test,model1.predict(X_test[['atemp','weathersit']]),squared = False)\n",
    "RMSE_train1 = metrics.mean_squared_error(y_train,model1.predict(X_train[['atemp','weathersit']]),squared = False)\n",
    "\n",
    "print(RMSE_test1,'RMSE_test1')\n",
    "print(RMSE_train1,'RMSE_train1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Make prediction\n",
    "\n",
    "Ans.On average, for one unit increase in atemp, the count of total bike rental will increase by 323.55%. \n",
    "\n",
    "On average, for one unit increase in weathersit, the count of total bike rental will decrease by 18.68%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#What is the MAE and RMSE of train and test data in this model? \n",
    "#Interpret the coefficients of these tow variables carefully.\n",
    "\n",
    "Ans. The RMSE and MAE are quite similar for both the test and train data. \n",
    "This means that our model with 2 coefficients is quite good as it has similar \n",
    "predictive power when we either use the train or test data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.3 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt',\n",
       " 'log_cnt',\n",
       " 'const']"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df1[['atemp','weathersit']]\n",
    "y=df1[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 10-fold CV on the train data: 1.0759777192190874\n",
      "MAE (Model1:Linear) on 10-fold CV on the test data: 1.0760807089791418\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_10cv_model1 = err_train/10              #average the rmse\n",
    "mae_test_10cv_model1 = err_test/10              #average the rmse\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model1))\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the train data: 1.367851926571254\n",
      "RMSE on 10-fold CV on the test data: 1.367971999867151\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))     # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "rmse_train_10cv_model1 = err_train/10             #average the rmse\n",
    "rmse_test_10cv_model1 = err_test/10              #average the rmse\n",
    "print('RMSE on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model1))\n",
    "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last excercise, using all features which are\n",
    "- holiday : weather day is holiday or not \n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model2: log(crt) = 4.3078 + -0.1735 holiday + 0.0140 weekday + -0.0688 workingday + 0.1303 weathersit + 0.0219 temp + 3.2168 atemp + -2.52094 hum +0.4852 windspeed\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "from sklearn import linear_model\n",
    "\n",
    "# construct the model instance\n",
    "model2_lr = linear_model.LinearRegression()\n",
    "\n",
    "# fit the model\n",
    "model2 = model2_lr.fit(X[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']], y) \n",
    "\n",
    "# print the coefficients\n",
    "beta_0 = model2.intercept_ #or can use =model1.intercept_\n",
    "beta_1 = model2.coef_[0]\n",
    "beta_2 = model2.coef_[1]\n",
    "beta_3 = model2.coef_[2]\n",
    "beta_4 = model2.coef_[3]\n",
    "beta_5 = model2.coef_[4]\n",
    "beta_6 = model2.coef_[5]\n",
    "beta_7 = model2.coef_[6]\n",
    "beta_8 = model2.coef_[7]\n",
    "\n",
    "\n",
    "print(f\"Fit model2: log(crt) = {beta_0:.4f} + {beta_1:.4f} holiday + {beta_2:.4f} weekday + {beta_3:.4f} workingday + {beta_4:.4f} weathersit + {beta_5:.4f} temp + {beta_6:.4f} atemp + {beta_7:.5f} hum +{beta_8:.4f} windspeed\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0045013315980174 MAE_test2\n",
      "0.9942507448696232 MAE_train2\n"
     ]
    }
   ],
   "source": [
    "MAE_test2 = metrics.mean_absolute_error(y_test,model2.predict(X_test[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']]))\n",
    "MAE_train2 = metrics.mean_absolute_error(y_train,model2.predict(X_train[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']]))\n",
    "\n",
    "print(MAE_test2,'MAE_test2')\n",
    "print(MAE_train2,'MAE_train2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2879748211214048 RMSE_test2\n",
      "1.2844663583715965 RMSE_train2\n"
     ]
    }
   ],
   "source": [
    "RMSE_test2 = metrics.mean_squared_error(y_test,model2.predict(X_test[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']]),squared = False)\n",
    "RMSE_train2 = metrics.mean_squared_error(y_train,model2.predict(X_train[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']]),squared = False)\n",
    "\n",
    "print(RMSE_test2,'RMSE_test2')\n",
    "print(RMSE_train2,'RMSE_train2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ans. The RMSE and MAE are quite similar for both the test and train data. However, both are lower than our previous model with only 2 coefficients. Thus, our model with all features is a better model for predicting the independent variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.4 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ans. We should use all features in our modle as it does not exist a problem of overfitting as observed by the lower RMSE and MAE of the model with all features compared to the model with two features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.5 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "fit() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [373]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predict \u001b[38;5;241m=\u001b[39m \u001b[43mmodel2_lr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mholiday\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweekday\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mworkingday\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweathersit\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtemp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43matemp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwindspeed\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: fit() missing 1 required positional argument: 'y'"
     ]
    }
   ],
   "source": [
    "predict = model2_lr.fit(X[['holiday', 'weekday','workingday','weathersit','temp','atemp','hum','windspeed']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'predict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [374]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Here is the space for your codes\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m table1 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredict value\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[43mpredict\u001b[49m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactual value\u001b[39m\u001b[38;5;124m'\u001b[39m:y,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror/residual\u001b[39m\u001b[38;5;124m'\u001b[39m:y\u001b[38;5;241m-\u001b[39mpredict})\n",
      "\u001b[1;31mNameError\u001b[0m: name 'predict' is not defined"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "table1 = pd.DataFrame({'predict value':predict,'actual value':y,'error/residual':y-predict})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The umemployment rate in Apr is 14.8%.\n",
      "The umemployment rate in May is 13.3%.\n",
      "The umemployment rate in June is 11.1%.\n",
      "The umemployment rate in July is 10.2%.\n",
      "The umemployment rate in Aug is 8.4%.\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "x = zip(unemp,month)\n",
    "\n",
    "for i,j in x:\n",
    "    if i>8:\n",
    "        print(f'The umemployment rate in {j} is {i}%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
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       " 494,\n",
       " 496,\n",
       " 498,\n",
       " 500]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "x = list(range(2,502,2))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer is 500\n"
     ]
    }
   ],
   "source": [
    "total = 0 \n",
    "for i in range(2,502,2):\n",
    "    total = total + log(i) \n",
    "    \n",
    "print(\"The answer is\", i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2010-06-29</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.169333</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>281494500.0</td>\n",
       "      <td>1.592667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>2.028000</td>\n",
       "      <td>1.553333</td>\n",
       "      <td>1.719333</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>257806500.0</td>\n",
       "      <td>1.588667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>123282000.0</td>\n",
       "      <td>1.464000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>77097000.0</td>\n",
       "      <td>1.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>103003500.0</td>\n",
       "      <td>1.074000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                High       Low      Open     Close       Volume  Adj Close\n",
       "Date                                                                      \n",
       "2010-06-29  1.666667  1.169333  1.266667  1.592667  281494500.0   1.592667\n",
       "2010-06-30  2.028000  1.553333  1.719333  1.588667  257806500.0   1.588667\n",
       "2010-07-01  1.728000  1.351333  1.666667  1.464000  123282000.0   1.464000\n",
       "2010-07-02  1.540000  1.247333  1.533333  1.280000   77097000.0   1.280000\n",
       "2010-07-06  1.333333  1.055333  1.333333  1.074000  103003500.0   1.074000"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader.data as web\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "df = web.DataReader('TSLA', 'yahoo', start='2010-06-29', end='2022-09-30')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Adj Close']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.169333</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>281494500.0</td>\n",
       "      <td>1.592667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>2.028000</td>\n",
       "      <td>1.553333</td>\n",
       "      <td>1.719333</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>257806500.0</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>-0.002515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.728000</td>\n",
       "      <td>1.351333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>123282000.0</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>-0.081723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.247333</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>77097000.0</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>-0.134312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.055333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>103003500.0</td>\n",
       "      <td>1.074000</td>\n",
       "      <td>-0.175470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                High       Low      Open     Close       Volume  Adj Close  \\\n",
       "Date                                                                         \n",
       "2010-06-29  1.666667  1.169333  1.266667  1.592667  281494500.0   1.592667   \n",
       "2010-06-30  2.028000  1.553333  1.719333  1.588667  257806500.0   1.588667   \n",
       "2010-07-01  1.728000  1.351333  1.666667  1.464000  123282000.0   1.464000   \n",
       "2010-07-02  1.540000  1.247333  1.533333  1.280000   77097000.0   1.280000   \n",
       "2010-07-06  1.333333  1.055333  1.333333  1.074000  103003500.0   1.074000   \n",
       "\n",
       "              return  \n",
       "Date                  \n",
       "2010-06-29       NaN  \n",
       "2010-06-30 -0.002515  \n",
       "2010-07-01 -0.081723  \n",
       "2010-07-02 -0.134312  \n",
       "2010-07-06 -0.175470  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['return'] = np.log(df[['Adj Close']]).diff()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['return']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['Date', 'df[['return']]'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [273]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Here is the space for your codes\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m; sns\u001b[38;5;241m.\u001b[39mset_theme(color_codes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m----> 4\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlmplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdf[[\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreturn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m]]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscatter_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.35\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[0;32m     44\u001b[0m     )\n\u001b[0;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:605\u001b[0m, in \u001b[0;36mlmplot\u001b[1;34m(x, y, data, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, facet_kws, size)\u001b[0m\n\u001b[0;32m    603\u001b[0m need_cols \u001b[38;5;241m=\u001b[39m [x, y, hue, col, row, units, x_partial, y_partial]\n\u001b[0;32m    604\u001b[0m cols \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique([a \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m need_cols \u001b[38;5;28;01mif\u001b[39;00m a \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m])\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m--> 605\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcols\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;66;03m# Initialize the grid\u001b[39;00m\n\u001b[0;32m    608\u001b[0m facets \u001b[38;5;241m=\u001b[39m FacetGrid(\n\u001b[0;32m    609\u001b[0m     data, row\u001b[38;5;241m=\u001b[39mrow, col\u001b[38;5;241m=\u001b[39mcol, hue\u001b[38;5;241m=\u001b[39mhue,\n\u001b[0;32m    610\u001b[0m     palette\u001b[38;5;241m=\u001b[39mpalette,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    613\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfacet_kws,\n\u001b[0;32m    614\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3511\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3509\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m   3510\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 3511\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   3513\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m   3514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   5779\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   5780\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 5782\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5784\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   5785\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   5786\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5842\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   5840\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[0;32m   5841\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 5842\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   5844\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m   5845\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['Date', 'df[['return']]'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "import seaborn as sns; sns.set_theme(color_codes=True)\n",
    "\n",
    "sns.lmplot(\n",
    "    data=df, x=\"Date\", y = \"df[['return']]\", height=6,\n",
    "    scatter_kws=dict(s=1.5, alpha=0.35));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'TLSA' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [275]\u001b[0m, in \u001b[0;36m<cell line: 17>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m         ticker[tick] \u001b[38;5;241m=\u001b[39m closing_prices\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ticker\n\u001b[1;32m---> 17\u001b[0m ticker \u001b[38;5;241m=\u001b[39m read_data(\u001b[43mTLSA\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'TLSA' is not defined"
     ]
    }
   ],
   "source": [
    "def read_data(ticker_list,\n",
    "          start=dt.datetime(2021, 1, 2),\n",
    "          end=dt.datetime(2021, 12, 31)):\n",
    "    \"\"\"\n",
    "    This function reads in closing price data from Yahoo\n",
    "    for each tick in the ticker_list.\n",
    "    \"\"\"\n",
    "    ticker = pd.DataFrame() #Create an empty dataframe-- just like an empty excel sheet.\n",
    "\n",
    "    for tick in ticker_list:\n",
    "        prices = data.DataReader(tick, 'TSLA', start, end)\n",
    "        closing_prices = prices['Close']\n",
    "        ticker[tick] = closing_prices\n",
    "\n",
    "    return ticker\n",
    "\n",
    "ticker = read_data(TLSA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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