{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#6504930105"
   ]
  },
  {
   "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": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import (linear_model, metrics, model_selection)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "    log_cnt  \n",
       "0  2.772589  \n",
       "1  3.688879  \n",
       "2  3.465736  \n",
       "3  2.564949  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "df= pd.read_csv('hour.csv')\n",
    "#convert target value into log\n",
    "y = np.log(df[\"cnt\"]) \n",
    "df[\"log_cnt\"] = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='temp', ylabel='log_cnt'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot scatterplot\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=\"log_cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "var_scatter(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.0"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate median\n",
    "np.median(df['cnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [40]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#calculate median\u001b[39;00m\n\u001b[0;32m      2\u001b[0m l \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcnt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweathersit\u001b[39m\u001b[38;5;124m'\u001b[39m]])\n\u001b[1;32m----> 3\u001b[0m np\u001b[38;5;241m.\u001b[39mmedian(\u001b[43ml\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcnt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
     ]
    }
   ],
   "source": [
    "l = list(df[['cnt', 'weathersit']])\n",
    "np.median(l['cnt'], axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"cnt\", \"log_cnt\"], axis=1).copy()\n",
    "y = df[\"log_cnt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)\n",
    "lr_model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>1085</td>\n",
       "      <td>2011-02-17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.4848</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>8</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3755</th>\n",
       "      <td>3756</td>\n",
       "      <td>2011-06-10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>37</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13755</th>\n",
       "      <td>13756</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>32</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4992</th>\n",
       "      <td>4993</td>\n",
       "      <td>2011-07-31</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>25</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12986</th>\n",
       "      <td>12987</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.5758</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>10</td>\n",
       "      <td>82</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16019</th>\n",
       "      <td>16020</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16104</th>\n",
       "      <td>16105</td>\n",
       "      <td>2012-11-08</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4091</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.4179</td>\n",
       "      <td>16</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>930</td>\n",
       "      <td>2011-02-11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.1212</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>4</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16009</th>\n",
       "      <td>16010</td>\n",
       "      <td>2012-11-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.3485</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>31</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>16499</td>\n",
       "      <td>2012-11-25</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13903 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "1084      1085  2011-02-17       1   0     2  22        0        4   \n",
       "3755      3756  2011-06-10       2   0     6   9        0        5   \n",
       "13755    13756  2012-08-01       3   1     8   8        0        3   \n",
       "4992      4993  2011-07-31       3   0     7  22        0        0   \n",
       "12986    12987  2012-06-30       3   1     6   7        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "16019    16020  2012-11-05       4   1    11   4        0        1   \n",
       "16104    16105  2012-11-08       4   1    11  18        0        4   \n",
       "929        930  2011-02-11       1   0     2   8        0        5   \n",
       "16009    16010  2012-11-04       4   1    11  18        0        0   \n",
       "16498    16499  2012-11-25       4   1    11   4        0        0   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "1084            1           1  0.50  0.4848  0.59     0.2836       8   \n",
       "3755            1           1  0.76  0.6970  0.55     0.0000      37   \n",
       "13755           1           2  0.66  0.6061  0.78     0.1940      32   \n",
       "4992            0           1  0.74  0.6970  0.70     0.2985      25   \n",
       "12986           0           1  0.64  0.5758  0.89     0.1642      10   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "16019           1           2  0.30  0.2879  0.52     0.2537       5   \n",
       "16104           1           1  0.40  0.4091  0.24     0.4179      16   \n",
       "929             1           1  0.10  0.1212  0.74     0.1642       4   \n",
       "16009           0           1  0.36  0.3485  0.43     0.2239      31   \n",
       "16498           0           1  0.22  0.2727  0.44     0.0000       1   \n",
       "\n",
       "       registered  \n",
       "1084           68  \n",
       "3755          176  \n",
       "13755         649  \n",
       "4992           71  \n",
       "12986          82  \n",
       "...           ...  \n",
       "16019          11  \n",
       "16104         491  \n",
       "929           212  \n",
       "16009         206  \n",
       "16498           1  \n",
       "\n",
       "[13903 rows x 16 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: log(cnt) = 3.2815 + 3.2008 atemp + -0.1915 weathersit\n"
     ]
    }
   ],
   "source": [
    "model1 = lr_model.fit(X_train[[\"atemp\",\"weathersit\"]], y_train) \n",
    "beta_0 = lr_model.intercept_\n",
    "beta_1 = lr_model.coef_[0]\n",
    "beta_2 = lr_model.coef_[1]\n",
    "\n",
    "print(f\"Fit model: log(cnt) = {beta_0:.4f} + {beta_1:.4f} atemp + {beta_2:.4f} weathersit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation of variables:**\n",
    "\n",
    "With each rise in temperature the estimated average increase in the log of count of bikes is 302.08, holding the variable weathersit constant. \n",
    "\n",
    "With each rise in weather situation the estimated average decrease in the log of count of bikes is 19.15, holding the variable atemp constant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0609770390162445 Model 1 MAE_test\n",
      "1.081227882448117 Model 1 MAE_train\n"
     ]
    }
   ],
   "source": [
    "MAE_test_1 = metrics.mean_absolute_error(y_test,model1.predict(X_test[[\"atemp\",\"weathersit\"]]) )\n",
    "MAE_train_1 = metrics.mean_absolute_error(y_train, model1.predict(X_train[[\"atemp\",\"weathersit\"]]))\n",
    "print(MAE_test_1, 'Model 1 MAE_test')\n",
    "print(MAE_train_1, 'Model 1 MAE_train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3485565569501459 Model 1 RMSE_test\n",
      "1.3726825422633553 Model 1 RMSE_train\n"
     ]
    }
   ],
   "source": [
    "RMSE_test_1 = metrics.mean_squared_error(y_test,model1.predict(X_test[[\"atemp\",\"weathersit\"]]) , squared=False)\n",
    "RMSE_train_1 = metrics.mean_squared_error(y_train,model1.predict(X_train[[\"atemp\",\"weathersit\"]]) , squared=False)\n",
    "print(RMSE_test_1, 'Model 1 RMSE_test')\n",
    "print(RMSE_train_1, 'Model 1 RMSE_train')"
   ]
  },
  {
   "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": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['atemp',\n",
    " 'weathersit']]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the train data: 1.3678542027076968\n",
      "RMSE on 10-fold CV on the test data: 1.3679826216466222\n",
      "MAE (Model1:Linear) on 10-fold CV on the train data: 1.075965589464371\n",
      "MAE (Model1:Linear) on 10-fold CV on the test data: 1.0762769755244075\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_5cv_model1))\n",
    "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_5cv_model1))\n",
    "\n",
    "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              \n",
    "mae_test_10cv_model1 = err_test/10              \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": "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": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "X=df[['atemp',\n",
    " 'weathersit', 'holiday', 'weekday', 'workingday','temp','hum','windspeed']]\n",
    "y=df['log_cnt']\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 5-fold CV on the train data: 1.285055828515238\n",
      "RMSE on 5-fold CV on the test data: 1.2860648623821445\n",
      "MAE (Model1:Linear) on 5-fold CV on the train data: 0.9961982648297409\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 0.9970603562114781\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_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))\n",
    "\n",
    "\n",
    "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_5cv_model1 = err_train/5              \n",
    "mae_test_5cv_model1 = err_test/5              \n",
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "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": [
    "Answer: \n",
    "The RMSE for all three models are very similiar. However the one for model 3 is slightly smaller than the other ones and therefore the model with all features should be used. \n",
    "\n",
    "Additionally the model does not seem to have a problem with overfitting. When comparing the values for RMSE between train data and test data, there is only a small variation between the two values. Therefore, the model does not have a problem with overfiting\n"
   ]
  },
  {
   "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": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marth\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual price</th>\n",
       "      <th>Predicted price</th>\n",
       "      <th>Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.772589</td>\n",
       "      <td>3.392295</td>\n",
       "      <td>-0.619706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.688879</td>\n",
       "      <td>3.367345</td>\n",
       "      <td>0.321535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.367345</td>\n",
       "      <td>0.098391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.564949</td>\n",
       "      <td>3.543888</td>\n",
       "      <td>-0.978939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-3.543888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>4.779123</td>\n",
       "      <td>3.911723</td>\n",
       "      <td>0.867401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>4.488636</td>\n",
       "      <td>3.911723</td>\n",
       "      <td>0.576913</td>\n",
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       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>4.499810</td>\n",
       "      <td>3.784114</td>\n",
       "      <td>0.715696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>3.912255</td>\n",
       "      <td>0.198619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>3.891820</td>\n",
       "      <td>3.684865</td>\n",
       "      <td>0.206956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual price  Predicted price  Residual\n",
       "0          2.772589         3.392295 -0.619706\n",
       "1          3.688879         3.367345  0.321535\n",
       "2          3.465736         3.367345  0.098391\n",
       "3          2.564949         3.543888 -0.978939\n",
       "4          0.000000         3.543888 -3.543888\n",
       "...             ...              ...       ...\n",
       "17374      4.779123         3.911723  0.867401\n",
       "17375      4.488636         3.911723  0.576913\n",
       "17376      4.499810         3.784114  0.715696\n",
       "17377      4.110874         3.912255  0.198619\n",
       "17378      3.891820         3.684865  0.206956\n",
       "\n",
       "[17379 rows x 3 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "model1 = lr.fit(X_train[[\"atemp\", \"weathersit\", \"holiday\", \"weekday\", \"workingday\",\"temp\",\"hum\",\"windspeed\"]], y_train) \n",
    "predict1 = model1.predict(X)\n",
    "table = pd.DataFrame({'Actual price':y, 'Predicted price':predict1,'Residual':y-predict1, })\n",
    "table"
   ]
  },
  {
   "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": 75,
   "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": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unemploxment rate in Apr is 14.8\n",
      "The unemploxment rate in May is 13.3\n",
      "The unemploxment rate in June is 11.1\n",
      "The unemploxment rate in July is 10.2\n",
      "The unemploxment rate in Aug is 8.4\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "for rate, month_n in zip(unemp, month):\n",
    "    if rate > 8:\n",
    "        print(f\"The unemploxment rate in {month_n} is {rate}\")\n",
    "    \n",
    "    "
   ]
  },
  {
   "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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183.13512597977032"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "even = list(range(2, 501, 2))\n",
    "sum_log = 0\n",
    "for i in even:\n",
    "    if i > 100:\n",
    "        break\n",
    "    sum_log = sum_log + np.log(i)\n",
    "print(sum_log)\n"
   ]
  },
  {
   "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": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "import pandas_datareader.data as web\n",
    "import datetime as dt\n",
    "df_TESLA = web.DataReader('TSLA', 'yahoo', start='2010-06-29', end='2022-09-30')\n",
    "df_TESLA = df_TESLA.drop(['Volume'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_TESLA.plot(figsize = (20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_return = np.log(df_TESLA['Adj Close']/df_TESLA['Adj Close'].shift(1))\n",
    "log_return.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 1, which plots the prices of the stock over given time serious is nonstationary.\n",
    "Plot 2, which plots the log returns is stationary. "
   ]
  },
  {
   "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": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Adj Close', ylabel='Count'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_theme(style=\"ticks\")\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "sns.despine(f)\n",
    "\n",
    "sns.histplot(\n",
    "    log_return,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}