{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#your id here"
   ]
  },
  {
   "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": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>holiday</th>\n",
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       "      <th>workingday</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>17375</th>\n",
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       "      <td>11</td>\n",
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       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
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       "      <td>1</td>\n",
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       "      <td>22</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
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       "      <td>37</td>\n",
       "      <td>49</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "instant                                                                   \n",
       "1        2011-01-01       1   0     1   0        0        6           0   \n",
       "2        2011-01-01       1   0     1   1        0        6           0   \n",
       "3        2011-01-01       1   0     1   2        0        6           0   \n",
       "4        2011-01-01       1   0     1   3        0        6           0   \n",
       "5        2011-01-01       1   0     1   4        0        6           0   \n",
       "...             ...     ...  ..   ...  ..      ...      ...         ...   \n",
       "17375    2012-12-31       1   1    12  19        0        1           1   \n",
       "17376    2012-12-31       1   1    12  20        0        1           1   \n",
       "17377    2012-12-31       1   1    12  21        0        1           1   \n",
       "17378    2012-12-31       1   1    12  22        0        1           1   \n",
       "17379    2012-12-31       1   1    12  23        0        1           1   \n",
       "\n",
       "         weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "instant                                                                      \n",
       "1                 1  0.24  0.2879  0.81     0.0000       3          13   16  \n",
       "2                 1  0.22  0.2727  0.80     0.0000       8          32   40  \n",
       "3                 1  0.22  0.2727  0.80     0.0000       5          27   32  \n",
       "4                 1  0.24  0.2879  0.75     0.0000       3          10   13  \n",
       "5                 1  0.24  0.2879  0.75     0.0000       0           1    1  \n",
       "...             ...   ...     ...   ...        ...     ...         ...  ...  \n",
       "17375             2  0.26  0.2576  0.60     0.1642      11         108  119  \n",
       "17376             2  0.26  0.2576  0.60     0.1642       8          81   89  \n",
       "17377             1  0.26  0.2576  0.60     0.1642       7          83   90  \n",
       "17378             1  0.26  0.2727  0.56     0.1343      13          48   61  \n",
       "17379             1  0.26  0.2727  0.65     0.1343      12          37   49  \n",
       "\n",
       "[17379 rows x 16 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "db = pd.read_csv(\"hour.csv\",index_col=0, parse_dates= True)\n",
    "db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
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       "      <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>5</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "instant                                                                   \n",
       "1        2011-01-01       1   0     1   0        0        6           0   \n",
       "2        2011-01-01       1   0     1   1        0        6           0   \n",
       "3        2011-01-01       1   0     1   2        0        6           0   \n",
       "4        2011-01-01       1   0     1   3        0        6           0   \n",
       "5        2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "         weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "instant                                                                       \n",
       "1                 1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "2                 1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "3                 1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "4                 1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "5                 1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "          log_cnt  \n",
       "instant            \n",
       "1        2.772589  \n",
       "2        3.688879  \n",
       "3        3.465736  \n",
       "4        2.564949  \n",
       "5        0.000000  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(db[\"cnt\"]) #ln(price)\n",
    "db[\"log_cnt\"] = y\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"temp\"):\n",
    "    if ax is None:\n",
    "        _,ax = plt.subplots(figsize=(8,6))\n",
    "    db.plot.scatter(x=var , y=\"cnt\", alpha=0.1, s=10, ax=ax)\n",
    "    \n",
    "    return ax\n",
    "\n",
    "var_scatter(db);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"temp\"):\n",
    "    if ax is None:\n",
    "        _,ax = plt.subplots(figsize=(8,6))\n",
    "    db.plot.scatter(x=var , y=\"log_cnt\", alpha=0.1, s=10, ax=ax)\n",
    "    \n",
    "    return ax\n",
    "\n",
    "var_scatter(db);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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",
       "      <th>log_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",
       "      <td>0.167350</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",
       "      <td>0.165782</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",
       "      <td>0.115297</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",
       "      <td>0.563126</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",
       "      <td>-0.024935</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",
       "      <td>0.029637</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",
       "      <td>0.001463</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",
       "      <td>-0.119842</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",
       "      <td>0.385724</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",
       "      <td>0.382619</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",
       "      <td>-0.335751</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",
       "      <td>0.113496</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",
       "      <td>0.575246</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",
       "      <td>0.778950</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",
       "      <td>0.806352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_cnt</th>\n",
       "      <td>0.167350</td>\n",
       "      <td>0.165782</td>\n",
       "      <td>0.115297</td>\n",
       "      <td>0.563126</td>\n",
       "      <td>-0.024935</td>\n",
       "      <td>0.029637</td>\n",
       "      <td>0.001463</td>\n",
       "      <td>-0.119842</td>\n",
       "      <td>0.385724</td>\n",
       "      <td>0.382619</td>\n",
       "      <td>-0.335751</td>\n",
       "      <td>0.113496</td>\n",
       "      <td>0.575246</td>\n",
       "      <td>0.778950</td>\n",
       "      <td>0.806352</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",
       "log_cnt     0.167350  0.165782  0.115297  0.563126 -0.024935  0.029637   \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",
       "log_cnt       0.001463   -0.119842  0.385724  0.382619 -0.335751   0.113496   \n",
       "\n",
       "              casual  registered       cnt   log_cnt  \n",
       "season      0.120206    0.174226  0.178056  0.167350  \n",
       "yr          0.142779    0.253684  0.250495  0.165782  \n",
       "mnth        0.068457    0.122273  0.120638  0.115297  \n",
       "hr          0.301202    0.374141  0.394071  0.563126  \n",
       "holiday     0.031564   -0.047345 -0.030927 -0.024935  \n",
       "weekday     0.032721    0.021578  0.026900  0.029637  \n",
       "workingday -0.300942    0.134326  0.030284  0.001463  \n",
       "weathersit -0.152628   -0.120966 -0.142426 -0.119842  \n",
       "temp        0.459616    0.335361  0.404772  0.385724  \n",
       "atemp       0.454080    0.332559  0.400929  0.382619  \n",
       "hum        -0.347028   -0.273933 -0.322911 -0.335751  \n",
       "windspeed   0.090287    0.082321  0.093234  0.113496  \n",
       "casual      1.000000    0.506618  0.694564  0.575246  \n",
       "registered  0.506618    1.000000  0.972151  0.778950  \n",
       "cnt         0.694564    0.972151  1.000000  0.806352  \n",
       "log_cnt     0.575246    0.778950  0.806352  1.000000  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = db.corr()\n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 188,
     "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",
    "\n",
    "sns.set()\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(db.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>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": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.groupby('weathersit')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.groupby('season')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.groupby('holiday')[['cnt']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### People tend to use the bike mostly on fall that not on a holiday with a clear weather."
   ]
  },
  {
   "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": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "X = db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</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>0.0000</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>2</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>0.0000</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>3</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>0.0000</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>4</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>0.0000</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>5</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>0.0000</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",
       "    <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\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",
       "      <td>4.779123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</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>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>4.488636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>4.499810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\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",
       "      <td>4.110874</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17379</th>\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",
       "      <td>1</td>\n",
       "      <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",
       "      <td>3.891820</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "instant                                                                   \n",
       "1        2011-01-01       1   0     1   0        0        6           0   \n",
       "2        2011-01-01       1   0     1   1        0        6           0   \n",
       "3        2011-01-01       1   0     1   2        0        6           0   \n",
       "4        2011-01-01       1   0     1   3        0        6           0   \n",
       "5        2011-01-01       1   0     1   4        0        6           0   \n",
       "...             ...     ...  ..   ...  ..      ...      ...         ...   \n",
       "17375    2012-12-31       1   1    12  19        0        1           1   \n",
       "17376    2012-12-31       1   1    12  20        0        1           1   \n",
       "17377    2012-12-31       1   1    12  21        0        1           1   \n",
       "17378    2012-12-31       1   1    12  22        0        1           1   \n",
       "17379    2012-12-31       1   1    12  23        0        1           1   \n",
       "\n",
       "         weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "instant                                                                       \n",
       "1                 1  0.24  0.2879  0.81     0.0000       3          13   16   \n",
       "2                 1  0.22  0.2727  0.80     0.0000       8          32   40   \n",
       "3                 1  0.22  0.2727  0.80     0.0000       5          27   32   \n",
       "4                 1  0.24  0.2879  0.75     0.0000       3          10   13   \n",
       "5                 1  0.24  0.2879  0.75     0.0000       0           1    1   \n",
       "...             ...   ...     ...   ...        ...     ...         ...  ...   \n",
       "17375             2  0.26  0.2576  0.60     0.1642      11         108  119   \n",
       "17376             2  0.26  0.2576  0.60     0.1642       8          81   89   \n",
       "17377             1  0.26  0.2576  0.60     0.1642       7          83   90   \n",
       "17378             1  0.26  0.2727  0.56     0.1343      13          48   61   \n",
       "17379             1  0.26  0.2727  0.65     0.1343      12          37   49   \n",
       "\n",
       "          log_cnt  const  \n",
       "instant                   \n",
       "1        2.772589      1  \n",
       "2        3.688879      1  \n",
       "3        3.465736      1  \n",
       "4        2.564949      1  \n",
       "5        0.000000      1  \n",
       "...           ...    ...  \n",
       "17375    4.779123      1  \n",
       "17376    4.488636      1  \n",
       "17377    4.499810      1  \n",
       "17378    4.110874      1  \n",
       "17379    3.891820      1  \n",
       "\n",
       "[17379 rows x 18 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['const'] = 1\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import (linear_model, metrics, model_selection) ## For model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_mod11 = ['atemp','weathersit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE Model 1 = 1.0724        Train MAE Model 1 = 1.0751\n",
      "Test RMSE Model 1 = 1.3660      Train RMSE Model 1 = 1.3684\n"
     ]
    }
   ],
   "source": [
    "mod1_lr_model = linear_model.LinearRegression()\n",
    "\n",
    "model1 = mod1_lr_model.fit(X_train[var_mod11], y_train)\n",
    "\n",
    "mae_test_mod1 = metrics.mean_absolute_error(y_test, model1.predict(X_test[var_mod11]))\n",
    "\n",
    "mae_train_mod1 = metrics.mean_absolute_error(y_train, model1.predict(X_train[var_mod11]))\n",
    "\n",
    "rmse_test_mod1 = metrics.mean_squared_error(y_test, model1.predict(X_test[var_mod11]), squared = False)\n",
    "\n",
    "rmse_train_mod1 = metrics.mean_squared_error(y_train, model1.predict(X_train[var_mod11]), squared = False)\n",
    "\n",
    "print(f'Test MAE Model 1 = {mae_test_mod1:.4f}        Train MAE Model 1 = {mae_train_mod1:.4f}')\n",
    "\n",
    "print(f'Test RMSE Model 1 = {rmse_test_mod1:.4f}      Train RMSE Model 1 = {rmse_train_mod1:.4f}')\n"
   ]
  },
  {
   "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": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "X=db[['atemp',\n",
    " 'weathersit']]\n",
    "y=db[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 10-fold CV on the train data: 1.3678610242566687\n",
      "RMSE (Model1:Linear) on 10-fold CV on the test data: 1.3679043294659836\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(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \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 (Model1:Linear) on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model1))\n",
    "print('RMSE (Model1:Linear) on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 10-fold CV on the train data: 1.0759664497437678\n",
      "MAE (Model1:Linear) on 10-fold CV on the test data: 1.0762204641787287\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": "markdown",
   "metadata": {},
   "source": [
    "### Feature All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=db[[\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",
    "y=db[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model2:Linear) on 10-fold CV on the train data: 0.7696518370075838\n",
      "RMSE (Model2:Linear) on 10-fold CV on the test data: 0.7700756933536294\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(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_10cv_model2 = err_train/10              #average the rmse\n",
    "rmse_test_10cv_model2 = err_test/10              #average the rmse\n",
    "print('RMSE (Model2:Linear) on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model2))\n",
    "print('RMSE (Model2:Linear) on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model2:Linear) on 10-fold CV on the train data: 0.5848129507735343\n",
      "MAE (Model2:Linear) on 10-fold CV on the test data: 0.5851967709958508\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_model2 = err_train/10              #average the rmse\n",
    "mae_test_10cv_model2 = err_test/10              #average the rmse\n",
    "print('MAE (Model2:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model2))\n",
    "print('MAE (Model2:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model2))"
   ]
  },
  {
   "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": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "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": [
    "#Here is the space for your codes\n",
    "\n",
    "### We should use all features because its has lower RMSE and MAE.  \n",
    "\n",
    "### RMSE: RMSE can be interpreted as the standard deviation of the unexplained variance. Lower RMSE indicate better fit. So all feature moel is better.\n",
    "\n",
    "### MAE: the model accuracy given on the same scale as the prediction target. In the model is 0.5 which is a very low number. Close to 0 is good."
   ]
  },
  {
   "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": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "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": 210,
   "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']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(unemp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jan', 3.5),\n",
       " ('Feb', 3.5),\n",
       " ('Mar', 4.4),\n",
       " ('Apr', 14.8),\n",
       " ('May', 13.3),\n",
       " ('June', 11.1),\n",
       " ('July', 10.2),\n",
       " ('Aug', 8.4),\n",
       " ('Sep', 7.8),\n",
       " ('Oct', 6.9),\n",
       " ('Nov', 6.7),\n",
       " ('Dec', 6.7)]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(month, unemp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apr has an employment of 14.8%\n",
      "Apr has an employment of 13.3%\n",
      "Apr has an employment of 11.1%\n",
      "Apr has an employment of 10.2%\n",
      "Apr has an employment of 8.4%\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "for value in unemp:\n",
    "    if value > 8:\n",
    "        unemp = value\n",
    "        print(f\"{month[3]} has an employment of {unemp}%\")"
   ]
  },
  {
   "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": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
       " 4,\n",
       " 6,\n",
       " 8,\n",
       " 10,\n",
       " 12,\n",
       " 14,\n",
       " 16,\n",
       " 18,\n",
       " 20,\n",
       " 22,\n",
       " 24,\n",
       " 26,\n",
       " 28,\n",
       " 30,\n",
       " 32,\n",
       " 34,\n",
       " 36,\n",
       " 38,\n",
       " 40,\n",
       " 42,\n",
       " 44,\n",
       " 46,\n",
       " 48,\n",
       " 50,\n",
       " 52,\n",
       " 54,\n",
       " 56,\n",
       " 58,\n",
       " 60,\n",
       " 62,\n",
       " 64,\n",
       " 66,\n",
       " 68,\n",
       " 70,\n",
       " 72,\n",
       " 74,\n",
       " 76,\n",
       " 78,\n",
       " 80,\n",
       " 82,\n",
       " 84,\n",
       " 86,\n",
       " 88,\n",
       " 90,\n",
       " 92,\n",
       " 94,\n",
       " 96,\n",
       " 98,\n",
       " 100,\n",
       " 102,\n",
       " 104,\n",
       " 106,\n",
       " 108,\n",
       " 110,\n",
       " 112,\n",
       " 114,\n",
       " 116,\n",
       " 118,\n",
       " 120,\n",
       " 122,\n",
       " 124,\n",
       " 126,\n",
       " 128,\n",
       " 130,\n",
       " 132,\n",
       " 134,\n",
       " 136,\n",
       " 138,\n",
       " 140,\n",
       " 142,\n",
       " 144,\n",
       " 146,\n",
       " 148,\n",
       " 150,\n",
       " 152,\n",
       " 154,\n",
       " 156,\n",
       " 158,\n",
       " 160,\n",
       " 162,\n",
       " 164,\n",
       " 166,\n",
       " 168,\n",
       " 170,\n",
       " 172,\n",
       " 174,\n",
       " 176,\n",
       " 178,\n",
       " 180,\n",
       " 182,\n",
       " 184,\n",
       " 186,\n",
       " 188,\n",
       " 190,\n",
       " 192,\n",
       " 194,\n",
       " 196,\n",
       " 198,\n",
       " 200,\n",
       " 202,\n",
       " 204,\n",
       " 206,\n",
       " 208,\n",
       " 210,\n",
       " 212,\n",
       " 214,\n",
       " 216,\n",
       " 218,\n",
       " 220,\n",
       " 222,\n",
       " 224,\n",
       " 226,\n",
       " 228,\n",
       " 230,\n",
       " 232,\n",
       " 234,\n",
       " 236,\n",
       " 238,\n",
       " 240,\n",
       " 242,\n",
       " 244,\n",
       " 246,\n",
       " 248,\n",
       " 250,\n",
       " 252,\n",
       " 254,\n",
       " 256,\n",
       " 258,\n",
       " 260,\n",
       " 262,\n",
       " 264,\n",
       " 266,\n",
       " 268,\n",
       " 270,\n",
       " 272,\n",
       " 274,\n",
       " 276,\n",
       " 278,\n",
       " 280,\n",
       " 282,\n",
       " 284,\n",
       " 286,\n",
       " 288,\n",
       " 290,\n",
       " 292,\n",
       " 294,\n",
       " 296,\n",
       " 298,\n",
       " 300,\n",
       " 302,\n",
       " 304,\n",
       " 306,\n",
       " 308,\n",
       " 310,\n",
       " 312,\n",
       " 314,\n",
       " 316,\n",
       " 318,\n",
       " 320,\n",
       " 322,\n",
       " 324,\n",
       " 326,\n",
       " 328,\n",
       " 330,\n",
       " 332,\n",
       " 334,\n",
       " 336,\n",
       " 338,\n",
       " 340,\n",
       " 342,\n",
       " 344,\n",
       " 346,\n",
       " 348,\n",
       " 350,\n",
       " 352,\n",
       " 354,\n",
       " 356,\n",
       " 358,\n",
       " 360,\n",
       " 362,\n",
       " 364,\n",
       " 366,\n",
       " 368,\n",
       " 370,\n",
       " 372,\n",
       " 374,\n",
       " 376,\n",
       " 378,\n",
       " 380,\n",
       " 382,\n",
       " 384,\n",
       " 386,\n",
       " 388,\n",
       " 390,\n",
       " 392,\n",
       " 394,\n",
       " 396,\n",
       " 398,\n",
       " 400,\n",
       " 402,\n",
       " 404,\n",
       " 406,\n",
       " 408,\n",
       " 410,\n",
       " 412,\n",
       " 414,\n",
       " 416,\n",
       " 418,\n",
       " 420,\n",
       " 422,\n",
       " 424,\n",
       " 426,\n",
       " 428,\n",
       " 430,\n",
       " 432,\n",
       " 434,\n",
       " 436,\n",
       " 438,\n",
       " 440,\n",
       " 442,\n",
       " 444,\n",
       " 446,\n",
       " 448,\n",
       " 450,\n",
       " 452,\n",
       " 454,\n",
       " 456,\n",
       " 458,\n",
       " 460,\n",
       " 462,\n",
       " 464,\n",
       " 466,\n",
       " 468,\n",
       " 470,\n",
       " 472,\n",
       " 474,\n",
       " 476,\n",
       " 478,\n",
       " 480,\n",
       " 482,\n",
       " 484,\n",
       " 486,\n",
       " 488,\n",
       " 490,\n",
       " 492,\n",
       " 494,\n",
       " 496,\n",
       " 498,\n",
       " 500]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(range(2,502,2))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1124.196900951069\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "\n",
    "import math\n",
    "\n",
    "total = 0\n",
    "\n",
    "for value in x:\n",
    "    if value < 102:\n",
    "        continue\n",
    "    total = total + math.log(value)\n",
    "\n",
    "print(total)"
   ]
  },
  {
   "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": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "# We will import all these here to ensure that they are loaded, but\n",
    "# will usually re-import close to where they are used to make clear\n",
    "# where the functions come from\n",
    "from sklearn import (linear_model, metrics, model_selection)  #when import sklearn, need to import metrics, model selection also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>1.329333</td>\n",
       "      <td>1.329333</td>\n",
       "      <td>968637000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-08-01</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>1.478667</td>\n",
       "      <td>1.159333</td>\n",
       "      <td>1.298667</td>\n",
       "      <td>1.298667</td>\n",
       "      <td>225573000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-01</td>\n",
       "      <td>1.308000</td>\n",
       "      <td>1.544000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>270688500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>1.379333</td>\n",
       "      <td>1.458000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>98217000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>1.462667</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.403333</td>\n",
       "      <td>2.355333</td>\n",
       "      <td>2.355333</td>\n",
       "      <td>424726500</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>286.923340</td>\n",
       "      <td>318.500000</td>\n",
       "      <td>206.856674</td>\n",
       "      <td>252.753326</td>\n",
       "      <td>252.753326</td>\n",
       "      <td>1948221600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>251.720001</td>\n",
       "      <td>264.209991</td>\n",
       "      <td>208.693329</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>2011227900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>298.320007</td>\n",
       "      <td>216.166672</td>\n",
       "      <td>297.149994</td>\n",
       "      <td>297.149994</td>\n",
       "      <td>1744884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>301.276672</td>\n",
       "      <td>314.666656</td>\n",
       "      <td>271.809998</td>\n",
       "      <td>275.609985</td>\n",
       "      <td>275.609985</td>\n",
       "      <td>1695263200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>272.579987</td>\n",
       "      <td>313.799988</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>1298653000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2010-07-01    1.666667    1.728000    0.998667    1.329333    1.329333   \n",
       "1    2010-08-01    1.366667    1.478667    1.159333    1.298667    1.298667   \n",
       "2    2010-09-01    1.308000    1.544000    1.300000    1.360667    1.360667   \n",
       "3    2010-10-01    1.379333    1.458000    1.333333    1.456000    1.456000   \n",
       "4    2010-11-01    1.462667    2.400000    1.403333    2.355333    2.355333   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "142  2022-05-01  286.923340  318.500000  206.856674  252.753326  252.753326   \n",
       "143  2022-06-01  251.720001  264.209991  208.693329  224.473328  224.473328   \n",
       "144  2022-07-01  227.000000  298.320007  216.166672  297.149994  297.149994   \n",
       "145  2022-08-01  301.276672  314.666656  271.809998  275.609985  275.609985   \n",
       "146  2022-09-01  272.579987  313.799988  262.470001  265.250000  265.250000   \n",
       "\n",
       "         Volume  \n",
       "0     968637000  \n",
       "1     225573000  \n",
       "2     270688500  \n",
       "3      98217000  \n",
       "4     424726500  \n",
       "..          ...  \n",
       "142  1948221600  \n",
       "143  2011227900  \n",
       "144  1744884000  \n",
       "145  1695263200  \n",
       "146  1298653000  \n",
       "\n",
       "[147 rows x 7 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"TSLA.csv\")\n",
    "df"
   ]
  },
  {
   "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": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.728000</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>1.329333</td>\n",
       "      <td>1.329333</td>\n",
       "      <td>968637000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>1.366667</td>\n",
       "      <td>1.478667</td>\n",
       "      <td>1.159333</td>\n",
       "      <td>1.298667</td>\n",
       "      <td>1.298667</td>\n",
       "      <td>225573000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>1.308000</td>\n",
       "      <td>1.544000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>270688500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1.379333</td>\n",
       "      <td>1.458000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>98217000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>1.462667</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.403333</td>\n",
       "      <td>2.355333</td>\n",
       "      <td>2.355333</td>\n",
       "      <td>424726500</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>286.923340</td>\n",
       "      <td>318.500000</td>\n",
       "      <td>206.856674</td>\n",
       "      <td>252.753326</td>\n",
       "      <td>252.753326</td>\n",
       "      <td>1948221600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>251.720001</td>\n",
       "      <td>264.209991</td>\n",
       "      <td>208.693329</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>2011227900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>227.000000</td>\n",
       "      <td>298.320007</td>\n",
       "      <td>216.166672</td>\n",
       "      <td>297.149994</td>\n",
       "      <td>297.149994</td>\n",
       "      <td>1744884000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>301.276672</td>\n",
       "      <td>314.666656</td>\n",
       "      <td>271.809998</td>\n",
       "      <td>275.609985</td>\n",
       "      <td>275.609985</td>\n",
       "      <td>1695263200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>272.579987</td>\n",
       "      <td>313.799988</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>1298653000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2010-07-01    1.666667    1.728000    0.998667    1.329333    1.329333   \n",
       "2010-08-01    1.366667    1.478667    1.159333    1.298667    1.298667   \n",
       "2010-09-01    1.308000    1.544000    1.300000    1.360667    1.360667   \n",
       "2010-10-01    1.379333    1.458000    1.333333    1.456000    1.456000   \n",
       "2010-11-01    1.462667    2.400000    1.403333    2.355333    2.355333   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-05-01  286.923340  318.500000  206.856674  252.753326  252.753326   \n",
       "2022-06-01  251.720001  264.209991  208.693329  224.473328  224.473328   \n",
       "2022-07-01  227.000000  298.320007  216.166672  297.149994  297.149994   \n",
       "2022-08-01  301.276672  314.666656  271.809998  275.609985  275.609985   \n",
       "2022-09-01  272.579987  313.799988  262.470001  265.250000  265.250000   \n",
       "\n",
       "                Volume  \n",
       "Date                    \n",
       "2010-07-01   968637000  \n",
       "2010-08-01   225573000  \n",
       "2010-09-01   270688500  \n",
       "2010-10-01    98217000  \n",
       "2010-11-01   424726500  \n",
       "...                ...  \n",
       "2022-05-01  1948221600  \n",
       "2022-06-01  2011227900  \n",
       "2022-07-01  1744884000  \n",
       "2022-08-01  1695263200  \n",
       "2022-09-01  1298653000  \n",
       "\n",
       "[147 rows x 6 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"TSLA.csv\",index_col=0, parse_dates= True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 147 entries, 2010-07-01 to 2022-09-01\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       147 non-null    float64\n",
      " 1   High       147 non-null    float64\n",
      " 2   Low        147 non-null    float64\n",
      " 3   Close      147 non-null    float64\n",
      " 4   Adj Close  147 non-null    float64\n",
      " 5   Volume     147 non-null    int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 8.0 KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"TSLA.csv\",index_col=0,parse_dates=['Date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.329333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>1.298667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>1.360667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1.456000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>2.355333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>252.753326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>224.473328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>297.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>275.609985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>265.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adj Close\n",
       "Date                  \n",
       "2010-07-01    1.329333\n",
       "2010-08-01    1.298667\n",
       "2010-09-01    1.360667\n",
       "2010-10-01    1.456000\n",
       "2010-11-01    2.355333\n",
       "...                ...\n",
       "2022-05-01  252.753326\n",
       "2022-06-01  224.473328\n",
       "2022-07-01  297.149994\n",
       "2022-08-01  275.609985\n",
       "2022-09-01  265.250000\n",
       "\n",
       "[147 rows x 1 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.drop([\"Open\", \"High\", \"Low\", \"Close\",\"Volume\"], axis = 1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2['2010':'2022'].plot(title='TSLA Price', legend=True)\n",
    "ax.set_xlabel('year', fontsize=12)\n",
    "ax.set_ylabel('price', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>Adj Close</th>\n",
       "      <th>log_Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.329333</td>\n",
       "      <td>0.284677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>1.298667</td>\n",
       "      <td>0.261338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>1.360667</td>\n",
       "      <td>0.307975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1.456000</td>\n",
       "      <td>0.375693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>2.355333</td>\n",
       "      <td>0.856682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close  log_Adj Close\n",
       "Date                                \n",
       "2010-07-01   1.329333       0.284677\n",
       "2010-08-01   1.298667       0.261338\n",
       "2010-09-01   1.360667       0.307975\n",
       "2010-10-01   1.456000       0.375693\n",
       "2010-11-01   2.355333       0.856682"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(df2[\"Adj Close\"]) \n",
    "df2[\"log_Adj Close\"] = y\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>log_Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>0.284677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>0.261338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>0.307975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>0.375693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>0.856682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>5.532414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>5.413757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>5.694237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>5.618987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>5.580673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            log_Adj Close\n",
       "Date                     \n",
       "2010-07-01       0.284677\n",
       "2010-08-01       0.261338\n",
       "2010-09-01       0.307975\n",
       "2010-10-01       0.375693\n",
       "2010-11-01       0.856682\n",
       "...                   ...\n",
       "2022-05-01       5.532414\n",
       "2022-06-01       5.413757\n",
       "2022-07-01       5.694237\n",
       "2022-08-01       5.618987\n",
       "2022-09-01       5.580673\n",
       "\n",
       "[147 rows x 1 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.drop([\"Adj Close\"], axis = 1)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df3['2010':'2022'].plot(title='TSLA Price', legend=True)\n",
    "ax.set_xlabel('year', fontsize=12)\n",
    "ax.set_ylabel('price', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "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": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x262bce5fb20>"
      ]
     },
     "execution_count": 227,
     "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "\n",
    "df4 = sns.histplot(\"log_Adj Close\")\n",
    "sns.displot(df2, x=\"Adj Close\")\n",
    "\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
}