{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ID Number: 6504930246"
   ]
  },
  {
   "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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",
    "    df.plot.scatter(x=var , y=\"cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(df[\"cnt\"])\n",
    "df[\"log_cnt\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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",
    "    df.plot.scatter(x=var , y=\"log_cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"cnt\"]\n",
    "\n",
    "CorrX = df.drop(['instant','casual','registered','yr'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 75,
     "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": [
    "correlation_data = CorrX.corr()  #-1<rho<1\n",
    "correlation_data\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(CorrX.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            cnt\n",
      "weathersit     \n",
      "1           159\n",
      "2           133\n",
      "3            63\n",
      "4            36\n",
      "          cnt\n",
      "season       \n",
      "1        76.0\n",
      "2       165.0\n",
      "3       199.0\n",
      "4       155.5\n",
      "         cnt\n",
      "holiday     \n",
      "0        144\n",
      "1         97\n"
     ]
    }
   ],
   "source": [
    "tablevac = pd.pivot_table(df, values = \"cnt\", index = [\"weathersit\"], aggfunc = np.median)\n",
    "\n",
    "\n",
    "tablesw = pd.pivot_table(df, values = \"cnt\", index = [\"season\"], aggfunc = np.median)\n",
    "tablesw\n",
    "\n",
    "tableslot = pd.pivot_table(df, values = \"cnt\", index = [\"holiday\"], aggfunc = np.median)\n",
    "\n",
    "\n",
    "tablefull = pd.concat([tablevac, tablesw, tableslot], axis = 0)\n",
    "print(tablevac)\n",
    "print(tablesw)\n",
    "print(tableslot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, after seperating out the median count of bikes by weatersit, season, and holiday I did find a few interesting insights. First, there are massive changes in the median count of bikes rented based on the weather outside. For example, for weathersit's 3 and 4, which I would infer as the worst weather conditions, the median count of bike rentals drops dramatically.\n",
    "\n",
    "As for season, there are also a few interesting insights. The spring is the season with the least amount of median bike rentals, with all of the other seasons close to one another, and fall taking the lead.\n",
    "\n",
    "Lastly, there are more median bike rentals when the day is not a holiday (holiday = 0), than when it is a holiday."
   ]
  },
  {
   "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": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  float64\n",
      " 1   yr          17379 non-null  float64\n",
      " 2   mnth        17379 non-null  float64\n",
      " 3   hr          17379 non-null  float64\n",
      " 4   holiday     17379 non-null  float64\n",
      " 5   weekday     17379 non-null  float64\n",
      " 6   workingday  17379 non-null  float64\n",
      " 7   weathersit  17379 non-null  float64\n",
      " 8   temp        17379 non-null  float64\n",
      " 9   atemp       17379 non-null  float64\n",
      " 10  hum         17379 non-null  float64\n",
      " 11  windspeed   17379 non-null  float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "from sklearn import linear_model\n",
    "\n",
    "X = df.drop(['dteday', 'registered','casual', 'cnt','instant'], axis=1).copy()\n",
    "\n",
    "for col in list(X):\n",
    "    X[col] = X[col].astype(float)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 40, 32, ..., 90, 61, 49])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)\n",
    "\n",
    "X=X[[\"atemp\",\"weathersit\"]]\n",
    "y=df['cnt']\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 5-fold CV on the train data: 165.1506794495342\n",
      "RMSE on 5-fold CV on the test data: 165.1533197184638\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))     # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "rmse_train_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 124.83440401344187\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 124.8602402075796\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_5cv_model1 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: Count = 35.1846 + 407.2695 atemp + -28.5961 weathersit\n"
     ]
    }
   ],
   "source": [
    "beta_0 = lr.intercept_\n",
    "beta_1 = lr.coef_[0]\n",
    "beta_2 = lr.coef_[1]\n",
    "\n",
    "print(f\"Fit model: Count = {beta_0:.4f} + {beta_1:.4f} atemp + {beta_2:.4f} weathersit\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, the coefficients of the data mean that with an increase of 1 degree of temperature (which is on an index of 0-1), there is an expected increase of 407 bike rentals. Secondly, with an increase in 1 of the weather situation, with the 3rd and 4th weather situations being supposedly the worst for biking (rainy and snowy), we can expect a decrease in 28 bike rentals"
   ]
  },
  {
   "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": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the train data: 165.1506794495342\n",
      "RMSE on 10-fold CV on the test data: 165.1533197184638\n",
      "122.9791565053305 MAE_test\n",
      "125.59030047639206 MAE_train\n"
     ]
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))     # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "rmse_train_10cv_model1 = err_train/10            #average the rmse\n",
    "rmse_test_10cv_model1 = err_test/10              #average the rmse\n",
    "print('RMSE on 10-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_5cv_model1))\n",
    "MAE_train = metrics.mean_absolute_error(y_train1, model1.predict(X_train1))\n",
    "MAE_test = metrics.mean_absolute_error(y_test1, model1.predict(X_test1))\n",
    "print(MAE_test, \"MAE_test\")\n",
    "print(MAE_train, \"MAE_train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " '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']"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)\n",
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['holiday',\n",
    " 'weekday',\n",
    " 'workingday',\n",
    " 'weathersit',\n",
    " 'temp',\n",
    " 'atemp',\n",
    " 'hum',\n",
    " 'windspeed']]\n",
    "y=df[['cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model3:Linear) on 5-fold CV on the train data: 156.58112220215713\n",
      "RMSE (Model3:Linear) on 5-fold CV on the test data: 156.66568005958405\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_5cv_model3 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model3 = err_test/5              #average the rmse\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model3))\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model2:Linear) on 5-fold CV on the train data: 116.76307706390662\n",
      "MAE (Model2:Linear) on 5-fold CV on the test data: 116.82423853507149\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_5cv_model2 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model2 = err_test/5              #average the rmse\n",
    "print('MAE (Model2:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model2))\n",
    "print('MAE (Model2:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model2))"
   ]
  },
  {
   "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 of the features since the RMSE and MAE of the regression models using all of the features are lower than the 2 features. There may be a problem with overfitting since we are using all of the variables instead of a select few."
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "predict1 = list(lr_model.predict(X[[\"COUPON\",\"DISTANCE\",\"Vacation_dum\"]]))\n",
    "\n",
    "dfres = pd.DataFrame({\"Model 1 Exp. FARE\" : predict1, \"Actual FARE1\" : y, \"Residual1\" : y - predict1, \"Model 2 Exp. FARE\" : predict2, \"Actual FARE2\" : y, \"Residual2\" : y - predict2, \"Model 3 Exp. FARE\" : predict3, \"Actual FARE3\" : y, \"Residual3\" : y - predict3})\n",
    "dfres"
   ]
  },
  {
   "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": 165,
   "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",
    "\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = zip(unemp, month)\n",
    "data = list(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unemployment rate in Apr is 14.8% \n",
      "The unemployment rate in May is 13.3% \n",
      "The unemployment rate in June is 11.1% \n",
      "The unemployment rate in July is 10.2% \n",
      "The unemployment rate in Aug is 8.4% \n"
     ]
    }
   ],
   "source": [
    "for i in range(len(data)):\n",
    "    if data[i][0] > 8:\n",
    "        print( f\"The unemployment rate in {data[i][1]} is {data[i][0]}% \")\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "\n",
    "list1 = list(range(2,501,2))\n",
    "list1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum of the log of numbers 2 - 100 are 438.36236130895935\n"
     ]
    }
   ],
   "source": [
    "summ = 0\n",
    "count = 0\n",
    "i=0\n",
    "\n",
    "while i < 101:\n",
    "    x = np.log(list1[i])\n",
    "    summ = summ + x\n",
    "    i = i + 1\n",
    "\n",
    "print(\"The sum of the log of numbers 2 - 100 are\", summ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker_list = {'TSLA' : 'Tesla'}\n",
    "\n",
    "from matplotlib import pyplot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  TSLA\n",
      "Date                  \n",
      "2010-06-29    1.592667\n",
      "2010-06-30    1.588667\n",
      "2010-07-01    1.464000\n",
      "2010-07-02    1.280000\n",
      "2010-07-06    1.074000\n",
      "...                ...\n",
      "2022-09-26  276.010010\n",
      "2022-09-27  282.940002\n",
      "2022-09-28  287.809998\n",
      "2022-09-29  268.209991\n",
      "2022-09-30  265.250000\n",
      "\n",
      "[3087 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here is the space for your codes\n",
    "def read_data(ticker_list,\n",
    "          start=dt.datetime(2010, 6, 29),\n",
    "          end=dt.datetime(2022, 9, 30)):\n",
    "    \"\"\"\n",
    "    This function reads in closing price data from Yahoo\n",
    "    for each tick in the ticker_list.\n",
    "    \"\"\"\n",
    "    ticker = pd.DataFrame()\n",
    "\n",
    "    for tick in ticker_list:\n",
    "        prices = data.DataReader(tick, 'yahoo', start, end)\n",
    "        closing_prices = prices['Close']\n",
    "        ticker[tick] = closing_prices\n",
    "\n",
    "    return ticker\n",
    "\n",
    "ticker = read_data(ticker_list)\n",
    "print(ticker)\n",
    "\n",
    "p1 = ticker.iloc[0]    #Get the first set of prices as a Series\n",
    "p2 = ticker.iloc[-1]   #Get the last set of prices as a Series\n",
    "price_change = (p2 - p1) / p1 * 100\n",
    "price_change\n",
    "\n",
    "ax = ticker.plot(lw=2)\n",
    "ax.set_xlabel('Date', fontsize=12)\n",
    "ax.set_ylabel('Stock Price', fontsize=12)\n",
    "plt.title(\"Tesla Stock Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ticker[\"log_returns\"] = np.log(ticker)\n",
    "ticker\n",
    "\n",
    "ax = ticker[\"log_returns\"].plot(lw=2)\n",
    "ax.set_xlabel('Date', fontsize=12)\n",
    "ax.set_ylabel('Log Stock Price', fontsize=12)\n",
    "plt.title(\"Tesla Log Stock Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because both the Stock price and Log Stock price of Tesla stock increases from 2010 to 2022, as shown on the graphs above, and does not settle around 1 point, the stationary condition of this data is false"
   ]
  },
  {
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "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 code\n",
    "\n",
    "import seaborn as sns\n",
    "ax = sns.displot(ticker, x=\"log_returns\")"
   ]
  }
 ],
 "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
}