{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6304640391"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6304640391 #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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: qeds in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (0.7.0)\n",
      "Requirement already satisfied: plotly in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (5.6.0)\n",
      "Requirement already satisfied: statsmodels in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (0.13.2)\n",
      "Requirement already satisfied: quandl in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (from qeds) (3.7.0)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (1.21.5)\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (1.4.2)\n",
      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (3.5.1)\n",
      "Requirement already satisfied: pyarrow in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (from qeds) (9.0.0)\n",
      "Requirement already satisfied: pandas-datareader in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (from qeds) (0.10.0)\n",
      "Requirement already satisfied: seaborn in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (0.11.2)\n",
      "Requirement already satisfied: quantecon in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (from qeds) (0.5.3)\n",
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      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from qeds) (1.0.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->qeds) (0.11.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->qeds) (1.3.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->qeds) (3.0.4)\n",
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      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->qeds) (2.8.2)\n",
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      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->qeds) (1.16.0)\n",
      "Requirement already satisfied: et-xmlfile in c:\\programdata\\anaconda3\\lib\\site-packages (from openpyxl->qeds) (1.1.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas->qeds) (2021.3)\n",
      "Requirement already satisfied: lxml in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas-datareader->qeds) (4.8.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->qeds) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->qeds) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->qeds) (1.26.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->qeds) (2021.10.8)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from plotly->qeds) (8.0.1)\n",
      "Requirement already satisfied: more-itertools in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (from quandl->qeds) (8.14.0)\n",
      "Requirement already satisfied: inflection>=0.3.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from quandl->qeds) (0.5.1)\n",
      "Requirement already satisfied: sympy in c:\\programdata\\anaconda3\\lib\\site-packages (from quantecon->qeds) (1.10.1)\n",
      "Requirement already satisfied: numba in c:\\programdata\\anaconda3\\lib\\site-packages (from quantecon->qeds) (0.55.1)\n",
      "Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in c:\\programdata\\anaconda3\\lib\\site-packages (from numba->quantecon->qeds) (0.38.0)\n",
      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from numba->quantecon->qeds) (61.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->qeds) (2.2.0)\n",
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      "Requirement already satisfied: patsy>=0.5.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from statsmodels->qeds) (0.5.2)\n",
      "Requirement already satisfied: mpmath>=0.19 in c:\\programdata\\anaconda3\\lib\\site-packages (from sympy->quantecon->qeds) (1.2.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install qeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all necessary tools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</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>instant</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404046</td>\n",
       "      <td>0.866014</td>\n",
       "      <td>0.489164</td>\n",
       "      <td>-0.004775</td>\n",
       "      <td>0.014723</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.003416</td>\n",
       "      <td>-0.014198</td>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>-0.074505</td>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.278379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0.404046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.178056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>0.866014</td>\n",
       "      <td>-0.010742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnth</th>\n",
       "      <td>0.489164</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.120638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>-0.004775</td>\n",
       "      <td>-0.006117</td>\n",
       "      <td>-0.003867</td>\n",
       "      <td>-0.005772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>0.394071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.014723</td>\n",
       "      <td>-0.009585</td>\n",
       "      <td>0.006692</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>-0.030927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.001357</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>-0.004485</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003498</td>\n",
       "      <td>-0.102088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.026900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>-0.003416</td>\n",
       "      <td>0.013743</td>\n",
       "      <td>-0.002196</td>\n",
       "      <td>-0.003477</td>\n",
       "      <td>0.002285</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>0.035955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.030284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.014198</td>\n",
       "      <td>-0.014524</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>-0.142426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.404772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>0.400929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.009577</td>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>-0.322911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.074505</td>\n",
       "      <td>-0.149773</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.093234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.158295</td>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.694564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.282046</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.278379</td>\n",
       "      <td>0.178056</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.394071</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.030284</td>\n",
       "      <td>-0.142426</td>\n",
       "      <td>0.404772</td>\n",
       "      <td>0.400929</td>\n",
       "      <td>-0.322911</td>\n",
       "      <td>0.093234</td>\n",
       "      <td>0.694564</td>\n",
       "      <td>0.972151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instant    season        yr      mnth        hr   holiday  \\\n",
       "instant     1.000000  0.404046  0.866014  0.489164 -0.004775  0.014723   \n",
       "season      0.404046  1.000000 -0.010742  0.830386 -0.006117 -0.009585   \n",
       "yr          0.866014 -0.010742  1.000000 -0.010473 -0.003867  0.006692   \n",
       "mnth        0.489164  0.830386 -0.010473  1.000000 -0.005772  0.018430   \n",
       "hr         -0.004775 -0.006117 -0.003867 -0.005772  1.000000  0.000479   \n",
       "holiday     0.014723 -0.009585  0.006692  0.018430  0.000479  1.000000   \n",
       "weekday     0.001357 -0.002335 -0.004485  0.010400 -0.003498 -0.102088   \n",
       "workingday -0.003416  0.013743 -0.002196 -0.003477  0.002285 -0.252471   \n",
       "weathersit -0.014198 -0.014524 -0.019157  0.005400 -0.020203 -0.017036   \n",
       "temp        0.136178  0.312025  0.040913  0.201691  0.137603 -0.027340   \n",
       "atemp       0.137615  0.319380  0.039222  0.208096  0.133750 -0.030973   \n",
       "hum         0.009577  0.150625 -0.083546  0.164411 -0.276498 -0.010588   \n",
       "windspeed  -0.074505 -0.149773 -0.008740 -0.135386  0.137252  0.003988   \n",
       "casual      0.158295  0.120206  0.142779  0.068457  0.301202  0.031564   \n",
       "registered  0.282046  0.174226  0.253684  0.122273  0.374141 -0.047345   \n",
       "cnt         0.278379  0.178056  0.250495  0.120638  0.394071 -0.030927   \n",
       "\n",
       "             weekday  workingday  weathersit      temp     atemp       hum  \\\n",
       "instant     0.001357   -0.003416   -0.014198  0.136178  0.137615  0.009577   \n",
       "season     -0.002335    0.013743   -0.014524  0.312025  0.319380  0.150625   \n",
       "yr         -0.004485   -0.002196   -0.019157  0.040913  0.039222 -0.083546   \n",
       "mnth        0.010400   -0.003477    0.005400  0.201691  0.208096  0.164411   \n",
       "hr         -0.003498    0.002285   -0.020203  0.137603  0.133750 -0.276498   \n",
       "holiday    -0.102088   -0.252471   -0.017036 -0.027340 -0.030973 -0.010588   \n",
       "weekday     1.000000    0.035955    0.003311 -0.001795 -0.008821 -0.037158   \n",
       "workingday  0.035955    1.000000    0.044672  0.055390  0.054667  0.015688   \n",
       "weathersit  0.003311    0.044672    1.000000 -0.102640 -0.105563  0.418130   \n",
       "temp       -0.001795    0.055390   -0.102640  1.000000  0.987672 -0.069881   \n",
       "atemp      -0.008821    0.054667   -0.105563  0.987672  1.000000 -0.051918   \n",
       "hum        -0.037158    0.015688    0.418130 -0.069881 -0.051918  1.000000   \n",
       "windspeed   0.011502   -0.011830    0.026226 -0.023125 -0.062336 -0.290105   \n",
       "casual      0.032721   -0.300942   -0.152628  0.459616  0.454080 -0.347028   \n",
       "registered  0.021578    0.134326   -0.120966  0.335361  0.332559 -0.273933   \n",
       "cnt         0.026900    0.030284   -0.142426  0.404772  0.400929 -0.322911   \n",
       "\n",
       "            windspeed    casual  registered       cnt  \n",
       "instant     -0.074505  0.158295    0.282046  0.278379  \n",
       "season      -0.149773  0.120206    0.174226  0.178056  \n",
       "yr          -0.008740  0.142779    0.253684  0.250495  \n",
       "mnth        -0.135386  0.068457    0.122273  0.120638  \n",
       "hr           0.137252  0.301202    0.374141  0.394071  \n",
       "holiday      0.003988  0.031564   -0.047345 -0.030927  \n",
       "weekday      0.011502  0.032721    0.021578  0.026900  \n",
       "workingday  -0.011830 -0.300942    0.134326  0.030284  \n",
       "weathersit   0.026226 -0.152628   -0.120966 -0.142426  \n",
       "temp        -0.023125  0.459616    0.335361  0.404772  \n",
       "atemp       -0.062336  0.454080    0.332559  0.400929  \n",
       "hum         -0.290105 -0.347028   -0.273933 -0.322911  \n",
       "windspeed    1.000000  0.090287    0.082321  0.093234  \n",
       "casual       0.090287  1.000000    0.506618  0.694564  \n",
       "registered   0.082321  0.506618    1.000000  0.972151  \n",
       "cnt          0.093234  0.694564    0.972151  1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_data = df.corr()\n",
    "corr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)\n",
    "plt.savefig(\"correlation.jpeg\") #Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate mean median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>17376</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>17377</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>17378</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 =df.copy()\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  int64  \n",
      " 1   yr          17379 non-null  int64  \n",
      " 2   mnth        17379 non-null  int64  \n",
      " 3   hr          17379 non-null  int64  \n",
      " 4   holiday     17379 non-null  int64  \n",
      " 5   weekday     17379 non-null  int64  \n",
      " 6   workingday  17379 non-null  int64  \n",
      " 7   weathersit  17379 non-null  int64  \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",
      " 12  casual      17379 non-null  int64  \n",
      " 13  registered  17379 non-null  int64  \n",
      " 14  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(11)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "X = df1.drop([\"instant\", \"dteday\"], axis=1).copy()\n",
    "X.info()"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['atemp', 'weathersit']]\n",
    "y=df[['cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)\n",
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2879, 1.    ],\n",
       "       [0.2727, 1.    ],\n",
       "       [0.2727, 1.    ],\n",
       "       ...,\n",
       "       [0.2576, 1.    ],\n",
       "       [0.2727, 1.    ],\n",
       "       [0.2727, 1.    ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16],\n",
       "       [40],\n",
       "       [32],\n",
       "       ...,\n",
       "       [90],\n",
       "       [61],\n",
       "       [49]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 5-fold CV on the test: 165.16673644619271\n",
      "RMSE on 5-fold CV on the train: 165.1572125629758\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_test += np.sqrt(np.mean(e_test*e_test))     # compute rmse for the estimation rmse test data\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))\n",
    "rmse_5cv_test = err_test/5\n",
    "rmse_5cv_train = err_train/5\n",
    "#average the rmse\n",
    "print('RMSE on 5-fold CV on the test: {}'.format(rmse_5cv_test))\n",
    "print('RMSE on 5-fold CV on the train: {}'.format(rmse_5cv_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 124.83790650972412\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 124.8375401857539\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": "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['atemp', 'weathersit']]\n",
    "y=df[['cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)\n",
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the test: 165.13262641013023\n",
      "RMSE on 10-fold CV on the train: 165.1582923288239\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_test += np.sqrt(np.mean(e_test*e_test))     # compute rmse for the estimation rmse test data\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))\n",
    "rmse_10cv_test = err_test/10\n",
    "rmse_10cv_train = err_train/10\n",
    "#average the rmse\n",
    "print('RMSE on 10-fold CV on the test: {}'.format(rmse_10cv_test))\n",
    "print('RMSE on 10-fold CV on the train: {}'.format(rmse_10cv_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 10-fold CV on the train data: 124.83585243780472\n",
      "MAE (Model1:Linear) on 10-fold CV on the test data: 124.86529506460684\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": [
    "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['holiday','weekday','workingday','temp','atemp', 'weathersit','hum','windspeed']]\n",
    "y=df[['cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)\n",
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the test: 156.58563437533638\n",
      "RMSE on 10-fold CV on the train: 156.58496462225312\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_test += np.sqrt(np.mean(e_test*e_test))     # compute rmse for the estimation rmse test data\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))\n",
    "rmse_10cv_test = err_test/10\n",
    "rmse_10cv_train = err_train/10\n",
    "#average the rmse\n",
    "print('RMSE on 10-fold CV on the test: {}'.format(rmse_10cv_test))\n",
    "print('RMSE on 10-fold CV on the train: {}'.format(rmse_10cv_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 10-fold CV on the train data: 116.76660017946251\n",
      "MAE (Model1:Linear) on 10-fold CV on the test data: 116.82665275568907\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_10cv_model1 = err_train/10              #average the rmse\n",
    "mae_test_10cv_model1 = err_test/10           #average the rmse\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model1))\n",
    "print('MAE (Model1:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The MAE measures the average magnitude of the errors in a set of forecasts, \n",
    "#The RMSE is a quadratic scoring rule which measures the average magnitude of the error.\n",
    "#he greater difference between them, the greater the variance in the individual errors in the sample\n",
    "#according to my findings the regression which uses the model with contains all the variable performs better than just 2 variables\n"
   ]
  },
  {
   "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": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-2.3688789921995554, 1010.9456000000001, 'train')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas = np.exp(np.linspace(10, -5, 100))\n",
    "mse = pd.DataFrame([fit_and_report_mses(linear_model.Lasso(alpha=alpha, max_iter=50000),\n",
    "                           X_train, X_test, y_train, y_test)\n",
    "                    for alpha in alphas])\n",
    "mse[\"log_alpha\"] = -np.log10(alphas)\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "mse.plot(x=\"log_alpha\", y=\"mse_test\", c=colors[0], ax=ax)\n",
    "mse.plot(x=\"log_alpha\", y=\"mse_train\", c=colors[1], ax=ax)\n",
    "ax.set_xlabel(r\"$-\\log(\\alpha)$\")\n",
    "ax.set_ylabel(\"MSE\")\n",
    "ax.get_legend().remove()\n",
    "ax.annotate(\"test\",(mse.log_alpha[15], mse.mse_test[15]),color=colors[0])\n",
    "ax.annotate(\"train\",(mse.log_alpha[30], mse.mse_train[30]),color=colors[1])#Here is the space for your codes"
   ]
  },
  {
   "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": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = cnt_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'predict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [139]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m table1 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredict price\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[43mpredict\u001b[49m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactual price\u001b[39m\u001b[38;5;124m'\u001b[39m:y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror/residual\u001b[39m\u001b[38;5;124m'\u001b[39m:y\u001b[38;5;241m-\u001b[39mpredict})\n",
      "\u001b[1;31mNameError\u001b[0m: name 'predict' is not defined"
     ]
    }
   ],
   "source": [
    "table1 = pd.DataFrame({'predict price':predict,'actual price':y, 'error/residual':y-predict})#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": 65,
   "metadata": {
    "scrolled": false
   },
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "d = {key: value for key, value in zip(month, unemp)}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
      "['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n"
     ]
    }
   ],
   "source": [
    "for value in unemp:\n",
    "    \n",
    "    if value > 8:\n",
    "\n",
    "        \n",
    "        print(f'{month[2]}')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "unempmonth = list(d.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Apr'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "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": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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",
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       " 230,\n",
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       " 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",
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       " 294,\n",
       " 296,\n",
       " 298,\n",
       " 300,\n",
       " 302,\n",
       " 304,\n",
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       " 316,\n",
       " 318,\n",
       " 320,\n",
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       " 324,\n",
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       " 330,\n",
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       " 338,\n",
       " 340,\n",
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       " 344,\n",
       " 346,\n",
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       " 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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = range(2, 502, 2)\n",
    "list(z)\n",
    "\n",
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the summation of the log of all even number up to 500 is 1307.3320269308394\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for i in z:\n",
    "       total = total + math.log(i)\n",
    "print(f\"the summation of the log of all even number up to 500 is {total}\")\n",
    "    \n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas-datareader in c:\\users\\6304640391\\appdata\\roaming\\python\\python39\\site-packages (0.10.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas-datareader) (1.4.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas-datareader) (2.27.1)\n",
      "Requirement already satisfied: lxml in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas-datareader) (4.8.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.21.5)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (1.26.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = [10,8]  # Set default figure size\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting yfinance\n",
      "  Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\programdata\\anaconda3\\lib\\site-packages (from yfinance) (1.21.5)\n",
      "Requirement already satisfied: requests>=2.26 in c:\\programdata\\anaconda3\\lib\\site-packages (from yfinance) (2.27.1)\n",
      "Requirement already satisfied: pandas>=0.24.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from yfinance) (1.4.2)\n",
      "Collecting multitasking>=0.0.7\n",
      "  Downloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)\n",
      "Requirement already satisfied: lxml>=4.5.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from yfinance) (4.8.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->yfinance) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->yfinance) (1.16.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (1.26.9)\n",
      "Installing collected packages: multitasking, yfinance\n",
      "Successfully installed multitasking-0.0.11 yfinance-0.1.74\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: The script sample.exe is installed in 'C:\\Users\\6304640391\\AppData\\Roaming\\Python\\Python39\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
     ]
    }
   ],
   "source": [
    "pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open        High         Low       Close     Volume  \\\n",
      "Date                                                                    \n",
      "2010-06-29    1.266667    1.666667    1.169333    1.592667  281494500   \n",
      "2010-06-30    1.719333    2.028000    1.553333    1.588667  257806500   \n",
      "2010-07-01    1.666667    1.728000    1.351333    1.464000  123282000   \n",
      "2010-07-02    1.533333    1.540000    1.247333    1.280000   77097000   \n",
      "2010-07-06    1.333333    1.333333    1.055333    1.074000  103003500   \n",
      "...                ...         ...         ...         ...        ...   \n",
      "2022-09-23  283.089996  284.500000  272.820007  275.329987   63615400   \n",
      "2022-09-26  271.829987  284.089996  270.309998  276.010010   58076900   \n",
      "2022-09-27  283.839996  288.670013  277.510010  282.940002   61925200   \n",
      "2022-09-28  283.079987  289.000000  277.570007  287.809998   54664800   \n",
      "2022-09-29  282.760010  283.649994  265.779999  268.209991   77620600   \n",
      "\n",
      "            Dividends  Stock Splits  \n",
      "Date                                 \n",
      "2010-06-29          0           0.0  \n",
      "2010-06-30          0           0.0  \n",
      "2010-07-01          0           0.0  \n",
      "2010-07-02          0           0.0  \n",
      "2010-07-06          0           0.0  \n",
      "...               ...           ...  \n",
      "2022-09-23          0           0.0  \n",
      "2022-09-26          0           0.0  \n",
      "2022-09-27          0           0.0  \n",
      "2022-09-28          0           0.0  \n",
      "2022-09-29          0           0.0  \n",
      "\n",
      "[3086 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    " \n",
    "# startDate , as per our convenience we can modify\n",
    "startDate = datetime.datetime(2010, 6, 29)\n",
    " \n",
    "# endDate , as per our convenience we can modify\n",
    "endDate = datetime.datetime(2022, 9, 30)\n",
    "Tesla = yahooFinance.Ticker(\"TSLA\")\n",
    " \n",
    "# pass the parameters as the taken dates for start and end\n",
    "print(Tesla.history(start=startDate,\n",
    "                                     end=endDate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla =Tesla.history(start=startDate,end=endDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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",
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       "    }\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Log_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3.086000e+03</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3085.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.092391</td>\n",
       "      <td>57.347290</td>\n",
       "      <td>54.734650</td>\n",
       "      <td>56.075755</td>\n",
       "      <td>9.338826e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.001662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>94.538287</td>\n",
       "      <td>96.712675</td>\n",
       "      <td>92.122923</td>\n",
       "      <td>94.452346</td>\n",
       "      <td>8.232210e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104949</td>\n",
       "      <td>0.035642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.076000</td>\n",
       "      <td>1.108667</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>1.053333</td>\n",
       "      <td>1.777500e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.236518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.226167</td>\n",
       "      <td>8.375334</td>\n",
       "      <td>7.978500</td>\n",
       "      <td>8.142833</td>\n",
       "      <td>4.172138e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.257999</td>\n",
       "      <td>15.709667</td>\n",
       "      <td>16.008666</td>\n",
       "      <td>7.537575e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.637500</td>\n",
       "      <td>23.983333</td>\n",
       "      <td>23.313667</td>\n",
       "      <td>23.635166</td>\n",
       "      <td>1.172816e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>411.470001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>9.140820e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.218292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close        Volume  \\\n",
       "count  3086.000000  3086.000000  3086.000000  3086.000000  3.086000e+03   \n",
       "mean     56.092391    57.347290    54.734650    56.075755  9.338826e+07   \n",
       "std      94.538287    96.712675    92.122923    94.452346  8.232210e+07   \n",
       "min       1.076000     1.108667     0.998667     1.053333  1.777500e+06   \n",
       "25%       8.226167     8.375334     7.978500     8.142833  4.172138e+07   \n",
       "50%      16.000000    16.257999    15.709667    16.008666  7.537575e+07   \n",
       "75%      23.637500    23.983333    23.313667    23.635166  1.172816e+08   \n",
       "max     411.470001   414.496674   405.666656   409.970001  9.140820e+08   \n",
       "\n",
       "       Dividends  Stock Splits   Log_return  \n",
       "count     3086.0   3086.000000  3085.000000  \n",
       "mean         0.0      0.002592     0.001662  \n",
       "std          0.0      0.104949     0.035642  \n",
       "min          0.0      0.000000    -0.236518  \n",
       "25%          0.0      0.000000    -0.015440  \n",
       "50%          0.0      0.000000     0.001258  \n",
       "75%          0.0      0.000000     0.019088  \n",
       "max          0.0      5.000000     0.218292  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla_daily_returns = tesla['Close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-06-29         NaN\n",
       "2010-06-30   -0.002511\n",
       "2010-07-01   -0.078473\n",
       "2010-07-02   -0.125683\n",
       "2010-07-06   -0.160937\n",
       "                ...   \n",
       "2022-09-23   -0.045948\n",
       "2022-09-26    0.002470\n",
       "2022-09-27    0.025108\n",
       "2022-09-28    0.017212\n",
       "2022-09-29   -0.068101\n",
       "Name: Close, Length: 3086, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla_daily_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesla['Log_return'] = np.log(tesla['Close']/tesla['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3086 entries, 2010-06-29 to 2022-09-29\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Open          3086 non-null   float64\n",
      " 1   High          3086 non-null   float64\n",
      " 2   Low           3086 non-null   float64\n",
      " 3   Close         3086 non-null   float64\n",
      " 4   Volume        3086 non-null   int64  \n",
      " 5   Dividends     3086 non-null   int64  \n",
      " 6   Stock Splits  3086 non-null   float64\n",
      " 7   Log_return    3085 non-null   float64\n",
      "dtypes: float64(6), int64(2)\n",
      "memory usage: 217.0 KB\n"
     ]
    }
   ],
   "source": [
    "tesla.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xP/dKUBX2qFbom5eAYl7yOWoKBgKSTcd5+vwSG2sLbQuQdhesGY1wdm2Bpy+ucnI52WwdbObKfO7qNldv5yhVnK5rBrRbuCjsz6HnnY7V6HUbqNvJoJXKKO+1UT4WnDXjyuesBx0KBgLU6WJxanVuPSjw5eu7zdfbBQbelsZLCzEeO7XAza0SV+/uNx8ptNNu4aKwP4eedzpWgxmmEPaOsTdQt5+KYtBKZZT3WhDXSL/pm/ZKblxl3qwHVyqNApIrVPjMlS1+4yv32cqXDr1WKDvc3ClSrtYpVepcuZXj6u29Q48ESpXGioTX7hfY3a+yvpxkMRXj7Eqy60XeeiNMsqKZ9kJFwsm7L+5uF4+1Z0eQ74XwB3X9pm/aK7lxnYdZ79EL51U8hZyay1auzGv3C+SLh2+qdMLkibOLvOnSEtVajedv7ZGKR5qPBJ59ZYfNXJl0wmR1IcbWXhVwubCe5tRy95UER3kjhKnbdBopOAqG/74YZM0NGOz+GOc4gTCZ9UruOPznO+zB33HNZq4moFByuL9VgHqdSqXW9hFAsVxjKR3jay6vcOlUBjMaYS2b4Onzi+yVqmzlSywtxFjNxACD3aJDxQn+WWffeQpRt+k0UnA0uE6P0NayCVYzMeJm8BtxDWIWz+msV3LHMYvnuxOd/YBcu7/Pp1/Z5tUH+7y2WTjyCODedonfeP4BuX2HUyspcoUKL97c5ebmPlWnzu++uMUXX91ld7/KbtHBjBp9bxU8igt2mA2PVKgcpuBocJ2uZW8Zb2957n7vjW67hA5jmGmAw3xv0OkOYlvsWewV6WWe7mGV2gFJxAyWF2JcOpHm8pnMkUcAyUSUU0tJkokouUKFz760xa9+/ja/+nt3ubVZIJ2MspoxWVqINUdB5wvV5qqF3Sr7UVyw3oZH3QYvSnfzHBz511IYRKdredhttLvtEtot3YOsT9BPGgYd8zBouvv5vONuiz1PrWTPPN3Ds5/DMTm9vMBbL63y1Y+tN6cXrmUTvPWR5UYX52KCt15eIZuOsVdwWEjFePPFJdaycV6+u8/d7QI7+zXqbqPge+X2Hr/6+Tvc2ir0rOz9KwgG1ZqYp4g4LLxzVqo4x27FTZp/v4sgnvF3+n27XoJSxTmyxHc6YfZ1TQ9T4fX63NYxD4N+ZhD3YhDbYqtMmG0KBgJSqdXIl2tsF6rN5/zJuMmplRSFcqNw8qYO3twpcn49xRPnlnj8TIa7O0XubJdIxYxmgXZrt0S5UmevWAX6W6kuyNbErEbEYe7q9G9UddxW3CT5HzEZhjHSFmW7XoLNXJlr9xotcXh47/RzTQ9T4fX6XDMa4dRKiosn00M9cvP/e9jrd5htsVu/KwxlQqfHHWG+r6fFbJX0E7SVL/PSrW0cp0bcPNyF+MKtPF98defgYnUpV+oUyjV+54VNqo7LGy+t8K1ffYZz6wtcvbNHqVLj8skMb7q0SLVuDNWaiJsRllLmobT0a5ZvrDB2dXrHO25GmhtVHbcVN27+a8b/iAnG16L0b/S1momxtd9/S9wzqgovqM8d5wZLYbxXCmUHp+4eeeQSxrROGwUDAXn2ygM+/cIuv/Xc/UMXZDphcnYlSSIWPfiNS9mp8cJrO3zxlV1evJXjwnqKiycXqdbrlJ0623tl7u+VMSJRTi/1v/qgv8DxD7ga1CzfWGHs6vSOtzc4ruLUB27FTVrrJlzDrH1x3IrMv9HXqZUUF0/03xKfFr0GMOYKFZ6/mes4XmOQYxzGeyWdMIkYxpFAb9C0znKDZ1jTU9qEXLno4NShUq0e+r0ZjbCxtsDTF5YeVtTVGvfzVZ48l8apuTx/I8//+PJ9bjwosp6Js1euEaHOudU0MdM48hzU063SPs6NHMZCoB/9DFoLQ1dnK//x9j9rnyadBvj10tqjEFQQ2pqGWSn82x3b1gCg20bkgxzjMN4rZjRCNGIcCfQGfZQyyw2eYYXnLE+55cUE6SScW+neGsmm47x+Y4lTSwmW0gnu7pS4tVnkzk6RlXSMS6cyLKViXH9Q5qXbO3zuyjbPvrzN3Z3ikYu3W6V9nBs5iGeUrcZRGE9rReo/3t45NYxuRXr4DHu9detRCIJTq7OVL3Fzc59r9wozW/h7V0s2HeeJjeyRLcmdWp2661KqOKwvxprjOvyvTzpYGiQN3a61fir6sC3VHIbjP/BdZ1nWSSDj/51t2y8HlqIpdeFMlgvraZKpOIWyQzJuNls7pUqNa/f3uXhigWQ8SnYhRiYV4+rtHIVKlbLrcmploTlY6NKpDLuFMl96JUcqFWU9m6RWd4/M+ffPIhgV78Y6t5Y61ncF9TndNCvSWnkknx8E75rwWtCtvHOaz4c3D0Fq16MwKP8xBQ4d30LZ4cqdPep1l/NrnQP11vPS+plh5gUA3Y5hoexQq7m8+qDAxkqKB/nyofeO4/7sJag09FPR+/Pe7Z4c13EJw/Hv+0q3LOtdwDPAaQ73RLlAtO0fzZF6rUat5nL9bp7cfpXVxSS5QoUXbuVZiEdwai6lco27u2WWkiarmThfqbhQd7mTq1KnwLOvbBGJwkomzlomxca6w9pijFTMZHffYT2bBGjOTBhH911QrbVxPHqYhop0mJu+VwAxCUGlKYiA1n9MgUPHN50weex0o+3iPYbpp9D3/xyGPppux7vbMfT+Lm5GcB2Dx0431kBZTMcO3YtheDQYVBpaj0eva7XbPTnuwa+TPP6DfPOHgL8LfNS27eKI0jO17u4UeW2riAu8fDfHuRMLAJQrjSBhYyVFtV5jIRFht+iQTkbJpKO4JDGiES6tp9gvObx4K0/cjFJ2alxYTfNgr0LRcTmVTRxainVUEWRroBFU78M4ejHColvhM8xNH4ZWQ6t2aZpU0NJ6TFsHL64uPgyi+y30/T8XC5MPLjtdA4NUdBHDYOngWCRbLiPvEdW4zl+7dI+qjOh1//TzuHXUwlA+DnLGV4CfVSDQXjQSJW7AqWwMw4hSKDtk03HedGmZp85ncQ2Xz1zZJrdXxaXOr//+Hb50bRvHdTm5mGA1u8BjZzI8djqDW69x7V6Bl27vUqrUWE3FqNY4tBTrIPu098t1XQ2qCUC3Z5bDPFsPQ6uhVWuanFqdu9vFiTyX7zQnv1eaO31Gu58nwf8cuVPag1yddJyD6o77XUHOigjDuQ6DQUqXZ4DvBX5uRGmZanc2C9zfg2v3C8SiNCNer1Xi1FwWkyZ3c2Vevp3jPz97h6pTJ264nFxdZHevwM37Ljv7Ze5slcnvlbm3XeR1G4ucXkkQo069/rDLbzNXZmuvysWT6WNFlP4R+CYG59a6b5ksvVtjnQqfYVvOYWg1tGpNU6HssLVfZTUTC+31c5zjOIlej9YW7aCtWhgsz+MMOo/7XYP0loXx/gmjQc7EO4C/YlnWDwN3/C/Ytv31gaZqCt3L7eMAWzmHusuhAqOx/aXBmy6usLVXJJNY4fZOkdxekboR5/rdHDcfGOwUqqQTJo+fXuTMWprbWwXy+2V+5TM3OL2WIREzqWOwlDIDK3gLZYerd/ZwgSdPtS9w+hHG59qj0qsg6jaI68ZmkdPLiWa37Kwcq3TCbE73msY89bp+724X2dqvcvFEeqBu+uMYdCDccY2z0jzud4Wxt2zaDXIkP3Lwn7QRMxoFQdWAYrl26DVvRbYILtful0jFDapOnat39qg4OWJmgrhZp+IYZNLwZNTg9HKSL1/f4bWtErGIwdc8nuTcepq6C3Ez0hwA1FoAdRtZ3U46YXL5YICVQbXte9p99qRG3YbBsAWR93elSo2rd3JcPp1p9hwFaRKBWVhaX91mBXQ7Ft2u37rrcv1gFH6nbvrjXPed0ugdU6/3bloDrVEY9Hqbp8bKsPoqzSzLitJ4RPDHbNue/GiaEFrKNAr1hRik443JFQ+fazWmBcbNCLV6nRdu71Gr1SlWXCo1WE7XySwkWVuM8eTZLEYkSiZp8tWPn4B6DdeI8PjZJZLxhxdy6wAgT7eR1e34H2Xk892DgTCMug2DQQqi1kKoUbiXcGouewWHbLoeeOEU9sBs2ILZ/0grnTCbY2j8n9FtVkC3Y9G7u90gkw5mQGirXmkM+/kM0qgq7Xk6hsPq62jbtl0DHqH74lZzrVBpVKQuEDk4qrlChS+8usPVO3uY0QiZVJwTS0mqlRqxaISlBZNTyyYnVhYwo2DUI6wupbi9U+alO3s8ciJFPGZyYT3FXqXW12Abf+EUdAU9yAAsaWg3UCqbjnNhPc1uyRnJYK2wB2bDDB5zanVubu7z7MvbvHAzz2au3PYzus0K6Kbb9RsxjLYL+XT7uyAGuHkLJpUqzpE1RvrRbxrCsOCNZ1SDGMN+T4TBIEfmJ4GfsSzrx4EbNOo9AGzbnvxVNGH5gxZLrQ4x0+sZcKnX4eRSwjf6H1YWE6xnE2y/WCNm1DHq+5TrBvlEnT/oGmQTBpvbZW6mC/zW8w9448Vl3vnGTF8XcmurdZAWrLcbWKcKPaxdwWHWrhAyo41d7FrnegdlEudpkHMy7PTKW1slDODsapK1bKLt8WvNe+viMl7Pgn+xHX+aO+Vj0OMZxAC3QtnhhZt5StUab760PPTqjr3SEOZpokEJS9kVZoOc5Y8AfxZ4GagAVcA5+P/cyxcbhUypBFWn1ih49qvsl6pEDYNX7+5x9fYe1VqNdMIkETVwa3XyRdjadzi5HOMdl1e5nyvy+Wt5buzss5OvsLNX4fZWAcM4OigxyGjee5yxlS+FppXQSb+th27HKMjj1+2zOrUc/YWT97dhaqENqtc58fJWqjhtK+Be+U4nTJ7YWOSrHl1hY22BZNw8clx7fY63GuHVO3sUyu23+Q6qZRpEpZZOmJxdTZKKt1/TzZ/fdnnvNw3tpisP23tz3J6I1vtlmu+JaTNIMPDIwX+P+v7zfp57uWJjKMWuA8/f3KFQdnj1wT47+w63tvf59Etb3NsukN932CtWeG27wlc/scTqkkHcNDCjMeqROl++vkupVKVQcdjaLVOs1Hj+Ro6b2/uHvm8U3WkuUKzUuLFZJFeohPYm7LeQ63aMgjx+x/ks/9+Oc5530HqdEy9v7br3O+XbXxF4Y1tWF5NDr0mfTphcOpFu9tS1S3OnfAx6L7QLAget2MxoY5Ozp84vtd1r4O52kWv3C81r59r9wqGtfQd9hOE/fsP23vRz/Q5ynbe+19+DOWmzFqj0faZt2742yoRMu0wqCexj0njGGDHgTReXePTUAuVynWi0wJeu73L1zj53dvb4wqu7ZBN1cvsuyZjLrft7lA7GBWRiUYplFzMRoe7W2CnU2c6XmyOKoXEhdnuO2K6br1vXXzYdp7Bf4dRCikLZIV+skivUjr2OwSj0Wn7VKyz8BVpr3oPsjuznszod+9a/9VpoFaf7I5uw6dUN6299dlsK13+c+unm9r+/23nw3gcGt7ZKAGysLRz53Hb5cF2Xa/cKrGZinFpJDXxOvO92anXu7JQHWkWw2yOErb3D04tXF2Js7VVZTMd6Pha4dr/A6kIjP7lChat39rh4In2sfSIG7Yno595rfW+h7FCru81F3TxhWAdi2g2yN8G/wDdOwM+27T8bWIqm1Ne9/gyffv4KJ1ci5Aouu/tVNtYXSBYqXLtf4E3nl8kkYtTrNfLFMnWnzr2CgwNk0zHqbpTT6SjnH13BwOD6VoFyuUKh7JKKRTGjJs/fzHHpRJqq4zYXHOrVSvJfqN0uXjMawTAMkvHGKO1cscZSymx2QU5LpdRaWPi74v15H6aw6zUFrFe62h37dmM8coVK2wJvmvnz2W4p3HbnqZ9Ko/W4ekFEp1kGmXiUUrXG7e0SK5lEX8fXMAxWMzG29ntXtH6tQcDp5UTXVQQHqVTSCZOLJw+v69DvOJR0wjwUOECjYD/uc/V+/751HEe3Srz1M9MJk4JjjGSK56BmbVDiILm40vLzaeBPAf8quORMr3ubexSASq3O2dUEa9kETq1OqeKQTUVZSicp12q8dKfAxlqKsytJbjzYYwFwqlVKTpVXNl0KRIlHXHb2K9zbgUdPpXn9hRXOrCR4kHfIFx3ypRpJ0yBudq6gB+kCbfe3F0+km4VYvzf5OKLzflb/23eMI0HMOKaAdTNoa6hdgTcrBtm7weuK7dTd7fWQ+Z93t2t9+3smMumHMw38G/l4UxWdWp3NXJm1bIJkvPHeYQZ8+heZ8re4WwVVqQxSGbfm58mDXQ/HrVB2Bup18Roto5jiOahRD0ocd2/HII8JfrL1d5ZlPQP8eKApmlKF2sFCQ3V49HSWZNxkK1/iK6/lSMQaA4BKFZezy3E++9Ie+XKFch3KZbi/C0sZWFo0ePRkmpsP9tnKV8kVyjxyOsvGapJY1CQRr7OYMknGomztVak49Y7rDbS7UPvtXvcvduJ1qfd1DMYQnff6DjMawYAjQUwQN+4wBY7/hh6kNdSuwJsV/kqydSXG1l6CK3f2MKDt1L5C2WlW/BWnfqTi9TvcM/HwNa8nYillslt0OLeWIl+o8uwrO7z1kWU21s0jf98v//XS7VHdMJ897L3mX7yo06yjoCqhfj4nnTCP1eviffYszhYYd2/HcUubzwPfEEA6pp5pNJZgqNVhM18ADloudZe1xRi1ugu47OxV+PK1HfZLFbbysF2BIrCzBwvxCE+cW2LjxCJLmRiXziywvVfhv3/pPl+6vsPGcorVxSSnVlLNLsKgFMoOTt1tDkAatEDwt9JGGZ33UyEbhjHS6UmDFJCDjlj3T3+bZl4+XBe28iW28qXmQCvvHAI9B/w9djrD5dPtp9X6W/vetec9Gur3HHmfsZZ9GEQsLcS4fCKNYRjHGhzW7nrxn/vjDEAbJjDtdyOyoAay9vM5Xi+Ft5R1kJ897cbd2zHImIF3tvwqDXw38JVAUzSl3IP1mPYqsJmvNlvVmaRJ1amTL9WJmXBrs0jRqZFKJFhKl6mUoVyDaBSihsG93TKFokO5WuPsQoIHOw63t/YpVVab3Zuj6DpKJ0z2qsah54iDRKWFssPNrRKrCzGy6cCSdUS/LYCwtBLa3dBxM8JSymz7mKfZGlia7vW9vHyczRpcvXuw98VB676fnicvGO1WsXufkytUmj0Eg94T7cYy1F3YLTvcy5dJxiPE+vwsfwDtHYN2A0ZPLyeagUC7RxqDprvfwL0RJPfeiCyoSqjfzxnlYMVpNu7ejkHunGda/vvgwe/fE3SiptHVm7sAFBy48aDQLMjOr6UpVuu49Rr7JZelxQRvf2SZSAQKBcjVGss6ZlJQcSKUqw6ZpAkuVOtwasnEjMdIJ4xDI6y7RcTDtDjMaIRoxGj2OAx6s/kHJc1ytD6odq3DilNnt+hQcY6eH++4G8Z0BwNePiKGweXTja25O3Xdt6vA2k0p63RND/v4prXHAqBUcdjeK7O6EGs+3utXP9NEvbxeubOHU6v3ne5O6W393l5a17YYpWF60gb9bOicl9Zrptsx7KfMnLWphK0GGTPwyCgTYlnWu4CfBqLAR2zb/mDL68bB698KFIA/Z9v2s6NM0yAq7sMbcT0ba3ZdFss1KhWHaNRkbdEkV4ySXUqxlInhUMYFTmUhGofVxRiPn16kUqtzK1fg/k6JzbzDiUWDvWLjQlxdTPY1wnrQqVDemAF/62KQqHTUq+pNm0EGyfl5hVw+P91bgPjz0WkzpkGOUa+ZMINcq6WKw6t399jcqxA3I4fGI2zmynzxWo43X1ji6QuNwLhY6G9dNX+PjxmNdL1PazWXYqV2aN2EbsejUPbtLtoyfsJ/rPrpJej1LHpUz6pHMSCuW1pbXyuUnY5jUPrJ86xNJWzV9xmxLOv3Ovz+s8dNxMFGSB8CvgV4CniPZVlPtbztW4DHD/57H/BPjvu9QTIPunxd4H6uMbjv7k6RT33pNs/d3CMVj+DUwKm6ZBIxzi+nmks3bu3BvW148fYOX7i2y/M3djExSERhv1Dhfq7E7e0Se6Wjq7e14x+U028r3ZuSd5zFhkbZEujHOJ6399s66NZam/RxCoteYyfyheqRcQZxM9KxddeL1zK8ejvHczf2WE7HjoxHWMsmeOsjy5xcSQ6850Ch7HBzu5GfbufY25siVzy830i345FOmH31sPTTS+B/VOHPh5e3uNk9kBnWKJ7zdwusW1/rNgaln96lWX80MUhp9FjrLw5a60GsQPh24Ipt2y/btl0BPga8u+U97wZ+3rZt17bt3wGWLcs6E8B3ByLqNroUY8DGamMQXSoe5fyJDK87s8BWvsQvf/om24UyyYRJoVTF64TM1SFfg+v36nz2xbv87kubfOmVbfbKdTKpBNFIhMunUs3FT3rdTMMMyombkeaAKW8FwtZCt1s3WxjkChVeuJmj7rZdDiMQ/RZonQqOsB/DQRy327Rb4bqZK/PsKzts5sqHWpQVp35oSeFB0pErVPjiqzvc3alwfj3BRssYA6dWp+LUWcsmqDiNacFecLlXrPCVV7d5+U6+6z3Y+nCnXdq8+3NjNdmskDsNwPX/fWuA0e6z/b0TnY6LN/r+zk65bTBSceojCVZHUZl2C7paXzOjnVex7CdAn/UgvudZsSzr5w/+Gff923MJ+HIA6dgAXvP9fAP4A328ZwO4HcD3H5uRaNxwBrCQijcvvDdfXOb6gwJfvPKAV+4XuHw6jVN1eGWzTGtxUgFeuVvi8pk0p1ZSxBMGUdclV3GJxUwKFZfVTKw5p7pbD8GgXacVp/GYwOvedGr1I11q3boqw8KpubguI1so6biDoqbhGPbruN2m3a5Rr4W+lk0c+h6vdQd0fITQqTvaqdWp1V3WF2NEIib7pRq7xfKhbmT/NMPmdMMlg5sPCvzqF+9y+XSGtz66cqTC9r7viZb5+t0WmzKjEW5sFptpbDcIsjVNvRYR88ajLKZjzWvt8unMkUc17dZzGOVsoEmsECiD6eesX+3wbxf4TeDfBpCOdqOlWpt3/bwHy7LeR+MxQkfPPPMMAPl8vs/kdee6Ln/7O96Ic9CXkUlGm5+dTcBTZxM8eXqD7/raM80Ev+3RDK3jxyI0tj82owaGYeC6LrV6I+OpRJSYGSFi1CmXCtRqLvuOQSSogWZuYxllw61i1Bo9HE+cahQwRq1MPl/GAF7X8rswMV148nQcA5fC/t7IBuEZQLHQX95d1z2UDtd1ed3JOBjdj6HruoFdn6NiAOeWjL7z0XoseskmoFouHvqeYqHcHN3ffJbvwtmsAU4jHY37xqXgGIe+L2G4vPl8o7KNGAB1FuPGoeu7MYuj8fuH/4ezSxH+7B8+g2FANFKnWHi4V4j3fWXHaFwbtYff2e0Y+V+jzftc1wWMQ2nyv97us/2/i7ouj67HiLoV8vlq23PhXceu60LdJWIYfY+RGESnczLsZ4X93uhlUnlYXl7u+FrPYMBbbMiyrN+xbfuTwSXrkBvAed/P54BbQ7wH27Y/DHy425ddv37dBVhcXBwmrUfk83l+8uNf4DdfatxEP/Idl3nvNz8OPIyIYwZ84coDfuMrD4jW6vzy5+6wWTr8OdkIfNWTWSplh0od6nWXhNlYCvUPveEs3/TGUywtJtnKl7jyYI/HTmdY6jA4a1C5QoV6pYgbafRq+Fdk6zeSn3T0nytUuHGvwPmVCAsLmYm3QB4urZtsToG72WdLOp/PB3Z9TlIulydXjhAzIzzIV5rHAoK7XrbyJa7cbdwPq4vJtp/r1Orc3Nzn1laVJzYWWW5z3+wVK9x8UODceppM6uH58c7Fw/OZYLHNIj0PVz9MHrvHp/XaGUbrcTiaj8Pn4u52sbnMedA9Vp3OyTDnf5B7Y9JlUidhvL8HmU3wScuyLBprC5y0bftPWJb1NiBr2/Z/PWY6PgM8blnWI8DNg+/40y3v+QTwAcuyPkbjEcKubduheEQAEI3H8HZzrhxsYVwoO5QqNa7d3ydhGnzuyg7X7uXZyZVp98jRiML9zT1KNQOjXsMwI9TiEfbKda7dyeM8dQJoDEAKevnQdKKxBK4DHbsle5n0aNt0orFeu1Erj/TG77eAaTeAaZYHILVTd12efWWHN19YOtItfXe7yNZ+lYsnOlc+3br8/XP6/W3Ndo8fvApwuctslxsPCvzal+/zjU+f4MnzR9PT6fwNu2JnN8NOl+y1zgG0X+di1LOB2q2L0Gnp6EH0uhcnXSZNk0FmE/xlGiP4XwK+/uDXReD/OG4ibNt2gA8AnwSeAz5u2/aXLct6v2VZ7z94268AL9PYI+GfAt9/3O8Niuu6XL1WaP4ccY3mRbhXqrJfcohForz+fJavf/ok6XQEo3b4MxJANg1112Bnr8a9HOTzdcoVB1xIJ48uX9paOB5nMJe3BG42HWd9MUap6rC+OFjBMOnKblyLdPQ7iHDWBxz1I2IYbUfnF8oHu+4tdL/GOh1r/++z6fiRqWLe/eANAnRqdYqVxgZFnc7bufU03/j0Cc6tt181q9f5DPJ8t86j9+ejk37WOYDO61x4gYxXUY+KN/U5X6we2VdimM/qdi96gU/EGM/aCkGY1HoGg5TaPwT8Edu2X7Us628c/O554IkgEmLb9q/QqPD9v/sZ379d4AeC+K6gGYZxKKpKp6PNirFUcUjEoiTjUd7xxDp3tkv8j9+/R8k9/IygTGORoacvLBG9leOlWw4JE2KxOO9840m+4elTXVdqCyLKhkaBUHVcvnQ9z5Nnjw486iQM3XFeGka9XE+/QU/rMSmUD28dO8tBQvNcGLCxvnDkda8Xp5/eFf80OO+9/nPQLghsHXh3ejnB42cy3NouHUqf//szqXjbHoF+8zrMo7XWz2j9204DCEsVh81cmaWFGHWXtguF9Tvlzm8cawz4pz6vZBLNfSVGsQGYF/gAA/d0TsqkejMGuWIXeTia3xsHF6MxCH7urSw1/p8Aziw/LPzSCZOVhRi7xcbc4/VsgjdsLHMmC5kIJIH0Qe1VqcD5E1necmmFxTSczMIjpxd56+NruJFI2xXrvAsHOt/8g8gVKiwtxHjybIZipd6z9QsPu3z7mfY4St6xcEc4tRD6bwG2tlrmaZXGXueiWyvU36r3jtOdnXJzDQzv990qXa9FuLQQaxaqG2sLPH1+qTl+4/mbuWYL7DgtMS+vm7ny0PPoO7VwvcrOv3cCNKZefubqNs/f2OXa/cKRtQ16TbnzH3t//vsJdIc5Xv78tU59Pk6PYq97sdPxC7NJ9bAOEgz8OvDDLb/7QeC/BZec6ZWIPzyUkSjNVuArt/e49qDAYqrRW7C6mODtT63z2PklojEoAbWD8nIpDQvJCOlYnI3lGNF0EhNYTic6rmUf1IXjTbu6dr9A3YVLpzKcXEp03SbZKxRyhQpb+1VWM5NdfdA7FmFZyrf13DQLwYA3mZqkThVDP+fCX0H4P8dbL+LuTvFQoAscqXRLFYebD/YpVQ5Xol6LsO7SsYI02qRjGP4Kp91iPoN8RqfrojXta9kET57NgGH0fNTSTqdHCv0EusMcr3b3Qj+By3H4eyOScXNqHtlN6vHiIN/2l4HvsCzrVWDRsqwXgO8E/uooEjZtnGqjaHGABzsl4maE1YUYt3ZK3N0u4tbh1oMC93eKuBikYzE2ViOYNIYdLsXg8kaWL7+W47dfesD9vSrRap2dYo0bD/Y6rmXvXTheV1trwdqvQtmh7rrNgqXb+vn+v/EK64sn0hPv+h7XmIF+tVskpluLdhrXPu9UMbQ7F63581cQrZ/jAql4tNmqz6bjpBMmmXiUiEHzWfOrd/f4zNVt7m2XOn52O/5xBscNqL28JuNm28V8BvmMdssQt7uvk3GTx85med3Z7FD3nZdn/46P/eZ/mOM1iQpuFCsezrK+zqZlWRHg9cAfBt4IXKTxyODTtm1PT8k1QufXMvzejV3iwGtbjUFKp1ZSxEyDvVKGzVyJz17d5uxKit39Cnd2imzt1VmMN3ZJe+R0jAurC+wU6pgxg3odFhbirGViRCIGKTPStZXeWrAOujeBN5vAe3+3nfXafec0RNyT0u+4Dv+zwnD0bfQ2SMXQ+izUHzC0fo43W8Z/XVWcOndypeZCWBWnTrFS58mzGZKJaMfPbqf19aCCyE5jHGC4cTXtAqZ+89iN97fD7PgYhqC7n2M56QHN06avo2Tbdt2yrF+ybXsR+PTBf+ITNRvFtxGBiuPg1Bqr+Z1cTpMsVHjp1h64cH49xWLapOxUqFRgIQHZpThPXVglm4mzuhJleSHKtcV9ElGD27tFPvvSFqWqy1ImRjLevRsRDu9NsJiO9XXjerMJvBvLv5JZssOfh6FQmAZeIX56uftzS3/h1e+iRpM2yDXQrXD2T8/rVMinE0dXH7x85uE688l49NDUxeMO6htG68qCg26G0+7zOgVMQWj9zDAMBO5Ht1UnvdfTCVPl0wAGGjNgWdY7RpaSKbe+2Bg0eGI1wonFFOA2u/TiZoTHz2b4o191mqcurFAqO+QKdfLlxiZF+/kKpWoNIxLl6XNZnn5klSfPLRGJRnFrBhurSZYzg+8g2Glvgn66oxVVB8c7ll539zimpoWRP3+drsFuXbtmtP+15YMY1DesTvfOsPeUd6yAga6Pfu7zTsct7F3rrcey32mV0tkgV+U14D9ZlvVLNB4RNIcJ27b9Y0EnbJq4rsvvPN9YDNGt1nn6whJgNKeRpRJRtvccypUK1ZrLYjLOI6cz5Pd3KVbBdaFcdXj0RJqnLiyzmS+zX3LI5SoUK3VOLqd4/bnB1rFvbZX4I+dhp65MutUw6e8fRGtaex3nacpbEDpdg4NUmLlChSt32q886H8mPo5ttfs538P2pA17v7b7u17XWRgaAf3cC63Hst9pldLZIKVOCvhFGkHAORpLA58/+PecM8gctNwNYLdQw4waZFNRrj8o4NTquG6N//6VTT757C22Cg5Pnlnk5FKEVALKVdgvOGzuV7m3U2J3v0rEaCw0FAW292sUy7WuKejFHy33c8O3i669sQh3t4sTGeQ2TRF/P2n1t9yOm7dpGXzopbPTNrmti+30yo9/bEW7UfHjGkU+ymtz2Aq63d/1Sue4e6faXbfDHMtxzE6YdYMsR/y9vd5jWdZ7bNv+heMlafq4uMQOxgzky1AoV5oFWjRaxqm5LKcTfPMbT5JOmVy7u8eVu/tUHZeYAabZ2G1vM1/kt55zuP6gQDZpUnddynUXx6lwP1/p+fy/1/PWbou0tGr3LNGp1VlKmQONRQhSGFot/eqUVm+xGG83Pm9XuXTC7Dlgs5tJLVQyqH7T2c/7vBkB3jGe5PUxyu/2j6fwdiuFzssNTyqdw2h3ngdN47z1qo1K0EfuZwP+vKkRcRsXbq0OdbfWvIEfO53hQa7EZ69ucWo5xfn1BSp1cKmzthTn0ukkTz+SJZEwiUejlGs17u4UebBfYWkxxhOnM0SjMRaT7dc877d1OUi03O7mKpQd7uyUyaTNjmMRRm2aIv5Oad3MlXn2lR02cwe7xR38vp+pnN1MopAfpjei33QOs4xsu2PeaR2CoB332uznWHprl9zdLjb3WvDW+Wj3d+3Kg7DdQ+2uh0HTOE09hmEW9BUxLTOiAhUxDL7pLRucW4Q3XFrgdWdXgIcji3cKDtGoQcw0iBiQiUc4tZwGw6RSi7KcTPCmS8u8/twSF9bTnFtJUS5XeflOiWrdZadQplRpX0h4N4JXIBx3P/JOqwn2OwhuXKalW7zVWjbBWx9ZZi2baG44FeRc97DP4+43nV5wtLtf7fgd/Xx/a/DVjyCvrX4/qzUvrX/X7JlLmmztNTZE8y/E1O4YhK0XoJ0grttpyOc0CLrkGO06sCFmGC7JdJyveXydR89kmr9PJ0wurS9wZiVFpVZnM1/m1k6Fs8sJvu7xNZZSUcxIlGTc5G6+zPM39tkqVHiQdzixECEWMVjPxHmQrzRHFPt5NwI0lmwF2i7x2q9C2Wm7mmDYWhT+ICjMQUFroZ6Mm2ysLzQXqJmm55zdFg0KmvfZ7ZaR9Y876LXinz/46kfQS2v3quQ9/gGP3jXt/7tDPXMHWwx7//Warum/psJ8rwxrGu6daaCjFxC3Bk6lwko6QTJuHpoOdHY9zSMnF8gVaqTiUU5kTJ67tc9Xbu5CJMJjGwucWk5ydiXBiaU4b728yrm1BGdPZoiaUeqG0XWNd69luZRqfO9xusy8lRPXsom+HylMooDxB0Fh7iIcZtBlY8CpG7pCuzUvgz56GuQ66TYA0KtgK06954p//uCr3zwGubR2tylwfq0ricLhEfH+nrl0wmwsAZ5vbLrU7zlwXXeiA4Al3BQMBOTqrR3u7MLvXb3X3EjFP7J5LZtoFjCXz2RZSsKNzQKFYpWIEcFxYWevQqFcYykd4+zaIm4dTi0nuHwqw1MHG6x04nWrmtH2o7T7Neiz60k9r/MKz24tozDwLwDV7zEqlB1qdTd0Ac4wefEEcZ20m4kQdO9EOmEGurR2a8DUK72dHsf5P6dQdrhyZ4+rd/YGOp6GYQx9/sZtWh8DTrOgS9DrAX/e1NhYz5CK36NScdnMlTm1kjrSIri1XcSp1/nsy1u8eHOfRNzk1FKMm5tl1pw6lSrU63V2CzW+9nVrLKRNXr69x4O9CutLCRp7IrbXOlugH+0GCg5auE76eV279RS8VnUYug29BaAGmeueTjSWhg5bgDNoXpxanbrrspUvBXKd+FdyhEaXt1dpBmXUq2oOukxyO+nE0ZUY++U9Lhl21sqo9bt0d7+fM+4ZBtM8s6HvK8myrEc7vFQGbtu2Xbdt+w3BJGv6PNgpUa5CPG6wtBBrW0nt7ldZSceJGS5EI7zpXIZEIs7yQoxHT2aIRQ0+9/I2z722QzIW4cnzWR49k+H6/QK3tkqsZBIdb4xhCrF203oG/ZxxLkncz0Y/d7eLpMxGqzosU+yGOab+paHDxmu19bO7Xa3mcuXBXnOg5HF4AYVTq3P1zh4udP3caS6Yu/FWYhxGP8uMT1K/S3f3+znjnmo7LVN82xnkSF/h4QBBg8ODBeuWZX0C+H7btu8GlbhpsluuUK7BTrHWcXdBMxrhTq7E684u8T+/w2BtMcVusUq56vDi7T0unkxx40GRGw/2yRUd7uwU+fqnT/LGi0uY0YdTC4Mq5NIJc+gtVyeh143mPe89v9x+GqYcn9dF7W0W1K3ASydM9h2Dx05nAjkf/vn2l/toGXe7XsIcKIwybZPuyetlmB7OXp8zTmE/vt0McrT/AvCvgNcBSeAJ4F8C309jJ0MT+MdBJ3BavOXSGufXIlw+tdT29Ww6zlPns6ymYuwWHap1eOF2HtetcXOzRK7gkNuvUHdrbJxI8tWXl7l4Is3OfpWq445k/fBmgDLElquT0M/z1osn0kQj4W1VTzuvi/pyHxW8GY0QMYwjewkc93lwpz0K+p3tEPSMgaCNYhyO9/gMBtvfIMjv7+ecBzUzYFIzDKZ5ZsMg4ctPAo/Ztl06+PmKZVl/CXjRtu2ftSzrzwEvBZ3AabGQjrOaSbCxnuw4zScZj1IzDFbSMW4+cHEqDk69MQBuacEkETP5n742TcyM8ur9Ane2y6xlk4dW/PNa8e3WExi0RdH6WWHfKa/f5635fLjzMc2O00XtGVVXauvndrpegpgx0GmXvCAqgVG0Lv2DUifRfd3vOQ9zj82sG+RoR4BLLb+7AEQP/r1H8AMSp0a5UqVUbcwIaPeYwKnVKVUcFhIRTBMwwIw3Wllvf3ydE8sLXDy5wOUzWVYyCRJmBCMCp5YO7z7ozTf2WvV+g7Yo2n2WRvBKEPxTa1uNqiu1388NYsaA/14LuiU/itZlOmESjYxvUOqwa1JoNcHJGeTK+IfAf7Us65/R2LXwHPC9B78H+OPAbweZuGlSqbmUa41liL2Ruq07Bb56v0C+UKVQrrGYjHF+PU00arBfqpIyI+zsVXh1v4pTh7VMjFyxxn65yrnEQl+j/Y87E8B13b5bbGGI4MOQBmmv2RJcOroo6agGnfb7ue3eN+i11HrvhP058bgHpfbbS9Nqmp+5T7tBNir6e5ZlfRH4TuCtwG3g+2zb/s8Hr/8ijV0N51IsGiEWaWw4VHHqJOMP1xJfXYixtBBjLRMjgkulUmPjxAL3c2V+87kHQGP+b901uHQyRR2DcyspolGDu7sVFpMFzq6nD61U185xZwIYhsG5tWRzFbRuBaN/1K83uHHcFfI0j9yddV6hbtSm45GNt6DSaibWV49B670zieB5lI8qjmvYSn2cs5PksIHO1EHF/59HlJaplo5HWMkkWck83HkunTBZXYixtVfFqdd55V6BnXyFRDzCSiZGzDSo1xsDe9YXkxgRWF9MspBqnJb6VoS9UokX7+TJpM1jP6vtRzYdby6F2q2S9U/zmlSF7C9wWgvGsJqX3oxpG7/hX1BpFDtydgpcj3M9+D8TCFVgHHSlPor7e9hjP6v38CDrDMSAvwV8D3AWuAX8C+CnbNtu/3BwjpxYSXNi0SSZTDR7BvyLtOwVG6t+GYZBKmFSrbm8dHuPq7f2MAyDdHyfcyczOHV4ItPYlvWJjUVOLSe4tzveArWfqN4/zcs/7XGc/AWOP4AJ825Z6s0YneMU0sMsDjWITvfUca6HaXtUcRz+4zTs/d16fQx77Gf1Hh7kyvl7wNuB9wPXgIvA3waywP8WfNKmy95+lf1ynbR5uGL0V5onsklOLiVYycQpVRrrEawtxlhIRLl6t8RTySgnlxLNngUzGuHsapr1bPsZCqMySFQflm49f8EYllkRQazwOE0m0WLyf2euUOHqnT0un84M1Ys2ymu502cf53oY9FHFNAvi/m6txIc99rN6Dw+Sm+8E3mzb9ubBzy9YlvUs8AUUDHBjq8DLd/fZLlTaFoTZdJynLywRNyNUnDq1mkO5WmMhafKWR9a4eKrEqaU0+XJjXwBvw5JZiz5HpVNhO8kuvSBWeGwnrN2Uk2gxtXaVt9vOK6zHCw43FvzjdMKc5kkI4r5prcSH/cywNICCNshV1ql3Jsy9smNTqThUqjXubhUoVQ5Pi/Hf2F4lny9UqVRdohGDcsUhEY+xkIoe2oBlmlYHDKtJTlUaVQsirNOvhs3vcRYh8n9nNh1vuzxxWI+XX2sapyHN08I/zXVaFwQah0Hu2n8L/LJlWT9JY0OiizTGEHx8FAmbNo+cWeTCiQUqrsFmrszG+sND659VsJZNcHo5wXa+wrm1BGeWFyhU62SSUdIJ89BWq2Y0wtXbe6TiJS6dyvS9Das8NMkuvVG1IMLaTTlIflun3Q67bHA/XeVhPV5+rWnsluaw9hqEtdIddY9VWM/HoAZJ+V8H/gvwIeBzwD8C/hsw94MHAR45uYj1VWf4pqdPNHcG8/hnFRTKDsVyjS/f2OXL1/fIl8qkYxFKTmN1sK18ia18CadWJ50wScUjPH9rj81cOJ6DtxOm7Ub9uxbCdC8P2sks5Mnf8u1W8R2nhTzpymmQ+6L1nHY7x+2OSRD34DCf4f+bYbdWHrVRB4Oz0oszyDoDFeDHDv4DwLKsJLBPI1CYa9FohJWFBBvrjRZ8a0HkjVR2anW29qq8/lyWxUSUl27myS5Wefz0Ak7N5dr9/UO7sV06lWExFTsSYIRJmEbXTnrZVemPv4Du1qNwnIJ80tdlrlDhyp09HjsY0BjkBmOtxySIvA4z/dH/N+nE8Fsrj9Kon/FPQ89TP46beheNGQCgXKlxL1eiXKkBjYLgC6/ukIpHeer8UnPfdW8qXtyMsJ0rc/1BibVyjfXFBGdWDS6fzjTHCTi1Osm4eeiRQxiF6WZIJ0wKzviWXZ1Xx63YjrNaYL/CcF36C8eggpN2xySIvA4z/bE1qBvHWiiDGEcX/qwMKAzi6LQbwDt3FlIm59cXqPOwizoZi3Jm5fC0QO/CqTh1ItEoX/fkGk+cX+bMSuJQ1/bNzRJ3t4uh6HrvJUzd1uNedhXC9ZhkXKaha3TS12U2HT+0zfMog5Mg8trpM7qle9LHuJdpuE7DoudVaVnWO7u8PP3hUEDSiShL6Sjb+xVOHHRRP7GxCByNTr1W/6OnF0gnlqg4jZ+9feIvnkgTi8L9XKXjamizMmhlFgza4puFcxeGVnfYtbYYp7UFOa3pBl2ng+jnCD3T4/XrQSRk2m3my1y5s88bL2SbhbwZjXBjs8hSqsZu0WlWFt5ugaeXG6sVeheq97wNoFqHE9l4x4t40s9D5aFBC5x+Rs+H/dnbNFcQMj90nfavZ+ll2/Yj40jItItgYBqQTT3sMvMqibgZaS5z6vUKrKRNbm8VqTgul89kyKbjzedt/iV+O7UcFfGGx6AFTj+j59vt9jdps9Cj0WoW8yQyDF39ASk5NTb3ypScWvN3XiWRjJvN52qFssPNrRI3two8f2uPVPzouv79PIcL+7M66azbufMCBcMIXzAwi89fZzFPYTaP42umhWqSgEQMg1Q8TqRHIR43G1sd1+rw9LlFLp3KtK0UdNPMpzB3a85ib9Qs5inMFHyFl4KBgJxaTvL2x1c4tdx9ak3FqVOtw7m1NI+dzTbXJPAvNgS6aXpRsDR+s9gbNYt5CjMFX+GlOyAgdRfqGNR7TLRMJ0wunkhzaqWxsUquUGkuTuJfuUs3TXcKlmZDt6Cu3WvHXSVPJqs1+NK5CQ8FAwGJmxGWUmZz++FO/N3Ad7eLXLtXABozCS6fzhzZUUstlvYULM2GbkFdu9eGCQJnMXCclUp0Fs/NtFJNE5BcocKLt/Ns5ct93aSFssPWfpXVTKw5k8CbTdD6yECOUrA0G7oFde1eGyYI9M/qmVQFGnTlPSuVaKfzOSvBzjRRSRqQYqXGg1yZ7b1y8ybtdkGnEyYbq0lSieih34d1sw+RYQ1bqHvTa717yfvdoEGgf9XPSVWgQVfes9Iz1ul8zkqwM00UDARkbTHBGy8ssbGWbt6k3S5ob1GiOzvlQ4FD3IwceWQgMq1c1+1aqHe6R7z7IVeoBFYpBFWBDtNqDbrynvWesVkJdqbJbF5JE1Bx6uwUquwWqs0FTFov6NZZA/7XvUKx4tSbYwrUTSbTzjCMroV6r81xgMAqhaAq0GFarbNeeQdNx2v8FHYFpFSpce3uHtu5MoupGKuLySNzxgtlh6t39g5tUdxuE5N275PpNs8r3XW7fjutq9C6G16YqNUqs0hXc0CqtTqFSp10sv3cQm8Z4osnFjCjRnNpYn8F4Q8MLodgX/B5rsCCpr0kBhPmxZfCnDaRYSkYCMip5RR/5I2nSCaibQsKb3MirzJwanXubhfZ2quysZZsPlbw3huGLjJVYMFRa1JEwkzNvYCY0QjLi3FWFxNd15z3V/hb+1Wy6Sh7BYdr9woUyk6oRtGqAguOnoGKR9PmJIxUMgUkV6jwldd2ubm53/Ymb60MvJUIF1MxdksOq5nGrob9Ll40DqrARIIXpoBfxKMmX0CcWp3NXBnHqbOSSfTsVvcq2tbtigtlh92iw2I6RrKPnvlhn+trPIDMkmm6ntXjJmEU7rtmipjRCGvZBOdPpAe6yf2DkbbyJUoVh9PLib4/Y9hWhlonMkuCuJ7H1X2vHjcJI12NAcmm4zx1fomNtYWhbnJvOuGr9wvNBYn6MWwrQ60TmSVBXM/TGiBrDIIEQTVBQI473WjY6YTDfq+mR0kYDdvdH8T1PK0Bsmb9SBDUMxASZjRyaLMiRfoyj8bROu/Ukp7W7vtpDWIkXKbrqg+5oLrrprW7UuS4xlGxzdr9Na1BjISLrp4ADVvItAYRYZpeKDJO46jY1JIWOUq1TUC85YY7zQTo1muQK1R44WaOXKECNDY92i06VBw9JphmGtgVTqMOOHTeZRpNPDS2LGsV+DfAJeBV4Lts295u876fA74NuGfb9hvGmcZ++JcbblfI9Brk49/RQC2X2aCBXeEw7jUIdN5lGoWhZ+CHgU/Ztv048KmDn9v558C7xpWoQfWqwLu9nk3HD+1OqGeAs0FBXTiMe4yAzrtMozDUNu8GPnrw748C397uTbZt/zqwNaY0DaxXBd7tdVX+s0nnNRzGXTnrvMs0CkPoesq27dsAtm3ftizr5HE+zLKs9wHv6/aeZ555BoB8Pn+cr2pyXZd8Po/ruhiGEchnToKXj2k3C/mYhTxAePJhAMVCeai/DUsejkv5CI9J5WF5ebnja2MJBizL+i/A6TYv/WjQ32Xb9oeBD3d7z/Xr112AxcXFQL4zn89TJcaVu3s8djrTXCtg2uTz+cCOySTNQj5mIQ8wG/mYhTyA8hEmYczDWIIB27a/udNrlmXdtSzrzEGvwBng3jjSNArT2ycgIiLzLAwPtT4BvPfg3+8FfmmCaRlaNh3nCd8gQBERkWkRhmDgg4BlWdZLgHXwM5ZlnbUs61e8N1mW9QvAbwNPWJZ1w7Ks75tIajvQoCEREZlWhuu6vd81Y7wxA9lsNpDP6/b8J8z7rLemLYzPsYYxjnyM+rzqXITHLOQBlI8wmVQelpeXOz7NDlftNIPazXEOywpls7ZG+zjp2InILFEwMGLt5jiHpSLR4ijD07ETkVmikmzE2u2zHpaKJIg94OeVjp2IzBIFAxOgikRERMJEjwlERETmnIIBERGROadgYMzCMpNARETEo2BgzMIyk0BERMSjYGDMwjKTQETCRb2GMkkKBsZsHMsWq1ARmT7qNZRJUjAwg1SoiEwf9RrKJOmqm0EqVESmj9YfkUlSz8AM0g6KIjJt9HhzslRbiIhMuVmoSPV4c7IUDIiITLlZqEj1eHOydNRFRKbcLFSkGjMxWeoZCMgsdNOJyHTSOCE5Ll05AZmFbjoREZlPCgYCMk3ddOrFEBERPwUDAQmqm24cFbV6MURExE/BQMiMo6Kepl4MEREZPdUGITOOilqjdkVExE/BQMioohYRkXHTY4I5oAGDIiLSjYKBCRpXJa0BgyIi0o2CgQkaVyWtAYMiItKNgoEJ8lfSo+wl0OpkIiLSjWqHCfJX0urKFxGRSVEwEBLqyhcRkUlRMDBi/Xb/qytfREQmRTXPiHnd/7lCRdP7REQklBQMjJjX/Q9oTICIiISSHlCPmNf979TqmNGIxgSIiEjoqGYaEy0zLCIiYaXHBCIiInNOwUBAtP6/iIhMKwUDAdGiQSIiMq0UDAREiwaJiMi0Us0VEA0QFBGRaaWeARERkTmnYEBERGTOKRgQERGZcwoGQk5TFkVEZNQUDARkVBW2piyKiMioKRgIgOu6I6uwNWVRRERGTTVMAAzD4NxaciQVtqYsiojIqCkYCIgqbBERmVZ6TCAiIjLnFAyIiIjMOQUDIiIic07BgIiIyJzTAELBqdUplB2MSSdEREQmQj0D0lzYyHXdSSdFREQmQMGANBc2Mgz1DYiIzCMFA6KFjURE5pyCARERkTk38QGElmWtAv8GuAS8CnyXbdvbLe85D/w8cBqoAx+2bfunx5tSERGR2RSGnoEfBj5l2/bjwKcOfm7lAH/Ntu3XA+8AfsCyrKfGmEYREZGZFYZg4N3ARw/+/VHg21vfYNv2bdu2nz34dx54DtgYVwJFRERmWRiCgVO2bd+GRqUPnOz2ZsuyLgFfBfzu6JMmIiIy+8YyZsCyrP9C43l/qx8d8HMywL8Hfsi27VyH97wPeF+3z3nmmWcAyOfzg3x9R67rBvZZk6R8hMcs5AFmIx+zkAdQPsJkUnlYXl7u+Jox6YVmLMt6AfhG27ZvW5Z1Bvg127afaPO+GPD/Ap+0bfsfHOc7r1+/7gJks9njfExTPp9ncXExkM+aJOUjPGYhDzAb+ZiFPIDyESaTysPy8nLHxWTC8JjgE8B7D/79XuCXWt9gWZYBPAM8d9xAYFY4tTq5QgWnVp90UkREZMqFIRj4IGBZlvUSYB38jGVZZy3L+pWD93wd8D3AOy3L+vzBf986meSGg7eEcKHsTDopIiIy5Sb+mGASZuExgbe5UDphYkaDielmofsNZiMfs5AHmI18zEIeQPkIkzA+Jpj4okMyHC0hLCIiQQnDYwIRERGZIAUDIiIic07BQEA0ul9ERKaVgoGAaHS/iIhMKwUDAUknTM6tpUgnNCZTRESmi2qugGh0v4iITCv1DIiIiMw5BQMiIiJzTsGAiIjInFMwICIiMucUDIiIiMw5BQMiIiJzTsGAiIjInFMwICIiMucUDIiIiMw5BQMiIiJzTsGAzCXtMiki8pCCAZlL2mVSROQhBQMyl7TLpIjIQyoJZS5pl0kRkYfUMyAiIjLnFAyIiIjMOQUDIiIic07BgIiIyJxTMCAiIjLnFAyIiIjMOQUDIiIic07BgIiIyJxTMCAiIjLnFAyIiIjMOQUDIiIic85wXXfSaRi769evz1+mRURk7l24cMFo93v1DIiIiMw713X13zH/++Zv/ubPTjoNysds5WMW8jAr+ZiFPCgf4fovjHlQz4CIiMicUzAgIiIy5xQMiIiIzDkFAyIiInNOwYCIiMicUzAgIiIy5xQMiIiIzDkFAyIiInNOwUAwPjzpBARE+QiPWcgDzEY+ZiEPoHyESejyMJd7E4iIiMhD6hkQERGZcwoGRERE5pw56QRMO8uy3gX8NBAFPmLb9gcnnKS+WJb1KpAHaoBj2/bbLMtaBf4NcAl4Ffgu27a3J5XGdizL+jng24B7tm2/4eB3HdNtWdbfBL6PRj5/0LbtT04g2Ud0yMdPAH8BuH/wth+xbftXDl4LXT4syzoP/DxwGqgDH7Zt+6en7Xx0ycdPMCXnw7KsJPDrQIJGuf7vbNv+8Sk8F53y8RNMybnwWJYVBT4L3LRt+9vCfi7UM3AMByf7Q8C3AE8B77Es66nJpmog32Tb9lts237bwc8/DHzKtu3HgU8d/Bw2/xx4V8vv2qb74Fx8N/D0wd/844NzFgb/nKP5APh/Ds7JW3yFXVjz4QB/zbbt1wPvAH7gIK3Tdj465QOm53yUgXfatv1m4C3AuyzLegfTdy465QOm51x4/grwnO/nUJ8LBQPH83bgim3bL9u2XQE+Brx7wmk6jncDHz3490eBb59cUtqzbfvXga2WX3dK97uBj9m2XbZt+xXgCo1zNnEd8tFJKPNh2/Zt27afPfh3nkbBt8GUnY8u+egkdPmwbdu1bXvv4MfYwX8u03cuOuWjk1Dmw7Ksc8AfBz7i+3Woz4WCgePZAF7z/XyD7oVImLjAr1qW9TnLst538LtTtm3fhkYBCZycWOoG0ynd03h+PmBZ1hcty/o5y7JWDn4X+nxYlnUJ+Crgd5ni89GSD5ii82FZVtSyrM8D9wDbtu2pPBcd8gFTdC6Afwj8dRqPnTyhPhcKBo7HaPO7aZmr+XW2bb+VxiOOH7As6+snnaARmLbz80+AyzS6R28D//fB70OdD8uyMsC/B37Itu1cl7dOWz6m6nzYtl2zbfstwDng7ZZlvaHL20OZB+iYj6k5F5ZleWOBPtfnn4QiDwoGjucGcN738zng1oTSMhDbtm8d/P8e8B9pdEvdtSzrDMDB/+9NLoUD6ZTuqTo/tm3fPSgI68A/5WFXYWjzYVlWjEYF+q9s2/4PB7+euvPRLh/TeD4AbNveAX6NxvPnqTsXHn8+puxcfB3wJw8GaX8MeKdlWf+SkJ8LBQPH8xngccuyHrEsK05jEMgnJpymnizLWrAsa9H7N/BHgd+nkfb3HrztvcAvTSaFA+uU7k8A321ZVsKyrEeAx4FPTyB9ffEKigPfQeOcQEjzYVmWATwDPGfb9j/wvTRV56NTPqbpfFiWdcKyrOWDf6eAbwaeZ/rORdt8TNO5sG37b9q2fc627Us06oT/atv2nyHk50IrEB6TZVnfSuP5UBT4Odu2f2qyKerNsqxHafQGQGP6zr+2bfunLMtaAz4OXACuA99p23a/g9zGwrKsXwC+EVgH7gI/DvwiHdJtWdaPAn+exojxH7Jt+z+NP9VHdcjHN9LoBnVpTD36i94zxjDmw7KsPwT8D+BLPHw2+iM0nrdPzfnoko/3MCXnw7KsN9EYlBal0cj7uG3bf6fbPR22PEDXfPwLpuRc+FmW9Y3A/34wtTDU50LBgIiIyJzTYwIREZE5p2BARERkzikYEBERmXMKBkREROacggEREZE5p2BARAJnWdZPHCy0IiJTQFsYi8jQLMv608BfBZ6ksSX254HQr7UhIocpGBCRoViW9VdpbMP6fuCTQIXGErjvBvYnmDQRGZCCAREZmGVZS8DfAb7XtycBwC8Dv2xZ1k+0vP9PAv8njd3YPg/8Jdu2nzt47W8APwhkaazJ/v22bX/KsqwIjZ3f/gKwTGMP+PeHbVVMkVmgMQMiMoyvBZI8XNa6I8uyXgf8AvBDwAngV2gEDHHLsp4APgB8jW3bi8Afo7HcLDQChG8HvgE4C2wDHwoyEyLSoGBARIaxBjywbdvp473/C/D/2Q1V4O8DKeAPAjUgATxlWVbMtu1Xbdu+evB3fxH4Udu2b9i2XQZ+AvhTlmWpR1MkYAoGRGQYm8B6nxXzWeCa98PBNrSvARu2bV+h0WPwE8A9y7I+ZlnW2YO3XgT+o2VZO5Zl7QDP0QgeTgWVCRFpUIQtIsP4baBEoxv/3/V47y3gjd4PB1sGnwduAti2/a+Bf21ZVhb4WeD/Ar6HRsDw523b/s2gEy8ihykYEJGB2ba9a1nWjwEfsizLAX4VqNLYf/6bgILv7R8HftiyrD8C/DrwV4Ay8FsHYwY2gN+kEVwUedhj+TPAT1mW9V7btq9ZlnUC+IO2bf8SIhIoPSYQkaHYtv0PaKwx8LeA+zRa8h8AfrHlfS8Afwb4R8AD4E8Af8K27QqN8QIfPPj9HeAk8CMHf/rTwCeAX7UsKw/8DvAHRpopkTlluK476TSIiIjIBKlnQEREZM4pGBAREZlzCgZERETmnIIBERGROadgQEREZM4pGBAREZlzCgZERETmnIIBERGROadgQEREZM79/0JgfkSYNtMNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"Close\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    tesla.plot.scatter(x=var , y=\"Log_return\",alpha=0.1, s=1.5, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "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": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns; sns.set_theme(color_codes=True)\n",
    "\n",
    "sns.lmplot(\n",
    "    data=tesla, x=\"Close\", y=\"Log_return\", height=6,\n",
    "    scatter_kws=dict(s=1.5, alpha=0.35));#Here is the space for your codes"
   ]
  },
  {
   "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
}