{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***6304640094***\n",
    "\n",
    "***Aus Atsavakovith***"
   ]
  },
  {
   "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": 107,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "# We will import all these here to ensure that they are loaded, but\n",
    "# will usually re-import close to where they are used to make clear\n",
    "# where the functions come from\n",
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "\n",
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40477227577865854"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cnt\"].corr(df[\"temp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ezden62KeXLTo9WGBRwoDTfqEFDEas6WpadOGGdq0Cp6G515pQjIZ4LlXmvKq5ueDCViqogfI9yOOvmuSgho+IRHGh9QijcbowmQv0d4RdNleiI0ofCn1T8KG9i0dw4dsAx+e2zhCgU9IhPEh11hj6pZ82y5M2b17Jbpsw8C0fK9Ub0CzoLCRwx42dCsUBpr0CYkwNprKmGLD1O0iFezxBZONqpq5iKCX6g1oFhQurCUS0lzZaLhEeg41fEIijI2mMi4wrVJnQ2s1LRijicA3Nembfh/wQ8OXNHgXixK6DbpDDZ+QIsaHQDPNOCSN0IaA0AS8mfLQ3PGhHLcn+KDhS5YnF4GDLqxbUYMaPnFOXFbecbnOpWt24s6GrVi6Zmfex5C0Zx+0Vo2VQrrn0n7JZN+/b+8u20yEbdXRWEp8CAz0xbrlExT4xDlxCdjx4TpdLDpMA9U0+KC1aoSYdM9NnwlNT4Gwhe223UeQTAahZhLYwIdFh29Q4BPnFMvKWxKmNq7T1O/s4sVqw+8sac82uuVJ2LBUSPdc2u+iI5/pIlAqYKQ+RshZGaQ79OET5/jiVzZF8hHauE5TP6SLaGgbfmtpriTN1kYEvWSpsHEO6Tql/dXjzH3fps/UbTUVmD9znFEPdx8a9MQRaviE5ImzUqsG2pQPpnAbmPayt3EOTZR+2BYVG2bqKFjYojDGKEINn5A8cdXRK5c2FZcyqhWjBuHou62oGDUo437Ndfbv2xutbe05A95MkSwq0v1aumZnuqNfWBH/Vkopf9Qtz+faDqQ71PAJyUIU/Iguyqj6MA+Sdq25TtOiNpoIecmiIt0vFwGQpmx9ORW0l29HQFI4KPAJyYIPUfYSLkyfNubBh0WDhGTS10TIS8GF0v2SxqCZxyjMNSkMFPiEZCEKfkQXqUc25kHS0KUIeRt18CUN/aG54/HEwuqspnTNGKRFwf5DJ5EMUtt88CGOoHpcKq4kzEwCDVzY9Bz68AnJQjH4EV1ElttAMmUnk0GX7YVorlOjoedC6rYHyHEC0nXaMOmHHbdhI0pfQnM/Gcnfc6jhE1LEuHBLaDQtKb9cMmVLVgbNdbqoc992tr3L1vY5NHEEktUnCpqxxpIRBQucb1DDJyQLLrqjhY0vNcslK4EUkS59X3OdD80dn7Nbno37LUXpS3n00jyYWikAc83YRZS+hmKwwLmGGj7xElMtxIYW44N2bFppT+PjN50rTZ170xrzEtrrzDWXklbpojWtC+3bVDO2UVpXwkbFwbCfuShCgU+8RBK20o/VhrB20bBFVXs95Jer6VxphJx0Do0J1xTTdDKNSV96ZqTrlObJRhljU2yU1pWwEYzqwzPnGxT4xEtMfbY2/HsuqtSpaq8bvFw19eFN50rzfdNz+KCNVYwahJIEshb/AcyD7qR5svFMmi7wbqupwLqG6d6b0+nj7w59+MQ6Lnx8ks/Whn/P1P+t8QlraqubRERrBFAUfKE2IrJHXNo/XcUuH85rgltfLtwYNM+kVK1POkYxxK4AbvoSRA1q+MQ6NszQNiqrmWJ6DlsFa3L5nSXN14UJWHOd0mckn62N7mo+9BWQFmA2nnvpHFItAI07zSSuxBfi2D6XAp9Yx4WPLwrYMClKiyfp5SwVk7GBC5O+9HLWLDpMW+xqFk+atDkTNAsbaZzSgkDlTgs5roSEA036xDo2CnMUg7nNmlvhI/dI1v2G82RqwtVcp/QZU3O5Zh6klDbpmdO4R8ZfNRTbdh/B+KuGaofeBalwj2aepMWd5FZQpUDmeCYBN02AJIrFNWETCnziJWH7laPyMpAWTzbmKQoVy6T7ZWMeTMveAvJcSsLWRp69NFemAlizoPehCVAUnmvX0KRPYomLhjA+RJZrcJF+aIqLFCpbZW1NouxtxFxEoemTCxil3x0KfBJLrPnXpRx64cUb9qLARTBbVBY2EpKw1QhjKdZAeu6k1D9NQZqwF3BRCdqLY1CeBAU+cY4PAsLGy0CVQy8sKsLWXF3UJLdxDZIgs9EtTxLYUoCjxgIgPdumwYea51ZawJn+/jRBezYq5RH7UOAT5xSLydEHDcKHxZMGqQCQ5D+XuuVpkAS2i3LM0jzYsDxJxzBdoGmycHz4bZDuUOAT58TFt6ZZ2JhqQqb57dpx5kJzDtO2sJKZ2oWwtlFaV7rOKHS6i0qlPdIdCnziHB9W/5qSs6ZoFjamcyGdQ3N8HxZgUv66ZKa20R5Xmoemj4R0Uw6TviTQpTFIz6VGO5c+E3atAMCPhQnpDtPySCxxkTYUhZK1gPk4NbnhUo671EdeyrO30R5XcisEF2zzQUqJc/FcSrUCbKSsMiXOT6jhk1jiQ9cxG5h2FXSFZGkwjSzXWDKWrtmJL9//bFbt2YawDfu5snF8yQJgIwizGJolFSPU8ImXhF0Yx0b1L2mMmmswvU5Js3WhaUnV4TRIwtbGdbjQnjUR8ib3u1BV63qKqdWIFoJwoIZPrGMjTzcKkfw28vBttCo1yfu2gaY6nKnGZuM6TPPsNb5vyVJher9txJ5I1+FDSp0PcSXFCDV8Yh0bq3MbNeLDxoZfOezr1GhaplqnRsM3LTlrIx5C8uFL2rlmYSMdw/R+27BSmJbvddH+OirxL1GDAp9YR9NcQ8KHH7xp/XYbTWVcYNq4Rgq4A2RB50PtdWmMmoWNdAzT+927VwLtHYFRASIJaXHmwtwelV4XUYMCn1jHRrc8HzAVhD7g4sXpwhpjo/uapJlKwlizsAl7AdfeEXTZhoF0Py8bkrLG5AqwNH3u6MMPB/rwCcmTKEQS28hPNy0XqxmH5De2YQHY+nKqJKxRhTnBrxyFZ0Kaa+l+anovWKnmRx++dSjwSSxxUZnNBdJ1aNLdTIvaaObSRoGgKGAq6KSeATZ6Clgp9iSU1jVFM8YoLK58gwKfxBIbZW990EKk69BoxtJ1SIsGG3nbLiofmloyXLTolXoGaEz6YdcC0JTWdRHpL90PLgi6Qx8+iSUav7MPAXUSNvzn0nWats8FZJ+si6A9KUrfh5gNG/dTul/3PvpcOvjwsfuq8j5PLnz47TAOoDsF0fC3bt2K+vp63Hjjjfj2t78NANixYwfq6upQU1ODVatWpT+7b98+1NfXo7a2FosXL0Z7e36pJIR0xoYJ2YXGZ4oNTctFB7diwVS7lsr7apDmWkrLkzRjG3U2bCA926bVG4sR5wL/7bffxvLly7F69Wps2LABr7/+OrZv345FixZh9erV2LRpE/bu3Yvt27cDABYsWIBly5Zh8+bNCIIAa9eudT1kQgqG9HL1IY7AxqLChe9aKq0roblOU2uIC0uHVHhHVVAqGRS8KJaN4MK44Vzg/8d//AemTp2KYcOGoU+fPli1ahX69euH0aNHY+TIkejduzfq6urQ2NiII0eOoK2tDZWVlQCA+vp6NDY2uh4y8RAX/jkffIDSy1XS5mwsCKRj2IjSl3zXQwf167K9EM29MhWmNjoPmsYqaBY+kuVp/FVDUZJA1uY5pjEdvhAXq1JPcO7DP3jwIPr06YN77rkHR48exaRJk3DllVeirKws/Zny8nI0Nzfj2LFjXf5eVlaG5ubmHp1v4ED9Q1lSksDgwRf36PgkM2HP5bY9TSkBsqcJ82eO6/H3/+bp/8Xm/zmI2htG4+6brs3rHDdO+N30McK61trxuc8xf+a4nNcvfV/DJ8svwaHmU/hk+SV5H0Mah7S/s7DOtF/zPIwamrqOUUMzX8eAfn1w+sw5DOjXJ+/rlO6HdB2dybS/c9CeZoyZPtM5ViHTWKVrOHriTHrr8/tSug5fcCl3nAv8jo4OvPTSS/jpT3+K/v374xvf+Ab69u2LROLjFWsQBEgkEkgmkxn/3hPee681q9ZwIbkCekjPCHsuJ1UOTwU3VQ7P6zw/f+FAevuViZfndY6vTLw8/d2wrvXs2XPpbaZzSAVObIzx8LFT6W2mY2iK4kjj6Mk4M+3XPA//92ufST+XmT5z+sy59DbTfhvBbp1LCGc6h7S/M5r7mc8xpPt52eB+qcI7g/vxfWkB2+/KkpIEhgwZkHGfc4F/6aWXYsKECRg8eDAA4Itf/CIaGxvRq1ev9GdaWlpQXl6OYcOGoaWlJf3348ePo7y83PWQiYf4EAVsA0lgS5HjNiKRpRe8jbK40nWaVtLTPA+m5zCtQQ8AFaMG4ei7ragYNSjj/qaP5rApy1y6KK0r3U+Nb5ylcf3EuQ9/8uTJeP755/HBBx+go6MD//Vf/4UpU6bgrbfewsGDB9HR0YGNGzeiqqoKI0aMQGlpKXbt2gUAWL9+PaqqwkkjIe7wwTeuCcAy9X9rrtP0HDb8lNILXvJdayLTbdQLMMXFOaR7Ls1DcMH2QlyU1pXup8aH70MwKemOc4E/duxYzJ07F7Nnz8bUqVMxfPhwzJo1Cw0NDZg3bx6mTp2Kyy+/HFOmTAEArFy5Eg8//DCmTJmC1tZWzJkzx/WQiWV8eBnYCMCS0FyndI7EBdueoll0mBakqRg1CCUJZNVaAVlISGOQgtVsLCI17W8lpIA5HwLJpLmW7qdm4STdbxeFlkh3ClJ455ZbbsEtt9zS5W8TJkzAhg0bun12zJgxeOqpp1wNjThA03yjGNBcp2SKljQ+G53NJPO2jXNIZmBpDJJmqymaI7Xg1TTHMUW63yUJIBmktvkidfWT7oWNAkTSOXzojhhHWFqXOCcq+bGmlggbLzVJ65Q0KRsNX2ykaZlqtja0b02v+rC1b2mu+5b27rK9EE1anhRrUPLRaqLEZFUhYGrRIeHA0rrEOS7aqUpogop8GKekdUpCTBPMJmno0jE0CxvTIEspKr56nHyvTC1LNgLmpLmWhLUNH77pMSRLCWBu0XFBHAMLKfCJc3yIsNeYoU3HqXkxSkhCSlqURGVhY/ry1dwrU1N21dhU6l/V2OE9Ht95TOdas+iQnjvpGJJLQOpJAMjXaZoxYYM41tqnwCexRPPiNRVCGteF9OIz1eBtLGxsaEKm6YdSDrx6YbOnCZMq8xPYGt+2JExNF5Ea7VyyuEjH0NTaPz+P+V6LNEYX2rcPC13X0IdPihIpCthGOVhT3zcgv/hMe49rxiDNlTQPmhRH00ZDkhDSxFvsfL0ZyWSAna9nrtZpw6/sIm0ubKR50NTSl+636hwhZ/LYaKAVNajhk6LERsCcZE63Ec0smU/3HzqJZDLIu3uaRqOU5kqaBxsuGo0PPhcabU1aNBRL5LhprIFkYreRZSMVIIpLJo9rKPBJJJFMfjb856bZBBpzuhSU58L0Kc2VjawK0/shfd9FXIi0ONMg3S8bgYGPL5ic93c1aBZG0lxJvw0bz1wcg/IkKPBJJJFeGDYCgUx9fJrvS5+RBJ1kZdC89KS5suHrPHn6t122FyJdhyQAXLzcbZTWla7ThrC+63u/TC8aMh0vgVRdh/CS8uS5kjR4Gxq+DQtcsUGBTyKJjeh0CUlrtKF1Sp/RRETnwsZLz0ZQn6mwlO63jcI7NjC1Ath4bqU4AqmYkwurkWQlKBb3im9Q4JNIIgkhG4JOevG5SCeyEREdNi7Sm2yY7E0XTxokF40UqyA9t5oFhalbwMZvx4c8e9O4kGKEUfqEZME0stzaGJJB3mPQRNCb1qHXVNqTKsRJ45QyCXyp3CZVyjONDNdYSqrGDkdJAlnrBWiq9ZkiPVNS5URNZUUfmnBFDQp8UpRoBJ0pPrxwJEHnIv1QE2AlmZn3HzqJZICs2Qi2WrbWL9yY9/3SPFOaHHaTZ0YjrKX7Kd0LSdhWj0ulipqkYUrzpFnYSNfpQ5Mu36DAJ0WJixxbH14oNnydUq6+dJ2aXH9pYWJ6HRorg2QtMe1MqEEShJJA1+T5m/YEkIStaaooID8PGouNjT4SYeODUtAZ+vAJyRMXlbqqx5lVh9Mg+celiOmdrzcjGaS2+WYCmGJj4SMFs9motCdho3CPabyDFCegmWvJfy49D1KePmCnj0TY+Fa+lxo+IXniwopwW00F1jVMz3oOycys0TCkz0gveBvpajZ8uqbYOIcksF24mkx7zUuBhxpLSOdFYD5j1FjPfNDgJXwbIwU+8RJTU5j0fc3xXbycw0bz4jQNTtT4laX5lhYNmuh36V5JvufxVw1FSSK1LRQ2njlpgSadQxJSkiUEMK9qqBGU0oLbB3O6b+V7KfCJl5j6x6Xva4Scix+r6UtJug4bGobkT9WYoU0XFZK/1sa9cpGVIT2X0nXYWBBIAZJRQfrt+JBl4xsU+MRLTAWVD6Y0jTAP+6WkEYSSKdtGmVNTbIxB0/QlF5pAMmkupYXLvY8+hzsbtuLeR5/La4waJO3aNLAQkOdKWri4sEzFEQp84iWm5joXmlKxaBiS+dWHxZOU3+7iGBWjBqEkgZyBZNJcSgsX6fs+PFMai44moC7X79PGM+ci7iNqcCZIJDGNftVE8Eq96m1E4EahGphpiWENUn13VY/2kMv7au63NBcuMjtMy/tKz6TmfpeUJJDsCFBSkl+2gub3KY3zsfuq8jp3MUOBT7xEErYu2mdqAotyvXA0wtyH1CFTXKTESdgoBysJsiDous2EJp0sbEwXNtIzKTVCAmQrgLRAu/fR59KLlmyCuxh+O66hSZ94iY3KarnQ+Nclk6AU/GQjkEyqDmdaklZzDBf4YH6VnjnNokQyuUu+acn37cM8aRYU0jhNK/GR/FAJ/EWLFnX72/z5860PhhAtpj4+TVCQaa96G0jV4UxL0vrCY/dV4YmF1Vm1ORuV2Uwr6dmoQR92FTwg/IqBmuObpji6WNj4kLbnmpyzuXz5cjQ3N2PXrl04ceJE+u/t7e14++23Qx8ciS+SOdzUnGejV70NTFuR2hDopuZwjetCMpdLLhzJVK6p5Cdp6JLve+igfjhyvBVDB/UTz5UvNu5nIpFyOyTylPjSM6m1dJzfZjqGDf+76W/Htyp4Lsip4d9yyy2oqanBgAEDUFtbm/5vxowZ+Nu//VtXYyQOsbHqNW1SAvhXsCITNjq0SaZNH7rASc+EJq/btP+5NE82nlvTYjEaTKPsNc9DMui67SkuMgGkZ8ZGSqt0DB+yT1yTU+Bfe+21qK+vxz/90z/hpptuSv83Y8YMjBo1ytUYiUNsNIQxzXd2gY08Xx/y0yVsLBikubIhCE3H6aKRkcbMHLY5PSouGgnTBZ4G0yJHxYjKh3/06FHcfvvtmDFjBurq6tL/keLDxqpX07ms0Ni4ThvHkALmTF/wNhYlUv66DX+rlOMuLQg090LywUvXIcV0ADq3Qa5z+ICNIE4fAkHjqMFLqJ66ZcuWob6+HldddRUS+TqGSCSwkeoSBc1Xg+R3tjFXYacWaeIQJH+qjd7lEpLPV1r4aOZRShXTFCAyjelg9Lk7mLbXHZWG37t3b3zta1/DDTfcgM9+9rPp/wjJxKTrU01KfF5Za0zAxbBwiYvZ0kXEtdQBLipIVgYb7hEfKgKS7qgE/pVXXon9++OTukD8x/QFr3E7FINJUDNPLvzfpkgmYhcCxoZ27oOpW3JNSM+9jfREG/gwl1FDZdJ/++23cfPNN2P48OEoLS1N//3ZZ58NbWAkurhIdzE9h0Z7NzUJmqYNaejdK4H2jiDry1czTzYqBpoipcT5YJ6V5joqSFUqpbnW1NKXSiVLaObah2ciaqg0/JkzZ2LkyJH49re/jdmzZ6OpqQn33HNP2GMjEcWFSV/S0F2k5LhonmPamtbGdbpwC0TBt60RdNL9Mn0mbGQBuIj0Nw1eTH6UU5jMN7cQ8SysI6ES+E8//TRmzZqFz372s6ivr8e8efPwzDPPhDw0ElVuq6nAuobpoQoISUO3kZIjlaV1YQqXrtNFLQAJzRgk82vY6WyuzhG2MNUUvfEhE0Ay+0tuBY3LTRLoUXBVuUYl8E+ePIk5c+YAAEpLS/HVr34VLS0toQ6M+IsPK2dJc7Wh2Wqa5+Q6h0YQSnMpvfgemjseTyyszlppzoVvW9M2VsK0eY5mrn1ImXPhd5asJdIYbPy+q8YOR0kitc2E9NvRLJykZ7sYYnBsoxL4HR0daG7+ODL1+PHjCHK1jCJFTRRWzjbM0JJGKJ1DEydgWtTGxeLLhuvCdOGhaQIkIWmdkqC0YU3xIWtCuhcuit64wIe59g2VwP/qV7+KL3/5y7j//vvxwAMP4KabbsLcuXPDHhvxFGnlbKO0roSLF4qp1ukiE0B6OWsiqqXP+JBiZVqaF9D54HNhw5IRF6RnX3qmohKB74O1syeobFe33HILrrnmGuzcuRO9evXC17/+dXzqU58Ke2zEU6ToWBdR+qZFUDQR9FLkuIRGCElzKRX/kdAIOVNBGBdMmwy5wjRC3gamsQyaCHwpe8RFlkzUGvCo32RjxozBmDFjwhwLKRImXT8C2/Y0YVJlZv+dDUxTcjQ/1PFXDcW23UeytviUOrxpkF5KD80dj8GDL8aJEx/mdXzNosV0YWMjba9YUt58wLQroHQ/TZ8XWxSD4uEalUmfkJ7gIkrfFI0p3dS/rgkC03T8MnGPaOq/S73LbZhXJbdBXKwMNmIRTJFiFSTft4sUSo2p3DT11oY5PmpxAhT4JJZofqimUfi2aswnk0HWBYEUWKhZ2EiLDmmuND7+KAh0F62Ii6XbXdhoYnSkz0gteH0ILHSNvy2bSGR5csv+tEk/35WvC/+bKfma8Ttj6qOXzLc2qpHZuBemJnsf/NI2kMzh0jyZPi8abLiqTJGqAQKyOV2TVhslc7wNqOGTLtgwc23bndJKo958oxg0AI0J2bROvcZ1YVo5TVrYuKhAZ8MCoHGx5MKFhcDGOUxrGmhLX+eyPLmw2EQNCnzSBRtCLgrd8jQLG01aXS40L72wX+DaTAETP6TGddG3tHeX7YVIiw5pLk1TKDWcPP3bLtt8kFwsPrg+XCxspPtto2iOVJSqGBb0PYUmfdIFG2au22oqMH/muLwjy12gieA1bY8rRfkDsonXNCLahgnYRhS+JlAs14LDVDMGzOdSs7CR5rtzi91idlWZNuixgeSKiqNJnwKfdMGXDlRhd2jT/Ng1fsRcaBYVkhCR9ktCTPPyvut7v0z7jR9fMFn8/IVo/POmPnzTewG4iS6XLCqm99MGLs5huli2UfMgKnUTXEKTPnGOxpwedrqL5vimLy0XtbxtaL6SGVkyfdoo7iM9E8US3S65JmzcTwkXC58o1LGPo0mfAp9YR8odj8oPzfSlpVlUmOa4S2O0kfdto0mQFFTnomOfKTbOMWjARV22F1JSkuiyLQQ+lIvVzLXpsx2FRYltKPCLDB9+rFKUvmkwnIaoFNWQcoVN0WjGktZpYx5Mg+qkhZGpNUaDDSuDdAwbQXumEfI+NM/R3E9pLqVnJmpFc2xAgV9k+KA9S1H6mhenqcD2YR40GoipELHRPEcy8UbB3C5lAcQJFyZ7CVNLhUb7lqwAkkC3oRT4oGD1BAr8iGFaTtIFNkrrmgpsG/MgzXUUBKFGY5Q0Qh8WT9LCxoaQM9WMyceYxmxoMO1eaOO59uG30RMo8COG9IBFwUyl8Vubmv1tzIM01y5+7C5805KwdLGI9EHYPnZfFZ5YWI3H7qsq2BjigrSAs1FaV8JGvIQPClZPKKjA/853voOFCxcCAHbs2IG6ujrU1NRg1apV6c/s27cP9fX1qK2txeLFi9HeXjgzlQ9E7QHLhI0IeVMNQWNul+ZaWpTYEGI+WAlcYKqh25hrF41totLn3RTT+6FZ8Eufkd4RNuIloqBgdaZgAv+FF17A008/DQBoa2vDokWLsHr1amzatAl79+7F9u3bAQALFizAsmXLsHnzZgRBgLVr1xZqyF4QtQcsXyRha7q6t1GBzjTnOirYKK0bNjbm2kb3Q5JCykaQFj6a36ekFEjviDjez4Jc6XvvvYdVq1bhnnvuwRtvvIFXX30Vo0ePxsiRIwEAdXV1aGxsxBVXXIG2tjZUVlYCAOrr6/GDH/wAs2fPLsSwiUOkAkCmVbI0Feii0MDHBwYNuAitbe1ZX+4ucFFMxlb3w/PbQj1TLuZKEtg2Cnz1LU1dR7ZATekdEUfXTUE0/GXLluFb3/oWfud3fgcAcOzYMZSVlaX3l5eXo7m5udvfy8rK0Nzc7Hy8pGeY9nDXYGrp0AT8RC0gJ18kbcuGNhY2moI1phqdjQY9PiAtXFy4HST3iWYMxWJBc4lzDf9f//Vfcdlll2HChAlYt24dACCZTCKR+PhnFAQBEolE1r/3hIED9cFOJSUJDB58cY+OT7rTWYuZP3NcgUeTmV++fATBR9tsY/xk+SU41HwKnyy/RHwu8t3fue1rPscY0K8PTp85hwH9+uQ9htLSPkAigdLSzMeQ9mvOIX1m1NDUXI8amt9ca+5VZwGRzzk61xIo1HX2ZH++x5g/c1zO363mGqTnuvMiMZ8xAPKzv21PU2rBvqfJ2/cQ4FbuOBf4mzZtQktLC770pS/h/fffR2trK44cOYJevXqlP9PS0oLy8nIMGzYMLS0t6b8fP34c5eXlPTrfe++1qltyDh58sdcNX6KIr/PZ+eWdbYyHj51Kb6XryHf/5E49AzJ9prP5NdP+02fOpbf5jmHzzgNIBqntVyZe3uP9nevka+53ps/83699Jud+6Rw9uVfZzmFzf7bPuHimTI8hubIONZ9Kb7MdX/p9dXap5fuOaG07l95mOsakyuGp66gc7u17CLAvd0pKEhgyZEDmfdbOouQnP/kJNm7ciPXr12P+/Pmorq7GmjVr8NZbb+HgwYPo6OjAxo0bUVVVhREjRqC0tBS7du0CAKxfvx5VVfHzu0SN6nGpwjs+RyJrTIYuirlIrgkX0etStLO0X9PrXkovNI2o9qXwjmT292WcubBRaU/CNIcekAN74xLg3BO8yMMvLS1FQ0MD5s2bh6lTp+Lyyy/HlClTAAArV67Eww8/jClTpqC1tRVz5swp8GiJxP5DJ5FMBlnLxUalOpWms1nnbRiYnmP8VUNRkkDOFr1StLO0XyPEJD+/qZCx4c/VVCWUkEoI0++cwof4GBdpmL5R0GVmfX096uvrAQATJkzAhg0bun1mzJgxeOqpp1wPLbbYiEyXXu6atrFL1+xMm/wytXg1HadmDBI2Xt7SdZieQxMVLkUzS61pfRBipu13ATt52Z191/ngIoLeBdJ1SM+c5vdt+hv2IdjUNV5o+MQfbKy8JfOtpnhQTxYN+aAZg2m0sub7LsynEqb1BmwgadfSfhvCWkJT9dC0SZAPiycb1R1Nr0Pz+5ZcTZIGzzx8EntM89sB4KG543MGomhycKU8edNxasaw8/VmJIPUNh8NwkausQtMrSXV48yfmccXTM65XxLoppq1huaTZ7ps88HGXIVNxahBOPpuq5F/XULSzjW/b2khKu3XpHIWGxT4pAsuhJRGwGQy49s8h2YMplpKVAr3SC9fF0JKcuFImGrWGmxYEcJuh2wDF8WBgqDr9kJsvIckt4LkqorK77cn0KRPnOOiS5VkKteY0k2DuHww12uQ3BuSkLJxP4vFnyqZw4vlOk2xsUCT5lpasJuW5o0iFPjEOTYaALlIb9Kkm5niohueKZKQMu1saAMb/lgblfRMBboPz0NUfNumc22ajhpF/L6jpCjRmOtMo9dtmKGlWt02zmHqurCBZNKXIuB90FrHXzUU23YfyZl+KOHCLSDR9NEcNoU4l5Kp24VvWxqDqYsHkOOATNNRowg1fGIdG7X0Tc1pUuS5jVrdnYP68sWHXGCpL7iLCHhTXLhPXGjfLhYd0nPtQrOVxqBZREr3QxLY0nVqLJFRqSlyHgp8Yp1tu48gmQyMfF82zP65eO6VVJ3t515pyvoZybTpoiWrhDRGjZk6CgLdB2xYMmwU9wkbG5qt6XVqFlfSOKV3iPR9TaW+qPn5adInXbARmTrp+hHYtqcJkyqHWx7dx5ia0zVCzoecaAnJ/OqDmZp8jA+LKymFsaQkgWRHkNXio0FKs5TQmPGl1L2wW2zbOoZLKPBJF2xUoLutpgLzZ47LmofvooqWhIu8bRdIcQYuKJbqcHFBWgS6KmKUy7+ueUeYpu5FpU6GTWjSJ10I25QO6Pyt0jhMTWkazdfUZ+vCfGujt7lk9nfh2vCBqESnh42N51bybUvNc6JiKo/KOM8T7yebdMOXVa80DhearWRWlLQUjVkzbO1YUy1QWvw8dp//HSptZExI7pG4WDJspKNKxXuk/VJRHA0uCufQpE+IgOblHHZTGQ3SGKSgn7u+98t0Ols24W+aAiUJIc08SWl39z76XPocvgp/GxXsJN91sVgyJCRh62LhYyNwMGy3YBShSZ84x4foV425XhqD5HbQ+EKllDgJG0JIGmcUBJ2NCHoXvusouA2kudQ8D6aNpzSpgZLbwDS9UJNyFzWTPgU+8RJJmJq+UDQahIt4BknIRCGNKwpj9IUoLJ58QLOAk4St6SJQI8xdvCNsQoFPIolkJZBW5zZK87oo9uJDGpdkDfFhjESPZGWwsYCTfhs2LB1hFwjSCHONtdInKPCJl5iayqTvazStqJnrwsKH0rnEHtKz72IBZ6N8r/RcmloBoybMNVDgEy8xNZXZ0OBdNOgxRdLGfGjGQqKFi2fGRfleKZAzamVxbUCBT7zE1GQvaTEas6ULf6upFnJRn15dthdy8vRvu2yLFV+C4aTnyvR+Fws2rEbSXErniKMFz1/VhZAcSHm8EsXid5YWJZpFSzHkl/sSDCc9V77UuchFVFw40lxK6aY2cv2jBjV8JXE0/8QdF6ZNF4F/Er4IyzjgQ3fEKGDDYiMtvoqx/a1EdJf0jmERB7+wUVlNIo4vhKgSFStFFLRnSTN2gabAk1QYS6qEGZVqfjahhq8kavmWheTJLftRv3BjqNYQFxG0UmARA+L8gVYKe9gorSsh+d81lhDJOibV67ex+PLBQtcT/F4Oe0QUfG++4IM1RLIAaLQY6YUQBW2N+EUULBE22uNKSO9TW8L4/Jbv7hT+PnUksrgIhpFMadILpViC9ki0iIIlgr+N4oUmfWIdG75vKUiyWFJqwnYL+JKuJrXgJf7golSy9PvW/C5Mn20bv72opVlSwyfWmXT9CGzb04RJlcPzPobkFohaW8pshO0W8EWjlFrwxgUfAuIkXGj40u9bak0N6Gpt5JprG4qJqav3yS370+9KF24HavjEOrfVVGBdw3SjB1gKkizGspe+wuY49qC5PIWNKpamPR5cVPuT2Lb7CJLJwJmlkho+sY6NVau0co5aOkyUcRG1bYqUgkX8woblSWMFyIUPabc2rKE9gQKfWEcy12mEtfQZKQJ36ZqdaQFg+mLIF435Ngom3r6lqchyn3sK+PDyJnqkbAUbv1/pHD4817fVVGD+zHE4ceJDJ+ejSZ9YRzKVaQLuTPNbJd+4i2A2jfk2bBOvDXO8L3EAuYhCoyPyMVK3PBuxLTbKThcbFPjEOpK25YPvLC4/9rj4jONyP13gImbDhwWajSj9qJVc53KYdMGGuV3yS7koWCP5dKNgSiekELhYJEoLtARSGR1h/jolV4HmXehDkbGeQA2fdEFjbvchB15anUtWBl80X+anE9KdRKLrNhNha9ea91zUSq5T4JMuaB5g6TNSqomNYhWSQI/KDzHs/HQbsQq+FO8h0cH0mdG4/SSBbLqY1rxDopYezF8w6YKmkIT0Gam0ro2+BFLhHfY+sAf94/aIiytJCsqTXG4at5/0nvnxwuqeDLkbxfgOoYZPrCNp3zZMcfsPnUQySG2LGVMthcLaL6rGDkdJIrUtZs433snWgMdGHA+bV/UcCnzSYySBPen6ESgpSeQ2+Qu+McnsL/3YoxI9K5k+WZK2uPAh/kXCRpS+LzEypCsU+KTHmL60NCk5kgYvCUoXfapt+LY19cA7b3uKL2VxTS0VYTcZcoWk+fqAD8Jac7+j1rjGByjwSY+Rglm2vpwK2ssmbDVmZkmD98FU7WIMpi9fGy9vG4sGU0tF00fPQVPEzbc+CNMooDHXSwFzkpUvKlZAmzBoj/QY02AWqeSlDeISHOWCxxdMzrnfRc40XRvRwoffn1R+O2o59Daghh8zXKxqq8elfPjZTG1SBK8N4qJJSW4FG3n+S9fsxJ0NW7F0zc6M+30Qxkwd9Aup4ZKN59L0XSZZKovRAkCBHzNcBA1J7XE1ObaSD48v+BSSW+HHC6vxxMJqoxQlybzqg3/dBxePC3yJyZCQfuPDP3pWhueohNl5m4mw43SiEGDZUyjwY4aLVe2TW/ajfuHGrMfQ+Oei4MOPAvc++hzubNiKex99LrRzMD1Kj+niKCqWK+mZkPa7uE5pweBDzw/bUODHDCn63caqVqq0ZwNq+DrisjCKiubLxZE9wo7SL8aWy3xbxgzphSNVrwLMm+fYIC6CjOiIiuYbF1wE5krBw1I1PwmpmieQim85fw6pGY8PUMOPGZJJUaOBRCXHnRBSGMZfNRQlidQ2H2wE9VWMGoSSRGqbD5o6+VGz2FDgx4yH5o7HEwurQ12NSnn4GqjBExJdTJUCG5kfknvSh2BT11DgxwwpKE/zI4jjD4WQuODCuuaiSp4UoGzDRx+1an+0l8YMqRiFjQh6CR+KchBCMjP+qqHYtvtI3uZ4wPw3biMGwLSrpxSrpDmHb1DDjxhRKAYh+d80AVamVgRaIYiPRCE2xUaMjvQbl8ztNlx6UsEoScN3EavkGgr8iGGaNicJQhsmKhv+NyngRlpURC2YhsQDxqak0DTQkpCUH+kdIJn8i5GCCPzHHnsM06ZNw7Rp0/Dd734XALBjxw7U1dWhpqYGq1atSn923759qK+vR21tLRYvXoz29nj/UEwL50hBe5rIVBd+K2lh40M51ygQlfx0U+JyncWCtPDRWEKKsRJe2DgX+Dt27MDzzz+Pp59+Gs888wxee+01bNy4EYsWLcLq1auxadMm7N27F9u3bwcALFiwAMuWLcPmzZsRBAHWrl3resheIQlkF2YozaIgF5qXcxTaiNrARvpRLuKSnx6X6ywWJIGusYRIyo+kmEgLhqgF5Glw7kgqKyvDwoULcdFFFwEAfu/3fg8HDhzA6NGjMXLkSABAXV0dGhsbccUVV6CtrQ2VlZUAgPr6evzgBz/A7NmzXQ+7aNAEopgiBexoXs5xeYHTUkHiiA3XhhQwZxq0V4w41/CvvPLKtAA/cOAAfv7znyORSKCsrCz9mfLycjQ3N+PYsWNd/l5WVobm5mbXQ/YK07Q6aVVrIyjw8QWT8cx367K2VdVo+FEIbiKEFA7Td5Xk4y9Gl0HB3qa/+c1vcPfdd+P+++9Hr169cODAgfS+IAiQSCSQTCaRSCS6/b0nDByoX72VlCQwePDFPTq+azqn1c2fOa7b/h/e/39yfv+T5ZfgUPMpfLL8kozXum1PU+oh39OU8fgA8DdP/y82/89B1N4wGnffdG23/fO/vw2Hmk9h1NBL8IM/ndRtf2ftPdt8X/qJfjjUdgqXfqKfeE8y7R/Qrw9OnzmHAf365PX9nn4mCufwYQwuzuHDGFycw+cxjBp6SfodkM8xOlsJs31f867Skukc0rvSFi7lTkEE/q5duzB//nwsWrQI06ZNw4svvoiWlpb0/paWFpSXl2PYsGFd/n78+HGUl5f36FzvvdeatSfzhQwefDFOnPiwR8cvJPmM9fCxU+ltpu9PqhyeMvlXDs96/M07DyAZpLZfmXh5t/2Hmk+lt9IYs+03PcYP/vj3xXNo99s4hg/n8GEMLs7hwxhcnMPnMZj+fjsrBdm+r3lXacn0feldaQvbcqekJIEhQwZk3mftLEqOHj2KP/qjP8LKlSsxbdo0AMDYsWPx1ltv4eDBg+jo6MDGjRtRVVWFESNGoLS0FLt27QIArF+/HlVVVa6H7BWmgSRSoIsmIM+0baQLc30U6hUQElei4LIrxrQ957P94x//GGfPnkVDQ0P6bzNnzkRDQwPmzZuHs2fPYuLEiZgyZQoAYOXKlViyZAlOnz6Nq6++GnPmzHE9ZK+QAlFcBOWZlqR87L7wF21SRUFW+yOFIIFUgGbcnzopaE/T6a6zjz3Tb1x6F0rniFoVPQ3OBf6SJUuwZMmSjPs2bNjQ7W9jxozBU089FfawigbpRyAJwnsffS5d0jKbYNa0jcyFDy0l45IFQPwiClkZPiyGNUqF9B6S3nVRaGdrG3/tKRHEhXYtYSqMNekypitfVsEjxF98WAz3LU3V0s9Via8YNfCwocC3iLSitIG0qPDhRyBpCDYaYxBCiheN4uGDghU1WEs/YpjmhrqoHiVpCKwnTkhxY9q8ykZp3SgEBrqGAt8iPvR4NkVzDS4i4NntjpDoYuq2s1Fa11Sx0LznopYNxKWPRVyY003PIQX1aY4vHUNCY9Knn58Qkouw37ea95zpu9A11PBjhmm3PcA8D58mfUL8JSqdB8PWrjXW1Kjl6lPDJ13QBB6a5uETYhsfUsmKBR+i9DVI2rVkSbQRAO1DkHRPoIZfZCxdsxN3NmzF0jU7M+630RDCVMMnxDZREVLEHtJ7qO1se5fthbhoJOYbFPhFhuT7Pp/Xmiu/VULS8BlwV1wkLtiS4sZFdLuNc0jvIWlBIO0vxm55FPhKorLak4StDf+55LdiwF1xEYXqcFyU2MNFjI2Nc0gCW3oPSQuGqPnnNdCHryQq0ZhSuUhNjWqJqPmtSPEThUUJsYtpLJFUlVTznota8R8KfCWmJWt9wcWCgBASXaJSCdP0nWxDcYmKIngev++oR0RFq5VWnNJ+RuATEm9cmPRtLCpM38k2tPOoKYL04RcZUqDJ1pdT+8+n312IJqjPNJ4hKnm+hJBwGH/VUJQkUttCYSMo77aaCqx5oDoSyiBAgV90mAaaaFb3pj8UplAR4i8uSoRLiocGU8XDRhGyqARzn4cm/SJDMnPZ8NFrWlcSQqKJDfdl9bjwTd2m/nPpOllalxCwNC4hxYwNrXX/oZNIBqltPmhqeUgaunQM6TpZWpd4z9I1O9MafKaIfObIE0JyIZXX1pQxNn3PaIKHJQ1dGoOmjLhEVIK5z0MNv8iQHnJp1asJqGOfaULii40YHOk944PmzEp7JPJUjBqEkkRqmwnNj5km/XjBBV5xIQlb6X5rqhpK50gmgy7bC9FEv0smeUm5kfb7sOiwDQV+kSH90GysWlkrP148dl8VnlhYjcfuqyr0ULLCVE890qJeWtBrqhpK57AhTKV3mWlp3WKEAr/IkH5oNjrdSVYCUlzc++hzuLNhK+599LlCDyUrTPWMFjby100XDdL3i9GkTxtdzLARtBe1VBRiBl04pDM2Unul4GINpinI0vejVkVPAzX8IsOFud2GlYAQ4icu3iGS4rF0zU7c2bAVS9fsDO0ccYQC3yEuqjKZ+qU0vlD+kIhvRCWw0IUwlebCtJKejd+/NA+ac4T9PtWY9KNWaY8C3yEuyklKfilJoNMXSqLIoAEXddnGGVMXjGlqr4aH5o7HEwurs5rzNQs4SSCbLmw0MQJR8/P7vRwm3ZCKRex8vRnJILXNtN9WDq1UeIMQl0TF6uTDOE0Lzjw0dzwGD74YJ058mPUzpqV1NYsWG/3sc6H5ftT8/BT4DnFRX9pFgBWtAISQQuNDlTsfxtATaNJ3iI1UFBedrAiJGszDt4eNd4ypqZvvuXCghq/kyS3709p5WCs6G+eguZ3EEVqd7GFDa71sSColLt9MnqhpzlGBGr4SF8EZmnNIn+GLj5B4E3aUvoY4VrGLAhT4SlzUVdacgznwhJBcSBkLkmvRRqoZ31N+QpO+El9MTKZRvjT5E1LcSO8IyXVoo5ImNXw/oYbvEGnl7KKxDU3+hMQbqR6IDe28b2nvLlviBxT4DpEEug23gQ95voSQ6GJDO5fSg6NWoa5YoMB3iLRytpG2JxGVEqSEkHCQgvZsKB6SpTFqFeqKBb71HWKrBnWuDlDS/raz7V22hJB4IcUj2YhXqhg1CEffbc3aQjtqFeqKBQp8Jb7k4UvmNml/Mui6JYQUF/379kZrW3tWK57pu+zJLfuxbU8TJlUOz/p9qXyvL0HQcYMmfSU2TFCSKU3TXEcyt7lIHyTEN+JSaU/TuEay4pm+y7btPoJkMqA5PoJQ4CvRCFIpEMWFj56QOBKX7BONW1CKFTKNwtd8n6Vx/YQCX4lGWLuoHy2dw0YLXkJIdJEWBaaxRMyxjy4U+BaRrACsYEWiCDM74oX0ntK8gxiF7ycU+BaRrACS9q3RzqXVtcbHR0hPcNFyOS5EIdZAEtYaCwFjifyES3aLuIjkN+1CRQgpHFGINbCRMscofD+hhm8RFz78sP1zhJDiJnHB9kIYXFy8UMO3CHtAE0J8J7hgS+IDNXyLmPrXWV+aEFJopPcQ44SiCwW+Eo0wlgJVJHM7U+oIIWHDOvfxhQJfCX8EhJBiQLJESq1tGScUXSjwlbjIPbWR7xyFtB9CSOGQLJFMwyxeKPCVuMg9tdHJLgppP4SQwrHz9WYkg9Q2E5LSUD1uBEpKEiybG0Eo8C3y3CtNSAapbSZc9KEmhJBcSBq8pDTcVlOBdQ3TmVEUQSKRlvfss8/iRz/6Edrb23HHHXfg1ltvdT4GqeUkoPuh8EdCCCGkEHgv8Jubm7Fq1SqsW7cOF110EWbOnIkbbrgBV1xxhdNxaPxaCaRyW7N5z5eu2Ykjx1sx4tL+eGju+G77pR7ShBBCSL54b9LfsWMHxo8fj4EDB6J///6ora1FY2NjoYeVEamgBaNbCSGEFArvBf6xY8dQVlaW/nd5eTmamzMHmxBCCDGD3RGLF+/vaDKZRCLxsZE8CIIu/5YYOFBfDaqkJIHBgy8WP5ftM51N+pk+c+OE38Xm/zmI2htGZ9wvfV87Dlf7i+UcPozBxTl8GIOLc/gwBhfnyLZ/QL8+OH3mHAb065PXe+gf/9+NOc8L6N+VRMblXCaCIPA6f+vpp5/GSy+9hBUrVgAAfvjDHyIIAtx7772q77/77mkkk7pLHDz4Ypw48WHeYyUfw7m0B+fSHpxLO3Ae7WF7LktKEhgyZEDmfdbOEhKf+9zn8MILL+DEiRM4c+YMtmzZgqqqqkIPixBCCIkU3pv0hw4dim9961uYM2cOzp07h1tuuQXXXXddoYdFCCGERArvBT4A1NXVoa6urtDDIIQQQiKL9yZ9QgghhJhDgU8IIYTEAAp8QgghJAZQ4BNCCCExgAKfEEIIiQEU+IQQQkgMoMAnhBBCYgAFPiGEEBIDIlF4x4SSEn2jnXw+T7LDubQH59IenEs7cB7tYXMucx3L++Y5hBBCCDGHJn1CCCEkBlDgE0IIITGAAp8QQgiJART4hBBCSAygwCeEEEJiAAU+IYQQEgMo8AkhhJAYQIFPCCGExAAKfEIIISQGxFLgP/vss5g6dSpqamrws5/9rNv+ffv2ob6+HrW1tVi8eDHa29sLMMpoIM3lf/7nf+JLX/oSZsyYgW9+85t4//33CzDKaCDN5Xm2bduG6upqhyOLFtI8vvnmm7j99tsxY8YMfP3rX+czmQNpLl977TXcfPPNmDFjBu6++2588MEHBRhldDh9+jSmT5+Ow4cPd9vnRO4EMeOdd94JJk+eHJw8eTL48MMPg7q6uuA3v/lNl89MmzYt2L17dxAEQfDggw8GP/vZzwowUv+R5vLUqVPB5z//+eCdd94JgiAIHn300eChhx4q1HC9RvNcBkEQtLS0BFOmTAkmT55cgFH6jzSPyWQyqKmpCbZv3x4EQRB873vfC7773e8Warheo3kmZ82aFWzbti0IgiB4+OGHg0ceeaQQQ40Ee/bsCaZPnx5cffXVwdtvv91tvwu5EzsNf8eOHRg/fjwGDhyI/v37o7a2Fo2Njen9R44cQVtbGyorKwEA9fX1XfaTj5Hm8ty5c1i+fDmGDh0KAKioqMDRo0cLNVyvkebyPEuWLMG9995bgBFGA2keX3vtNfTv3x9VVVUAgHvuuQe33nproYbrNZpnMplM4sMPPwQAnDlzBn379i3EUCPB2rVrsXz5cpSXl3fb50ruxE7gHzt2DGVlZel/l5eXo7m5Oev+srKyLvvJx0hzOWjQIPzBH/wBAKCtrQ2PP/44vvjFLzofZxSQ5hIA/uEf/gFXXXUVxo4d63p4kUGax0OHDuHSSy/FokWLcNNNN2H58uXo379/IYbqPZpncuHChViyZAm+8IUvYMeOHZg5c6brYUaGFStW4NOf/nTGfa7kTuwEfjKZRCLxcfvAIAi6/FvaTz5GO1enTp3CXXfdhTFjxuCmm25yOcTIIM3lr3/9a2zZsgXf/OY3CzG8yCDNY3t7O1588UXMmjULTz/9NEaOHImGhoZCDNV7pLlsa2vD4sWL8Xd/93d4/vnnMXv2bDzwwAOFGGrkcSV3Yifwhw0bhpaWlvS/W1pauphYLtx//PjxjCYYIs8lkFq5zp49GxUVFVixYoXrIUYGaS4bGxvR0tKCm2++GXfddVd6XklXpHksKyvD6NGjce211wIApk+fjldffdX5OKOANJe//vWvUVpaiuuuuw4A8Id/+Id48cUXnY+zGHAld2In8D/3uc/hhRdewIkTJ3DmzBls2bIl7c8DgBEjRqC0tBS7du0CAKxfv77LfvIx0lx2dHTgnnvuwY033ojFixfTUpIDaS7nz5+PzZs3Y/369Xj88cdRXl6Of/zHfyzgiP1Emsfrr78eJ06cwBtvvAEA2Lp1K66++upCDddrpLkcPXo03nnnHbz55psAgF/84hfphRTpGc7kjvUwwAiwYcOGYNq0aUFNTU3w+OOPB0EQBHPnzg1effXVIAiCYN++fcHNN98c1NbWBn/yJ38SnD17tpDD9Zpcc7lly5agoqIimDFjRvq/RYsWFXjE/iI9l+d5++23GaWfA2ke9+zZE9x8883B1KlTgzvvvDM4fvx4IYfrNdJcbtu2LairqwumT58e3HHHHcGhQ4cKOdxIMHny5HSUvmu5kwiCILC/jCCEEEKIT8TOpE8IIYTEEQp8QgghJAZQ4BNCCCExgAKfEEIIiQEU+IQQQkgMoMAnhIjceeedOHHiRKGHQQgxgAKfECLy3//934UeAiHEkN6FHgAhxG8efPBBAMAdd9yBv/7rv8Zf/MVf4OjRozh37hymTZuGe+65B4cPH8Ydd9yBz3/+89i7dy86Ojowf/58/Mu//AvefPNNXHPNNXjkkUfQ1NSE22+/Hb//+7+PV155BUEQYNmyZVmbihBC7EENnxCSk4cffhgA8Pd///d48MEHcfPNN2PdunV46qmnsGPHDmzatAkAcPjwYUycOBHr1q1DZWUlVqxYgUceeQT//u//jpdeegl79uwBADQ1NeEzn/kM1q9fjz/90z/Ffffdh3PnzhXq8giJDdTwCSEqzpw5g1/96ld4//338Zd/+ZcAgNbWVrzxxhu47rrr0KdPH1RXVwMARo0aheuvvx4DBgwAkGqt+v7776O8vByf+MQnUFdXBwCYOHEievXqhf379+Oaa64pzIUREhMo8AkhKhKJBIIgwD//8z+jX79+AIATJ06gtLQUJ0+eRJ8+fbo0SOrTp0/G4/Tq1avLv5PJZLe/EULsQ5M+IUSkV69e6N27NyorK/GTn/wEAPDBBx9g1qxZ+MUvftGjY504cQLPPfccgFS3uj59+uBTn/qU9TETQrpCDZ8QIjJlyhTcfvvt+Ku/+it8//vfR11dHX77299i+vTpmDFjBg4fPqw+VmlpKdavX4+VK1eib9+++OEPf0gNnxAHsFseIcQZhw8fRl1dHXbv3l3ooRASO2jSJ4QQQmIANXxCCCEkBlDDJ4QQQmIABT4hhBASAyjwCSGEkBhAgU8IIYTEAAp8QgghJAZQ4BNCCCEx4P8DthZCnxHyh3IAAAAASUVORK5CYII=\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=1, s=4, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CALCULATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cnt\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "\n",
    "model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        1\n",
      "1        1\n",
      "2        1\n",
      "3        1\n",
      "4        1\n",
      "        ..\n",
      "17374    2\n",
      "17375    2\n",
      "17376    1\n",
      "17377    1\n",
      "17378    1\n",
      "Name: weathersit, Length: 17379, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = df['weathersit']\n",
    "y = df['atemp']\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn \n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[1 1 2 ... 1 1 1].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [184]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:662\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    658\u001b[0m n_jobs_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs\n\u001b[1;32m    660\u001b[0m accept_sparse \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpositive \u001b[38;5;28;01melse\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 662\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    663\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    664\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    667\u001b[0m     sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X, dtype\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mdtype)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:581\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    579\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    580\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 581\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    582\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:964\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    962\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my cannot be None\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 964\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    967\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    969\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    979\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric)\n\u001b[1;32m    981\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:769\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    767\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[1;32m    768\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 769\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    770\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    771\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    772\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    773\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    774\u001b[0m         )\n\u001b[1;32m    776\u001b[0m \u001b[38;5;66;03m# make sure we actually converted to numeric:\u001b[39;00m\n\u001b[1;32m    777\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[1 1 2 ... 1 1 1].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This LinearRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [183]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mae_test \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mmean_absolute_error(y_test, \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m      2\u001b[0m mae_train \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mmean_absolute_error(y_train, model\u001b[38;5;241m.\u001b[39mpredict(X_train))\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe mean absolute error of the train data is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmae_train\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, while that of the test data is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmae_test\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:362\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m    349\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:343\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decision_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m--> 343\u001b[0m     \u001b[43mcheck_is_fitted\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    345\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(X, accept_sparse\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m], reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:1222\u001b[0m, in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m   1217\u001b[0m     fitted \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   1218\u001b[0m         v \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m(estimator) \u001b[38;5;28;01mif\u001b[39;00m v\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m v\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1219\u001b[0m     ]\n\u001b[1;32m   1221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fitted:\n\u001b[0;32m-> 1222\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NotFittedError(msg \u001b[38;5;241m%\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m(estimator)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m})\n",
      "\u001b[0;31mNotFittedError\u001b[0m: This LinearRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "mae_test = metrics.mean_absolute_error(y_test, model.predict(X_test))\n",
    "mae_train = metrics.mean_absolute_error(y_train, model.predict(X_train))\n",
    "print(f'The mean absolute error of the train data is {mae_train:.4f}, while that of the test data is {mae_test:.4f}')"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_train = 0\n",
    "err_test =0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train= y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_train += np.sqrt(np.mean(e_train*e_train)) # compute rmse for the estimation rmse test data\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))\n",
    "rmse_10cv_train = err_train/10              #average the rmse\n",
    "rmse_10cv_test = err_test/10  \n",
    "print('RMSE on 10-fold CV on the train: {}'.format(rmse_10cv_train))\n",
    "print('RMSE on 10-fold CV on the test: {}'.format(rmse_10cv_test))"
   ]
  },
  {
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = list(zip(unemp, month))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The umemployment rate in Apr is 14.8%\n",
      "The umemployment rate in May is 13.3%\n",
      "The umemployment rate in June is 11.1%\n",
      "The umemployment rate in July is 10.2%\n",
      "The umemployment rate in Aug is 8.4%\n"
     ]
    }
   ],
   "source": [
    "for x, y in zip(unemp, month):\n",
    "    if x > 8:\n",
    "         print(f\"The umemployment rate in {y} is {x}%\")"
   ]
  },
  {
   "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": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 500]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LOGG = list(range(2,501,2))\n",
    "LOGG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer is 2550\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(101):\n",
    "    if i % 2 == 0:\n",
    "        sum = sum + i\n",
    "        \n",
    "print(\"The answer is\", sum)"
   ]
  },
  {
   "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": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yfinance\n",
      "  Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
      "Requirement already satisfied: numpy>=1.15 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.21.5)\n",
      "Requirement already satisfied: requests>=2.26 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (2.27.1)\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 /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (4.8.0)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.24.0->yfinance) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->yfinance) (1.16.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (1.26.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/aus/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2.0.4)\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"
     ]
    }
   ],
   "source": [
    "pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open        High         Low       Close    Volume  \\\n",
      "Date                                                                   \n",
      "2022-06-28  244.483337  249.970001  232.343338  232.663330  90391200   \n",
      "2022-06-29  230.500000  231.173340  222.273331  228.490005  82897200   \n",
      "2022-06-30  224.509995  229.456665  218.863327  224.473328  94600500   \n",
      "2022-07-01  227.000000  230.229996  222.119995  227.263336  74460300   \n",
      "2022-07-05  223.000000  233.146667  216.166672  233.066666  84581100   \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",
      "2022-06-28          0           0.0  \n",
      "2022-06-29          0           0.0  \n",
      "2022-06-30          0           0.0  \n",
      "2022-07-01          0           0.0  \n",
      "2022-07-05          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",
      "[66 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yahooFinance\n",
    "import datetime\n",
    " \n",
    "startDate = datetime.datetime(2022, 6, 29)\n",
    "\n",
    "endDate = datetime.datetime(2022, 9, 30)\n",
    "GetTESLAInformation = yahooFinance.Ticker(\"TSLA\")\n",
    " \n",
    "print(GetTESLAInformation.history(start=startDate,\n",
    "                                     end=endDate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pandas_datareader import data as wb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "TSLA = wb.DataReader('TSLA', data_source='yahoo', start='2022-6-29', end='2022-9-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-06-28</th>\n",
       "      <td>249.970001</td>\n",
       "      <td>232.343338</td>\n",
       "      <td>244.483337</td>\n",
       "      <td>232.663330</td>\n",
       "      <td>90391200.0</td>\n",
       "      <td>232.663330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-29</th>\n",
       "      <td>231.173340</td>\n",
       "      <td>222.273331</td>\n",
       "      <td>230.500000</td>\n",
       "      <td>228.490005</td>\n",
       "      <td>82897200.0</td>\n",
       "      <td>228.490005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>229.456665</td>\n",
       "      <td>218.863327</td>\n",
       "      <td>224.509995</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>94600500.0</td>\n",
       "      <td>224.473328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>230.229996</td>\n",
       "      <td>222.119995</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>227.263336</td>\n",
       "      <td>74460300.0</td>\n",
       "      <td>227.263336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-05</th>\n",
       "      <td>233.146667</td>\n",
       "      <td>216.166672</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>233.066666</td>\n",
       "      <td>84581100.0</td>\n",
       "      <td>233.066666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2022-06-28  249.970001  232.343338  244.483337  232.663330  90391200.0   \n",
       "2022-06-29  231.173340  222.273331  230.500000  228.490005  82897200.0   \n",
       "2022-06-30  229.456665  218.863327  224.509995  224.473328  94600500.0   \n",
       "2022-07-01  230.229996  222.119995  227.000000  227.263336  74460300.0   \n",
       "2022-07-05  233.146667  216.166672  223.000000  233.066666  84581100.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-06-28  232.663330  \n",
       "2022-06-29  228.490005  \n",
       "2022-06-30  224.473328  \n",
       "2022-07-01  227.263336  \n",
       "2022-07-05  233.066666  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TSLA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log return is Date\n",
      "2022-06-28         NaN\n",
      "2022-06-29   -0.018100\n",
      "2022-06-30   -0.017736\n",
      "2022-07-01    0.012353\n",
      "2022-07-05    0.025215\n",
      "                ...   \n",
      "2022-09-26    0.002467\n",
      "2022-09-27    0.024798\n",
      "2022-09-28    0.017066\n",
      "2022-09-29   -0.070530\n",
      "2022-09-30   -0.011097\n",
      "Name: Log Return, Length: 67, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aus/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:397: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "TSLA['Log Return'] = np.log(TSLA['Adj Close']/TSLA['Adj Close'].shift(1))\n",
    "print(\"Log return is\", TSLA['Log Return']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TSLA['Log Return'].plot(figsize=(8,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aus/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "sns.distplot(TSLA['Log Return'].values, kde=False, norm_hist=True, ax=ax[0])"
   ]
  }
 ],
 "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
}