4) Exploratory-Data-Analysis ( jupyterlite ).ipynb

{
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
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  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Data Analysis with Python\n\nEstimated time needed: **30** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Explore features or charecteristics to predict price of car\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Table of Contents</h2>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n<ol>\n    <li><a href=\"https://#import_data\">Import Data from Module</a></li>\n    <li><a href=\"https://#pattern_visualization\">Analyzing Individual Feature Patterns using Visualization</a></li>\n    <li><a href=\"https://#discriptive_statistics\">Descriptive Statistical Analysis</a></li>\n    <li><a href=\"https://#basic_grouping\">Basics of Grouping</a></li>\n    <li><a href=\"https://#correlation_causation\">Correlation and Causation</a></li>\n    <li><a href=\"https://#anova\">ANOVA</a></li>\n</ol>\n\n</div>\n\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>What are the main characteristics that have the most impact on the car price?</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"import_data\">1. Import Data from Module 2</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Setup</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "you are running the lab in your  browser, so we will install the libraries using `piplite`\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # you are running the lab in your  browser, so we will install the libraries using ``piplite``\n    \nimport piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['scipy'])\nawait piplite.install(['seaborn'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Import libraries:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n\n# install specific version of libraries used in lab\n\n# !mamba install pandas==1.3.3\n# !mamba install numpy=1.21.2\n# !mamba install scipy=1.7.1-y\n# !mamba install seaborn=0.9.0-y",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function will download the dataset into your browser\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#This function will download the dataset into your browser \n\nfrom pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Load the data and store it in dataframe `df`:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "This dataset was hosted on IBM Cloud object. Click <a href=\"https://cocl.us/DA101EN_object_storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\">HERE</a> for free storage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you will need to download the dataset; if you are running locally, please comment out the following\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "\\#you will need to download the dataset; if you are running locally, please comment out the following\nawait download(path, \"auto.csv\")\npath=\"auto.csv\"\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"auto.csv\")\nfilename=\"auto.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "\ndf = pd.read_csv(filename)\ndf.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   symboling  normalized-losses         make aspiration num-of-doors  \\\n0          3                122  alfa-romero        std          two   \n1          3                122  alfa-romero        std          two   \n2          1                122  alfa-romero        std          two   \n3          2                164         audi        std         four   \n4          2                164         audi        std         four   \n\n    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n0  convertible          rwd           front        88.6  0.811148  ...   \n1  convertible          rwd           front        88.6  0.811148  ...   \n2    hatchback          rwd           front        94.5  0.822681  ...   \n3        sedan          fwd           front        99.8  0.848630  ...   \n4        sedan          4wd           front        99.4  0.848630  ...   \n\n   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n0                9.0       111.0    5000.0       21          27  13495.0   \n1                9.0       111.0    5000.0       21          27  16500.0   \n2                9.0       154.0    5000.0       19          26  16500.0   \n3               10.0       102.0    5500.0       24          30  13950.0   \n4                8.0       115.0    5500.0       18          22  17450.0   \n\n  city-L/100km  horsepower-binned  diesel  gas  \n0    11.190476             Medium       0    1  \n1    11.190476             Medium       0    1  \n2    12.368421             Medium       0    1  \n3     9.791667             Medium       0    1  \n4    13.055556             Medium       0    1  \n\n[5 rows x 29 columns]",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>make</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>...</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>horsepower-binned</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>94.5</td>\n      <td>0.822681</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>154.0</td>\n      <td>5000.0</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500.0</td>\n      <td>12.368421</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>10.0</td>\n      <td>102.0</td>\n      <td>5500.0</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950.0</td>\n      <td>9.791667</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>4wd</td>\n      <td>front</td>\n      <td>99.4</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>8.0</td>\n      <td>115.0</td>\n      <td>5500.0</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450.0</td>\n      <td>13.055556</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"pattern_visualization\">2. Analyzing Individual Feature Patterns Using Visualization</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "To install Seaborn we use pip, the Python package manager.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import visualization packages \"Matplotlib\" and \"Seaborn\". Don't forget about \"%matplotlib inline\" to plot in a Jupyter notebook.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h4>How to choose the right visualization method?</h4>\n<p>When visualizing individual variables, it is important to first understand what type of variable you are dealing with. This will help us find the right visualization method for that variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# list the data types for each column\nprint(df.dtypes)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "name": "stdout",
          "text": "symboling              int64\nnormalized-losses      int64\nmake                  object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                 float64\nstroke               float64\ncompression-ratio    float64\nhorsepower           float64\npeak-rpm             float64\ncity-mpg               int64\nhighway-mpg            int64\nprice                float64\ncity-L/100km         float64\nhorsepower-binned     object\ndiesel                 int64\ngas                    int64\ndtype: object\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h3>Question  #1:</h3>\n\n<b>What is the data type of the column \"peak-rpm\"? </b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['peak-rpm'].dtypes",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "dtype('float64')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "For example, we can calculate the correlation between variables  of type \"int64\" or \"float64\" using the method \"corr\":\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   symboling  normalized-losses  wheel-base    length  \\\nsymboling           1.000000           0.466264   -0.535987 -0.365404   \nnormalized-losses   0.466264           1.000000   -0.056661  0.019424   \nwheel-base         -0.535987          -0.056661    1.000000  0.876024   \nlength             -0.365404           0.019424    0.876024  1.000000   \nwidth              -0.242423           0.086802    0.814507  0.857170   \nheight             -0.550160          -0.373737    0.590742  0.492063   \ncurb-weight        -0.233118           0.099404    0.782097  0.880665   \nengine-size        -0.110581           0.112360    0.572027  0.685025   \nbore               -0.140019          -0.029862    0.493244  0.608971   \nstroke             -0.008245           0.055563    0.158502  0.124139   \ncompression-ratio  -0.182196          -0.114713    0.250313  0.159733   \nhorsepower          0.075819           0.217299    0.371147  0.579821   \npeak-rpm            0.279740           0.239543   -0.360305 -0.285970   \ncity-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \nhighway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \nprice              -0.082391           0.133999    0.584642  0.690628   \ncity-L/100km        0.066171           0.238567    0.476153  0.657373   \ndiesel             -0.196735          -0.101546    0.307237  0.211187   \ngas                 0.196735           0.101546   -0.307237 -0.211187   \n\n                      width    height  curb-weight  engine-size      bore  \\\nsymboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \nnormalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \nwheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \nlength             0.857170  0.492063     0.880665     0.685025  0.608971   \nwidth              1.000000  0.306002     0.866201     0.729436  0.544885   \nheight             0.306002  1.000000     0.307581     0.074694  0.180449   \ncurb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \nengine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \nbore               0.544885  0.180449     0.644060     0.572609  1.000000   \nstroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \ncompression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \nhorsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \npeak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \ncity-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \nhighway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \nprice              0.751265  0.135486     0.834415     0.872335  0.543155   \ncity-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \ndiesel             0.244356  0.281578     0.221046     0.070779  0.054458   \ngas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n\n                     stroke  compression-ratio  horsepower  peak-rpm  \\\nsymboling         -0.008245          -0.182196    0.075819  0.279740   \nnormalized-losses  0.055563          -0.114713    0.217299  0.239543   \nwheel-base         0.158502           0.250313    0.371147 -0.360305   \nlength             0.124139           0.159733    0.579821 -0.285970   \nwidth              0.188829           0.189867    0.615077 -0.245800   \nheight            -0.062704           0.259737   -0.087027 -0.309974   \ncurb-weight        0.167562           0.156433    0.757976 -0.279361   \nengine-size        0.209523           0.028889    0.822676 -0.256733   \nbore              -0.055390           0.001263    0.566936 -0.267392   \nstroke             1.000000           0.187923    0.098462 -0.065713   \ncompression-ratio  0.187923           1.000000   -0.214514 -0.435780   \nhorsepower         0.098462          -0.214514    1.000000  0.107885   \npeak-rpm          -0.065713          -0.435780    0.107885  1.000000   \ncity-mpg          -0.034696           0.331425   -0.822214 -0.115413   \nhighway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \nprice              0.082310           0.071107    0.809575 -0.101616   \ncity-L/100km       0.037300          -0.299372    0.889488  0.115830   \ndiesel             0.241303           0.985231   -0.169053 -0.475812   \ngas               -0.241303          -0.985231    0.169053  0.475812   \n\n                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\nsymboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \nnormalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \nwheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \nlength            -0.665192    -0.698142  0.690628      0.657373  0.211187   \nwidth             -0.633531    -0.680635  0.751265      0.673363  0.244356   \nheight            -0.049800    -0.104812  0.135486      0.003811  0.281578   \ncurb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \nengine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \nbore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \nstroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \ncompression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \nhorsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \npeak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \ncity-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \nhighway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \nprice             -0.686571    -0.704692  1.000000      0.789898  0.110326   \ncity-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \ndiesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \ngas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n\n                        gas  \nsymboling          0.196735  \nnormalized-losses  0.101546  \nwheel-base        -0.307237  \nlength            -0.211187  \nwidth             -0.244356  \nheight            -0.281578  \ncurb-weight       -0.221046  \nengine-size       -0.070779  \nbore              -0.054458  \nstroke            -0.241303  \ncompression-ratio -0.985231  \nhorsepower         0.169053  \npeak-rpm           0.475812  \ncity-mpg          -0.265676  \nhighway-mpg       -0.198690  \nprice             -0.110326  \ncity-L/100km       0.241282  \ndiesel            -1.000000  \ngas                1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>symboling</th>\n      <td>1.000000</td>\n      <td>0.466264</td>\n      <td>-0.535987</td>\n      <td>-0.365404</td>\n      <td>-0.242423</td>\n      <td>-0.550160</td>\n      <td>-0.233118</td>\n      <td>-0.110581</td>\n      <td>-0.140019</td>\n      <td>-0.008245</td>\n      <td>-0.182196</td>\n      <td>0.075819</td>\n      <td>0.279740</td>\n      <td>-0.035527</td>\n      <td>0.036233</td>\n      <td>-0.082391</td>\n      <td>0.066171</td>\n      <td>-0.196735</td>\n      <td>0.196735</td>\n    </tr>\n    <tr>\n      <th>normalized-losses</th>\n      <td>0.466264</td>\n      <td>1.000000</td>\n      <td>-0.056661</td>\n      <td>0.019424</td>\n      <td>0.086802</td>\n      <td>-0.373737</td>\n      <td>0.099404</td>\n      <td>0.112360</td>\n      <td>-0.029862</td>\n      <td>0.055563</td>\n      <td>-0.114713</td>\n      <td>0.217299</td>\n      <td>0.239543</td>\n      <td>-0.225016</td>\n      <td>-0.181877</td>\n      <td>0.133999</td>\n      <td>0.238567</td>\n      <td>-0.101546</td>\n      <td>0.101546</td>\n    </tr>\n    <tr>\n      <th>wheel-base</th>\n      <td>-0.535987</td>\n      <td>-0.056661</td>\n      <td>1.000000</td>\n      <td>0.876024</td>\n      <td>0.814507</td>\n      <td>0.590742</td>\n      <td>0.782097</td>\n      <td>0.572027</td>\n      <td>0.493244</td>\n      <td>0.158502</td>\n      <td>0.250313</td>\n      <td>0.371147</td>\n      <td>-0.360305</td>\n      <td>-0.470606</td>\n      <td>-0.543304</td>\n      <td>0.584642</td>\n      <td>0.476153</td>\n      <td>0.307237</td>\n      <td>-0.307237</td>\n    </tr>\n    <tr>\n      <th>length</th>\n      <td>-0.365404</td>\n      <td>0.019424</td>\n      <td>0.876024</td>\n      <td>1.000000</td>\n      <td>0.857170</td>\n      <td>0.492063</td>\n      <td>0.880665</td>\n      <td>0.685025</td>\n      <td>0.608971</td>\n      <td>0.124139</td>\n      <td>0.159733</td>\n      <td>0.579821</td>\n      <td>-0.285970</td>\n      <td>-0.665192</td>\n      <td>-0.698142</td>\n      <td>0.690628</td>\n      <td>0.657373</td>\n      <td>0.211187</td>\n      <td>-0.211187</td>\n    </tr>\n    <tr>\n      <th>width</th>\n      <td>-0.242423</td>\n      <td>0.086802</td>\n      <td>0.814507</td>\n      <td>0.857170</td>\n      <td>1.000000</td>\n      <td>0.306002</td>\n      <td>0.866201</td>\n      <td>0.729436</td>\n      <td>0.544885</td>\n      <td>0.188829</td>\n      <td>0.189867</td>\n      <td>0.615077</td>\n      <td>-0.245800</td>\n      <td>-0.633531</td>\n      <td>-0.680635</td>\n      <td>0.751265</td>\n      <td>0.673363</td>\n      <td>0.244356</td>\n      <td>-0.244356</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>-0.550160</td>\n      <td>-0.373737</td>\n      <td>0.590742</td>\n      <td>0.492063</td>\n      <td>0.306002</td>\n      <td>1.000000</td>\n      <td>0.307581</td>\n      <td>0.074694</td>\n      <td>0.180449</td>\n      <td>-0.062704</td>\n      <td>0.259737</td>\n      <td>-0.087027</td>\n      <td>-0.309974</td>\n      <td>-0.049800</td>\n      <td>-0.104812</td>\n      <td>0.135486</td>\n      <td>0.003811</td>\n      <td>0.281578</td>\n      <td>-0.281578</td>\n    </tr>\n    <tr>\n      <th>curb-weight</th>\n      <td>-0.233118</td>\n      <td>0.099404</td>\n      <td>0.782097</td>\n      <td>0.880665</td>\n      <td>0.866201</td>\n      <td>0.307581</td>\n      <td>1.000000</td>\n      <td>0.849072</td>\n      <td>0.644060</td>\n      <td>0.167562</td>\n      <td>0.156433</td>\n      <td>0.757976</td>\n      <td>-0.279361</td>\n      <td>-0.749543</td>\n      <td>-0.794889</td>\n      <td>0.834415</td>\n      <td>0.785353</td>\n      <td>0.221046</td>\n      <td>-0.221046</td>\n    </tr>\n    <tr>\n      <th>engine-size</th>\n      <td>-0.110581</td>\n      <td>0.112360</td>\n      <td>0.572027</td>\n      <td>0.685025</td>\n      <td>0.729436</td>\n      <td>0.074694</td>\n      <td>0.849072</td>\n      <td>1.000000</td>\n      <td>0.572609</td>\n      <td>0.209523</td>\n      <td>0.028889</td>\n      <td>0.822676</td>\n      <td>-0.256733</td>\n      <td>-0.650546</td>\n      <td>-0.679571</td>\n      <td>0.872335</td>\n      <td>0.745059</td>\n      <td>0.070779</td>\n      <td>-0.070779</td>\n    </tr>\n    <tr>\n      <th>bore</th>\n      <td>-0.140019</td>\n      <td>-0.029862</td>\n      <td>0.493244</td>\n      <td>0.608971</td>\n      <td>0.544885</td>\n      <td>0.180449</td>\n      <td>0.644060</td>\n      <td>0.572609</td>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n      <td>-0.267392</td>\n      <td>-0.582027</td>\n      <td>-0.591309</td>\n      <td>0.543155</td>\n      <td>0.554610</td>\n      <td>0.054458</td>\n      <td>-0.054458</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.008245</td>\n      <td>0.055563</td>\n      <td>0.158502</td>\n      <td>0.124139</td>\n      <td>0.188829</td>\n      <td>-0.062704</td>\n      <td>0.167562</td>\n      <td>0.209523</td>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n      <td>-0.065713</td>\n      <td>-0.034696</td>\n      <td>-0.035201</td>\n      <td>0.082310</td>\n      <td>0.037300</td>\n      <td>0.241303</td>\n      <td>-0.241303</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>-0.182196</td>\n      <td>-0.114713</td>\n      <td>0.250313</td>\n      <td>0.159733</td>\n      <td>0.189867</td>\n      <td>0.259737</td>\n      <td>0.156433</td>\n      <td>0.028889</td>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n      <td>-0.435780</td>\n      <td>0.331425</td>\n      <td>0.268465</td>\n      <td>0.071107</td>\n      <td>-0.299372</td>\n      <td>0.985231</td>\n      <td>-0.985231</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.075819</td>\n      <td>0.217299</td>\n      <td>0.371147</td>\n      <td>0.579821</td>\n      <td>0.615077</td>\n      <td>-0.087027</td>\n      <td>0.757976</td>\n      <td>0.822676</td>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n      <td>0.107885</td>\n      <td>-0.822214</td>\n      <td>-0.804575</td>\n      <td>0.809575</td>\n      <td>0.889488</td>\n      <td>-0.169053</td>\n      <td>0.169053</td>\n    </tr>\n    <tr>\n      <th>peak-rpm</th>\n      <td>0.279740</td>\n      <td>0.239543</td>\n      <td>-0.360305</td>\n      <td>-0.285970</td>\n      <td>-0.245800</td>\n      <td>-0.309974</td>\n      <td>-0.279361</td>\n      <td>-0.256733</td>\n      <td>-0.267392</td>\n      <td>-0.065713</td>\n      <td>-0.435780</td>\n      <td>0.107885</td>\n      <td>1.000000</td>\n      <td>-0.115413</td>\n      <td>-0.058598</td>\n      <td>-0.101616</td>\n      <td>0.115830</td>\n      <td>-0.475812</td>\n      <td>0.475812</td>\n    </tr>\n    <tr>\n      <th>city-mpg</th>\n      <td>-0.035527</td>\n      <td>-0.225016</td>\n      <td>-0.470606</td>\n      <td>-0.665192</td>\n      <td>-0.633531</td>\n      <td>-0.049800</td>\n      <td>-0.749543</td>\n      <td>-0.650546</td>\n      <td>-0.582027</td>\n      <td>-0.034696</td>\n      <td>0.331425</td>\n      <td>-0.822214</td>\n      <td>-0.115413</td>\n      <td>1.000000</td>\n      <td>0.972044</td>\n      <td>-0.686571</td>\n      <td>-0.949713</td>\n      <td>0.265676</td>\n      <td>-0.265676</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>0.036233</td>\n      <td>-0.181877</td>\n      <td>-0.543304</td>\n      <td>-0.698142</td>\n      <td>-0.680635</td>\n      <td>-0.104812</td>\n      <td>-0.794889</td>\n      <td>-0.679571</td>\n      <td>-0.591309</td>\n      <td>-0.035201</td>\n      <td>0.268465</td>\n      <td>-0.804575</td>\n      <td>-0.058598</td>\n      <td>0.972044</td>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n      <td>-0.930028</td>\n      <td>0.198690</td>\n      <td>-0.198690</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.082391</td>\n      <td>0.133999</td>\n      <td>0.584642</td>\n      <td>0.690628</td>\n      <td>0.751265</td>\n      <td>0.135486</td>\n      <td>0.834415</td>\n      <td>0.872335</td>\n      <td>0.543155</td>\n      <td>0.082310</td>\n      <td>0.071107</td>\n      <td>0.809575</td>\n      <td>-0.101616</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>0.110326</td>\n      <td>-0.110326</td>\n    </tr>\n    <tr>\n      <th>city-L/100km</th>\n      <td>0.066171</td>\n      <td>0.238567</td>\n      <td>0.476153</td>\n      <td>0.657373</td>\n      <td>0.673363</td>\n      <td>0.003811</td>\n      <td>0.785353</td>\n      <td>0.745059</td>\n      <td>0.554610</td>\n      <td>0.037300</td>\n      <td>-0.299372</td>\n      <td>0.889488</td>\n      <td>0.115830</td>\n      <td>-0.949713</td>\n      <td>-0.930028</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>-0.241282</td>\n      <td>0.241282</td>\n    </tr>\n    <tr>\n      <th>diesel</th>\n      <td>-0.196735</td>\n      <td>-0.101546</td>\n      <td>0.307237</td>\n      <td>0.211187</td>\n      <td>0.244356</td>\n      <td>0.281578</td>\n      <td>0.221046</td>\n      <td>0.070779</td>\n      <td>0.054458</td>\n      <td>0.241303</td>\n      <td>0.985231</td>\n      <td>-0.169053</td>\n      <td>-0.475812</td>\n      <td>0.265676</td>\n      <td>0.198690</td>\n      <td>0.110326</td>\n      <td>-0.241282</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n    </tr>\n    <tr>\n      <th>gas</th>\n      <td>0.196735</td>\n      <td>0.101546</td>\n      <td>-0.307237</td>\n      <td>-0.211187</td>\n      <td>-0.244356</td>\n      <td>-0.281578</td>\n      <td>-0.221046</td>\n      <td>-0.070779</td>\n      <td>-0.054458</td>\n      <td>-0.241303</td>\n      <td>-0.985231</td>\n      <td>0.169053</td>\n      <td>0.475812</td>\n      <td>-0.265676</td>\n      <td>-0.198690</td>\n      <td>-0.110326</td>\n      <td>0.241282</td>\n      <td>-1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "The diagonal elements are always one; we will study correlation more precisely Pearson correlation in-depth at the end of the notebook.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h3> Question  #2: </h3>\n\n<p>Find the correlation between the following columns: bore, stroke, compression-ratio, and horsepower.</p>\n<p>Hint: if you would like to select those columns, use the following syntax: df[['bore','stroke','compression-ratio','horsepower']]</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                       bore    stroke  compression-ratio  horsepower\nbore               1.000000 -0.055390           0.001263    0.566936\nstroke            -0.055390  1.000000           0.187923    0.098462\ncompression-ratio  0.001263  0.187923           1.000000   -0.214514\nhorsepower         0.566936  0.098462          -0.214514    1.000000",
            "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>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>bore</th>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Continuous Numerical Variables:</h2> \n\n<p>Continuous numerical variables are variables that may contain any value within some range. They can be of type \"int64\" or \"float64\". A great way to visualize these variables is by using scatterplots with fitted lines.</p>\n\n<p>In order to start understanding the (linear) relationship between an individual variable and the price, we can use \"regplot\" which plots the scatterplot plus the fitted regression line for the data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's see several examples of different linear relationships:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Positive Linear Relationship</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's find the scatterplot of \"engine-size\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # Engine size as potential predictor variable of price\n    \nsns.regplot(x=\"engine-size\", y=\"price\", data=df)\nplt.ylim(0,)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 53301.82327525413)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'engine-size' and 'price' and see that it's approximately 0.87.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[[\"engine-size\", \"price\"]].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             engine-size     price\nengine-size     1.000000  0.872335\nprice           0.872335  1.000000",
            "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>engine-size</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine-size</th>\n      <td>1.000000</td>\n      <td>0.872335</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.872335</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Highway mpg is a potential predictor variable of price. Let's find the scatterplot of \"highway-mpg\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='highway-mpg', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>As highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'highway-mpg' and 'price' and see it's approximately -0.704.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['highway-mpg', 'price']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             highway-mpg     price\nhighway-mpg     1.000000 -0.704692\nprice          -0.704692  1.000000",
            "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>highway-mpg</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>highway-mpg</th>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Weak Linear Relationship</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's see if \"peak-rpm\" is a predictor variable of \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='peak-rpm', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['peak-rpm','price']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "          peak-rpm     price\npeak-rpm  1.000000 -0.101616\nprice    -0.101616  1.000000",
            "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>peak-rpm</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>peak-rpm</th>\n      <td>1.000000</td>\n      <td>-0.101616</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.101616</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": " <div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question  3 a): </h1>\n\n<p>Find the correlation  between x=\"stroke\" and y=\"price\".</p>\n<p>Hint: if you would like to select those columns, use the following syntax: df[[\"stroke\",\"price\"]].  </p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " print('The correlation is 0.0823, the non-diagonal elements of the table')\n\ndf[[\"stroke\",\"price\"]].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "name": "stdout",
          "text": "The correlation is 0.0823, the non-diagonal elements of the table\n",
          "output_type": "stream"
        },
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         stroke    price\nstroke  1.00000  0.08231\nprice   0.08231  1.00000",
            "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>stroke</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>stroke</th>\n      <td>1.00000</td>\n      <td>0.08231</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.08231</td>\n      <td>1.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question  3 b):</h1>\n\n<p>Given the correlation results between \"price\" and \"stroke\", do you expect a linear relationship?</p> \n<p>Verify your results using the function \"regplot()\".</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # There is a weak correlation between the variable 'stroke' and 'price.' as such regression will not work well. We can see this using \"regplot\" to demonstrate this.\n\n # Code:\n\nsns.regplot(x=\"stroke\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "execution_count": 20,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='stroke', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Categorical Variables</h3>\n\n<p>These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \"object\" or \"int64\". A good way to visualize categorical variables is by using boxplots.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's look at the relationship between \"body-style\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x=\"body-style\", y=\"price\", data=df)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='body-style', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>We see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='engine-location', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's examine \"drive-wheels\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# drive-wheels\nsns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='drive-wheels', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Here we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"discriptive_statistics\">3. Descriptive Statistical Analysis</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Let's first take a look at the variables by utilizing a description method.</p>\n\n<p>The <b>describe</b> function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.</p>\n\nThis will show:\n\n<ul>\n    <li>the count of that variable</li>\n    <li>the mean</li>\n    <li>the standard deviation (std)</li> \n    <li>the minimum value</li>\n    <li>the IQR (Interquartile Range: 25%, 50% and 75%)</li>\n    <li>the maximum value</li>\n<ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can apply the method \"describe\" as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        symboling  normalized-losses  wheel-base      length       width  \\\ncount  201.000000          201.00000  201.000000  201.000000  201.000000   \nmean     0.840796          122.00000   98.797015    0.837102    0.915126   \nstd      1.254802           31.99625    6.066366    0.059213    0.029187   \nmin     -2.000000           65.00000   86.600000    0.678039    0.837500   \n25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n75%      2.000000          137.00000  102.400000    0.881788    0.925000   \nmax      3.000000          256.00000  120.900000    1.000000    1.000000   \n\n           height  curb-weight  engine-size        bore      stroke  \\\ncount  201.000000   201.000000   201.000000  201.000000  197.000000   \nmean    53.766667  2555.666667   126.875622    3.330692    3.256904   \nstd      2.447822   517.296727    41.546834    0.268072    0.319256   \nmin     47.800000  1488.000000    61.000000    2.540000    2.070000   \n25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \nmax     59.800000  4066.000000   326.000000    3.940000    4.170000   \n\n       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\ncount         201.000000  201.000000   201.000000  201.000000   201.000000   \nmean           10.164279  103.405534  5117.665368   25.179104    30.686567   \nstd             4.004965   37.365700   478.113805    6.423220     6.815150   \nmin             7.000000   48.000000  4150.000000   13.000000    16.000000   \n25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \nmax            23.000000  262.000000  6600.000000   49.000000    54.000000   \n\n              price  city-L/100km      diesel         gas  \ncount    201.000000    201.000000  201.000000  201.000000  \nmean   13207.129353      9.944145    0.099502    0.900498  \nstd     7947.066342      2.534599    0.300083    0.300083  \nmin     5118.000000      4.795918    0.000000    0.000000  \n25%     7775.000000      7.833333    0.000000    1.000000  \n50%    10295.000000      9.791667    0.000000    1.000000  \n75%    16500.000000     12.368421    0.000000    1.000000  \nmax    45400.000000     18.076923    1.000000    1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>201.000000</td>\n      <td>201.00000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>197.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.840796</td>\n      <td>122.00000</td>\n      <td>98.797015</td>\n      <td>0.837102</td>\n      <td>0.915126</td>\n      <td>53.766667</td>\n      <td>2555.666667</td>\n      <td>126.875622</td>\n      <td>3.330692</td>\n      <td>3.256904</td>\n      <td>10.164279</td>\n      <td>103.405534</td>\n      <td>5117.665368</td>\n      <td>25.179104</td>\n      <td>30.686567</td>\n      <td>13207.129353</td>\n      <td>9.944145</td>\n      <td>0.099502</td>\n      <td>0.900498</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.254802</td>\n      <td>31.99625</td>\n      <td>6.066366</td>\n      <td>0.059213</td>\n      <td>0.029187</td>\n      <td>2.447822</td>\n      <td>517.296727</td>\n      <td>41.546834</td>\n      <td>0.268072</td>\n      <td>0.319256</td>\n      <td>4.004965</td>\n      <td>37.365700</td>\n      <td>478.113805</td>\n      <td>6.423220</td>\n      <td>6.815150</td>\n      <td>7947.066342</td>\n      <td>2.534599</td>\n      <td>0.300083</td>\n      <td>0.300083</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>65.00000</td>\n      <td>86.600000</td>\n      <td>0.678039</td>\n      <td>0.837500</td>\n      <td>47.800000</td>\n      <td>1488.000000</td>\n      <td>61.000000</td>\n      <td>2.540000</td>\n      <td>2.070000</td>\n      <td>7.000000</td>\n      <td>48.000000</td>\n      <td>4150.000000</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n      <td>5118.000000</td>\n      <td>4.795918</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>101.00000</td>\n      <td>94.500000</td>\n      <td>0.801538</td>\n      <td>0.890278</td>\n      <td>52.000000</td>\n      <td>2169.000000</td>\n      <td>98.000000</td>\n      <td>3.150000</td>\n      <td>3.110000</td>\n      <td>8.600000</td>\n      <td>70.000000</td>\n      <td>4800.000000</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n      <td>7775.000000</td>\n      <td>7.833333</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>122.00000</td>\n      <td>97.000000</td>\n      <td>0.832292</td>\n      <td>0.909722</td>\n      <td>54.100000</td>\n      <td>2414.000000</td>\n      <td>120.000000</td>\n      <td>3.310000</td>\n      <td>3.290000</td>\n      <td>9.000000</td>\n      <td>95.000000</td>\n      <td>5125.369458</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n      <td>10295.000000</td>\n      <td>9.791667</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>137.00000</td>\n      <td>102.400000</td>\n      <td>0.881788</td>\n      <td>0.925000</td>\n      <td>55.500000</td>\n      <td>2926.000000</td>\n      <td>141.000000</td>\n      <td>3.580000</td>\n      <td>3.410000</td>\n      <td>9.400000</td>\n      <td>116.000000</td>\n      <td>5500.000000</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n      <td>16500.000000</td>\n      <td>12.368421</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>256.00000</td>\n      <td>120.900000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>59.800000</td>\n      <td>4066.000000</td>\n      <td>326.000000</td>\n      <td>3.940000</td>\n      <td>4.170000</td>\n      <td>23.000000</td>\n      <td>262.000000</td>\n      <td>6600.000000</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n      <td>45400.000000</td>\n      <td>18.076923</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "The default setting of \"describe\" skips variables of type object. We can apply the method \"describe\" on the variables of type 'object' as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe(include=['object'])",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "          make aspiration num-of-doors body-style drive-wheels  \\\ncount      201        201          201        201          201   \nunique      22          2            2          5            3   \ntop     toyota        std         four      sedan          fwd   \nfreq        32        165          115         94          118   \n\n       engine-location engine-type num-of-cylinders fuel-system  \\\ncount              201         201              201         201   \nunique               2           6                7           8   \ntop              front         ohc             four        mpfi   \nfreq               198         145              157          92   \n\n       horsepower-binned  \ncount                200  \nunique                 3  \ntop                  Low  \nfreq                 115  ",
            "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>make</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>engine-type</th>\n      <th>num-of-cylinders</th>\n      <th>fuel-system</th>\n      <th>horsepower-binned</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>22</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>3</td>\n      <td>2</td>\n      <td>6</td>\n      <td>7</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>toyota</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>mpfi</td>\n      <td>Low</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>32</td>\n      <td>165</td>\n      <td>115</td>\n      <td>94</td>\n      <td>118</td>\n      <td>198</td>\n      <td>145</td>\n      <td>157</td>\n      <td>92</td>\n      <td>115</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Value Counts</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Value counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \"value_counts\" method on the column \"drive-wheels\". Don’t forget the method \"value_counts\" only works on pandas series, not pandas dataframes. As a result, we only include one bracket <code>df['drive-wheels']</code>, not two brackets <code>df[['drive-wheels']]</code>.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].value_counts()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "fwd    118\nrwd     75\n4wd      8\nName: drive-wheels, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can convert the series to a dataframe as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].value_counts().to_frame()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     drive-wheels\nfwd           118\nrwd            75\n4wd             8",
            "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>drive-wheels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's repeat the above steps but save the results to the dataframe \"drive_wheels_counts\" and rename the column  'drive-wheels' to 'value_counts'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\ndrive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\ndrive_wheels_counts",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     value_counts\nfwd           118\nrwd            75\n4wd             8",
            "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>value_counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now let's rename the index to 'drive-wheels':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drive_wheels_counts.index.name = 'drive-wheels'\ndrive_wheels_counts",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "              value_counts\ndrive-wheels              \nfwd                    118\nrwd                     75\n4wd                      8",
            "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>value_counts</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can repeat the above process for the variable 'engine-location'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # engine-location as variable\n    \nengine_loc_counts = df['engine-location'].value_counts().to_frame()\nengine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\nengine_loc_counts.index.name = 'engine-location'\nengine_loc_counts.head(10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                 value_counts\nengine-location              \nfront                     198\nrear                        3",
            "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>value_counts</th>\n    </tr>\n    <tr>\n      <th>engine-location</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>front</th>\n      <td>198</td>\n    </tr>\n    <tr>\n      <th>rear</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>After examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"basic_grouping\">4. Basics of Grouping</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>The \"groupby\" method groups data by different categories. The data is grouped based on one or several variables, and analysis is performed on the individual groups.</p>\n\n<p>For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].unique()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['rwd', 'fwd', '4wd'], dtype=object)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>If we want to know, on average, which type of drive wheel is most valuable, we can group \"drive-wheels\" and then average them.</p>\n\n<p>We can select the columns 'drive-wheels', 'body-style' and 'price', then assign it to the variable \"df_group_one\".</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df_group_one = df[['drive-wheels','body-style','price']]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can then calculate the average price for each of the different categories of data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # grouping results\n    \ndf_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\ndf_group_one",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  drive-wheels         price\n0          4wd  10241.000000\n1          fwd   9244.779661\n2          rwd  19757.613333",
            "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>drive-wheels</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4wd</td>\n      <td>10241.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>fwd</td>\n      <td>9244.779661</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rwd</td>\n      <td>19757.613333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>From our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.</p>\n\n<p>You can also group by multiple variables. For example, let's group by both 'drive-wheels' and 'body-style'. This groups the dataframe by the unique combination of 'drive-wheels' and 'body-style'. We can store the results in the variable 'grouped_test1'.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # grouping results\n    \ndf_gptest = df[['drive-wheels','body-style','price']]\ngrouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\ngrouped_test1",
      "metadata": {
        "trusted": true
      },
      "execution_count": 34,
      "outputs": [
        {
          "execution_count": 34,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   drive-wheels   body-style         price\n0           4wd    hatchback   7603.000000\n1           4wd        sedan  12647.333333\n2           4wd        wagon   9095.750000\n3           fwd  convertible  11595.000000\n4           fwd      hardtop   8249.000000\n5           fwd    hatchback   8396.387755\n6           fwd        sedan   9811.800000\n7           fwd        wagon   9997.333333\n8           rwd  convertible  23949.600000\n9           rwd      hardtop  24202.714286\n10          rwd    hatchback  14337.777778\n11          rwd        sedan  21711.833333\n12          rwd        wagon  16994.222222",
            "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>drive-wheels</th>\n      <th>body-style</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4wd</td>\n      <td>hatchback</td>\n      <td>7603.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4wd</td>\n      <td>sedan</td>\n      <td>12647.333333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4wd</td>\n      <td>wagon</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>convertible</td>\n      <td>11595.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>fwd</td>\n      <td>hardtop</td>\n      <td>8249.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>fwd</td>\n      <td>hatchback</td>\n      <td>8396.387755</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>fwd</td>\n      <td>sedan</td>\n      <td>9811.800000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>fwd</td>\n      <td>wagon</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>23949.600000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>rwd</td>\n      <td>hardtop</td>\n      <td>24202.714286</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>rwd</td>\n      <td>hatchback</td>\n      <td>14337.777778</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>21711.833333</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>rwd</td>\n      <td>wagon</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>This grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \"pivot\" to create a pivot table from the groups.</p>\n\n<p>In this case, we will leave the drive-wheels variable as the rows of the table, and pivot body-style to become the columns of the table:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\ngrouped_pivot",
      "metadata": {
        "trusted": true
      },
      "execution_count": 35,
      "outputs": [
        {
          "execution_count": 35,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   price                                            \\\nbody-style   convertible       hardtop     hatchback         sedan   \ndrive-wheels                                                         \n4wd                  NaN           NaN   7603.000000  12647.333333   \nfwd              11595.0   8249.000000   8396.387755   9811.800000   \nrwd              23949.6  24202.714286  14337.777778  21711.833333   \n\n                            \nbody-style           wagon  \ndrive-wheels                \n4wd            9095.750000  \nfwd            9997.333333  \nrwd           16994.222222  ",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">price</th>\n    </tr>\n    <tr>\n      <th>body-style</th>\n      <th>convertible</th>\n      <th>hardtop</th>\n      <th>hatchback</th>\n      <th>sedan</th>\n      <th>wagon</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</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>4wd</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7603.000000</td>\n      <td>12647.333333</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>fwd</th>\n      <td>11595.0</td>\n      <td>8249.000000</td>\n      <td>8396.387755</td>\n      <td>9811.800000</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>23949.6</td>\n      <td>24202.714286</td>\n      <td>14337.777778</td>\n      <td>21711.833333</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Often, we won't have data for some of the pivot cells. We can fill these missing cells with the value 0, but any other value could potentially be used as well. It should be mentioned that missing data is quite a complex subject and is an entire course on its own.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # fill missing values with 0\n\ngrouped_pivot = grouped_pivot.fillna(0) \ngrouped_pivot",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   price                                            \\\nbody-style   convertible       hardtop     hatchback         sedan   \ndrive-wheels                                                         \n4wd                  0.0      0.000000   7603.000000  12647.333333   \nfwd              11595.0   8249.000000   8396.387755   9811.800000   \nrwd              23949.6  24202.714286  14337.777778  21711.833333   \n\n                            \nbody-style           wagon  \ndrive-wheels                \n4wd            9095.750000  \nfwd            9997.333333  \nrwd           16994.222222  ",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">price</th>\n    </tr>\n    <tr>\n      <th>body-style</th>\n      <th>convertible</th>\n      <th>hardtop</th>\n      <th>hatchback</th>\n      <th>sedan</th>\n      <th>wagon</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</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>4wd</th>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>7603.000000</td>\n      <td>12647.333333</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>fwd</th>\n      <td>11595.0</td>\n      <td>8249.000000</td>\n      <td>8396.387755</td>\n      <td>9811.800000</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>23949.6</td>\n      <td>24202.714286</td>\n      <td>14337.777778</td>\n      <td>21711.833333</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question 4:</h1>\n\n<p>Use the \"groupby\" function to find the average \"price\" of each car based on \"body-style\".</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # grouping results\n\ndf_gptest2 = df[['body-style','price']]\ngrouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\ngrouped_test_bodystyle\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 37,
      "outputs": [
        {
          "execution_count": 37,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    body-style         price\n0  convertible  21890.500000\n1      hardtop  22208.500000\n2    hatchback   9957.441176\n3        sedan  14459.755319\n4        wagon  12371.960000",
            "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>body-style</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>convertible</td>\n      <td>21890.500000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>hardtop</td>\n      <td>22208.500000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>hatchback</td>\n      <td>9957.441176</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>sedan</td>\n      <td>14459.755319</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wagon</td>\n      <td>12371.960000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "If you did not import \"pyplot\", let's do it again.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Variables: Drive Wheels and Body Style vs. Price</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's use a heat map to visualize the relationship between Body Style vs Price.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": " # use the grouped results\n    \nplt.pcolor(grouped_pivot, cmap='RdBu')\nplt.colorbar()\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5912290>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.</p>\n\n<p>The default labels convey no useful information to us. Let's change that:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "fig, ax = plt.subplots()\nim = ax.pcolor(grouped_pivot, cmap='RdBu')\n\n # label names\n    \nrow_labels = grouped_pivot.columns.levels[1]\ncol_labels = grouped_pivot.index\n\n # move ticks and labels to the center\n    \nax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\nax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n\n # insert labels\n    \nax.set_xticklabels(row_labels, minor=False)\nax.set_yticklabels(col_labels, minor=False)\n\n # rotate label if too long\n    \nplt.xticks(rotation=90)\n\nfig.colorbar(im)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5f93a88>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Visualization is very important in data science, and Python visualization packages provide great freedom. We will go more in-depth in a separate Python visualizations course.</p>\n\n<p>The main question we want to answer in this module is, \"What are the main characteristics which have the most impact on the car price?\".</p>\n\n<p>To get a better measure of the important characteristics, we look at the correlation of these variables with the car price. In other words: how is the car price dependent on this variable?</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"correlation_causation\">5. Correlation and Causation</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p><b>Correlation</b>: a measure of the extent of interdependence between variables.</p>\n\n<p><b>Causation</b>: the relationship between cause and effect between two variables.</p>\n\n<p>It is important to know the difference between these two. Correlation does not imply causation. Determining correlation is much simpler  the determining causation as causation may require independent experimentation.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p><b>Pearson Correlation</b></p>\n<p>The Pearson Correlation measures the linear dependence between two variables X and Y.</p>\n<p>The resulting coefficient is a value between -1 and 1 inclusive, where:</p>\n<ul>\n    <li><b>1</b>: Perfect positive linear correlation.</li>\n    <li><b>0</b>: No linear correlation, the two variables most likely do not affect each other.</li>\n    <li><b>-1</b>: Perfect negative linear correlation.</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Pearson Correlation is the default method of the function \"corr\". Like before, we can calculate the Pearson Correlation of the of the 'int64' or 'float64'  variables.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "execution_count": 41,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   symboling  normalized-losses  wheel-base    length  \\\nsymboling           1.000000           0.466264   -0.535987 -0.365404   \nnormalized-losses   0.466264           1.000000   -0.056661  0.019424   \nwheel-base         -0.535987          -0.056661    1.000000  0.876024   \nlength             -0.365404           0.019424    0.876024  1.000000   \nwidth              -0.242423           0.086802    0.814507  0.857170   \nheight             -0.550160          -0.373737    0.590742  0.492063   \ncurb-weight        -0.233118           0.099404    0.782097  0.880665   \nengine-size        -0.110581           0.112360    0.572027  0.685025   \nbore               -0.140019          -0.029862    0.493244  0.608971   \nstroke             -0.008245           0.055563    0.158502  0.124139   \ncompression-ratio  -0.182196          -0.114713    0.250313  0.159733   \nhorsepower          0.075819           0.217299    0.371147  0.579821   \npeak-rpm            0.279740           0.239543   -0.360305 -0.285970   \ncity-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \nhighway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \nprice              -0.082391           0.133999    0.584642  0.690628   \ncity-L/100km        0.066171           0.238567    0.476153  0.657373   \ndiesel             -0.196735          -0.101546    0.307237  0.211187   \ngas                 0.196735           0.101546   -0.307237 -0.211187   \n\n                      width    height  curb-weight  engine-size      bore  \\\nsymboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \nnormalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \nwheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \nlength             0.857170  0.492063     0.880665     0.685025  0.608971   \nwidth              1.000000  0.306002     0.866201     0.729436  0.544885   \nheight             0.306002  1.000000     0.307581     0.074694  0.180449   \ncurb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \nengine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \nbore               0.544885  0.180449     0.644060     0.572609  1.000000   \nstroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \ncompression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \nhorsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \npeak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \ncity-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \nhighway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \nprice              0.751265  0.135486     0.834415     0.872335  0.543155   \ncity-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \ndiesel             0.244356  0.281578     0.221046     0.070779  0.054458   \ngas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n\n                     stroke  compression-ratio  horsepower  peak-rpm  \\\nsymboling         -0.008245          -0.182196    0.075819  0.279740   \nnormalized-losses  0.055563          -0.114713    0.217299  0.239543   \nwheel-base         0.158502           0.250313    0.371147 -0.360305   \nlength             0.124139           0.159733    0.579821 -0.285970   \nwidth              0.188829           0.189867    0.615077 -0.245800   \nheight            -0.062704           0.259737   -0.087027 -0.309974   \ncurb-weight        0.167562           0.156433    0.757976 -0.279361   \nengine-size        0.209523           0.028889    0.822676 -0.256733   \nbore              -0.055390           0.001263    0.566936 -0.267392   \nstroke             1.000000           0.187923    0.098462 -0.065713   \ncompression-ratio  0.187923           1.000000   -0.214514 -0.435780   \nhorsepower         0.098462          -0.214514    1.000000  0.107885   \npeak-rpm          -0.065713          -0.435780    0.107885  1.000000   \ncity-mpg          -0.034696           0.331425   -0.822214 -0.115413   \nhighway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \nprice              0.082310           0.071107    0.809575 -0.101616   \ncity-L/100km       0.037300          -0.299372    0.889488  0.115830   \ndiesel             0.241303           0.985231   -0.169053 -0.475812   \ngas               -0.241303          -0.985231    0.169053  0.475812   \n\n                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\nsymboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \nnormalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \nwheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \nlength            -0.665192    -0.698142  0.690628      0.657373  0.211187   \nwidth             -0.633531    -0.680635  0.751265      0.673363  0.244356   \nheight            -0.049800    -0.104812  0.135486      0.003811  0.281578   \ncurb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \nengine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \nbore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \nstroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \ncompression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \nhorsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \npeak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \ncity-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \nhighway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \nprice             -0.686571    -0.704692  1.000000      0.789898  0.110326   \ncity-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \ndiesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \ngas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n\n                        gas  \nsymboling          0.196735  \nnormalized-losses  0.101546  \nwheel-base        -0.307237  \nlength            -0.211187  \nwidth             -0.244356  \nheight            -0.281578  \ncurb-weight       -0.221046  \nengine-size       -0.070779  \nbore              -0.054458  \nstroke            -0.241303  \ncompression-ratio -0.985231  \nhorsepower         0.169053  \npeak-rpm           0.475812  \ncity-mpg          -0.265676  \nhighway-mpg       -0.198690  \nprice             -0.110326  \ncity-L/100km       0.241282  \ndiesel            -1.000000  \ngas                1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>symboling</th>\n      <td>1.000000</td>\n      <td>0.466264</td>\n      <td>-0.535987</td>\n      <td>-0.365404</td>\n      <td>-0.242423</td>\n      <td>-0.550160</td>\n      <td>-0.233118</td>\n      <td>-0.110581</td>\n      <td>-0.140019</td>\n      <td>-0.008245</td>\n      <td>-0.182196</td>\n      <td>0.075819</td>\n      <td>0.279740</td>\n      <td>-0.035527</td>\n      <td>0.036233</td>\n      <td>-0.082391</td>\n      <td>0.066171</td>\n      <td>-0.196735</td>\n      <td>0.196735</td>\n    </tr>\n    <tr>\n      <th>normalized-losses</th>\n      <td>0.466264</td>\n      <td>1.000000</td>\n      <td>-0.056661</td>\n      <td>0.019424</td>\n      <td>0.086802</td>\n      <td>-0.373737</td>\n      <td>0.099404</td>\n      <td>0.112360</td>\n      <td>-0.029862</td>\n      <td>0.055563</td>\n      <td>-0.114713</td>\n      <td>0.217299</td>\n      <td>0.239543</td>\n      <td>-0.225016</td>\n      <td>-0.181877</td>\n      <td>0.133999</td>\n      <td>0.238567</td>\n      <td>-0.101546</td>\n      <td>0.101546</td>\n    </tr>\n    <tr>\n      <th>wheel-base</th>\n      <td>-0.535987</td>\n      <td>-0.056661</td>\n      <td>1.000000</td>\n      <td>0.876024</td>\n      <td>0.814507</td>\n      <td>0.590742</td>\n      <td>0.782097</td>\n      <td>0.572027</td>\n      <td>0.493244</td>\n      <td>0.158502</td>\n      <td>0.250313</td>\n      <td>0.371147</td>\n      <td>-0.360305</td>\n      <td>-0.470606</td>\n      <td>-0.543304</td>\n      <td>0.584642</td>\n      <td>0.476153</td>\n      <td>0.307237</td>\n      <td>-0.307237</td>\n    </tr>\n    <tr>\n      <th>length</th>\n      <td>-0.365404</td>\n      <td>0.019424</td>\n      <td>0.876024</td>\n      <td>1.000000</td>\n      <td>0.857170</td>\n      <td>0.492063</td>\n      <td>0.880665</td>\n      <td>0.685025</td>\n      <td>0.608971</td>\n      <td>0.124139</td>\n      <td>0.159733</td>\n      <td>0.579821</td>\n      <td>-0.285970</td>\n      <td>-0.665192</td>\n      <td>-0.698142</td>\n      <td>0.690628</td>\n      <td>0.657373</td>\n      <td>0.211187</td>\n      <td>-0.211187</td>\n    </tr>\n    <tr>\n      <th>width</th>\n      <td>-0.242423</td>\n      <td>0.086802</td>\n      <td>0.814507</td>\n      <td>0.857170</td>\n      <td>1.000000</td>\n      <td>0.306002</td>\n      <td>0.866201</td>\n      <td>0.729436</td>\n      <td>0.544885</td>\n      <td>0.188829</td>\n      <td>0.189867</td>\n      <td>0.615077</td>\n      <td>-0.245800</td>\n      <td>-0.633531</td>\n      <td>-0.680635</td>\n      <td>0.751265</td>\n      <td>0.673363</td>\n      <td>0.244356</td>\n      <td>-0.244356</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>-0.550160</td>\n      <td>-0.373737</td>\n      <td>0.590742</td>\n      <td>0.492063</td>\n      <td>0.306002</td>\n      <td>1.000000</td>\n      <td>0.307581</td>\n      <td>0.074694</td>\n      <td>0.180449</td>\n      <td>-0.062704</td>\n      <td>0.259737</td>\n      <td>-0.087027</td>\n      <td>-0.309974</td>\n      <td>-0.049800</td>\n      <td>-0.104812</td>\n      <td>0.135486</td>\n      <td>0.003811</td>\n      <td>0.281578</td>\n      <td>-0.281578</td>\n    </tr>\n    <tr>\n      <th>curb-weight</th>\n      <td>-0.233118</td>\n      <td>0.099404</td>\n      <td>0.782097</td>\n      <td>0.880665</td>\n      <td>0.866201</td>\n      <td>0.307581</td>\n      <td>1.000000</td>\n      <td>0.849072</td>\n      <td>0.644060</td>\n      <td>0.167562</td>\n      <td>0.156433</td>\n      <td>0.757976</td>\n      <td>-0.279361</td>\n      <td>-0.749543</td>\n      <td>-0.794889</td>\n      <td>0.834415</td>\n      <td>0.785353</td>\n      <td>0.221046</td>\n      <td>-0.221046</td>\n    </tr>\n    <tr>\n      <th>engine-size</th>\n      <td>-0.110581</td>\n      <td>0.112360</td>\n      <td>0.572027</td>\n      <td>0.685025</td>\n      <td>0.729436</td>\n      <td>0.074694</td>\n      <td>0.849072</td>\n      <td>1.000000</td>\n      <td>0.572609</td>\n      <td>0.209523</td>\n      <td>0.028889</td>\n      <td>0.822676</td>\n      <td>-0.256733</td>\n      <td>-0.650546</td>\n      <td>-0.679571</td>\n      <td>0.872335</td>\n      <td>0.745059</td>\n      <td>0.070779</td>\n      <td>-0.070779</td>\n    </tr>\n    <tr>\n      <th>bore</th>\n      <td>-0.140019</td>\n      <td>-0.029862</td>\n      <td>0.493244</td>\n      <td>0.608971</td>\n      <td>0.544885</td>\n      <td>0.180449</td>\n      <td>0.644060</td>\n      <td>0.572609</td>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n      <td>-0.267392</td>\n      <td>-0.582027</td>\n      <td>-0.591309</td>\n      <td>0.543155</td>\n      <td>0.554610</td>\n      <td>0.054458</td>\n      <td>-0.054458</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.008245</td>\n      <td>0.055563</td>\n      <td>0.158502</td>\n      <td>0.124139</td>\n      <td>0.188829</td>\n      <td>-0.062704</td>\n      <td>0.167562</td>\n      <td>0.209523</td>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n      <td>-0.065713</td>\n      <td>-0.034696</td>\n      <td>-0.035201</td>\n      <td>0.082310</td>\n      <td>0.037300</td>\n      <td>0.241303</td>\n      <td>-0.241303</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>-0.182196</td>\n      <td>-0.114713</td>\n      <td>0.250313</td>\n      <td>0.159733</td>\n      <td>0.189867</td>\n      <td>0.259737</td>\n      <td>0.156433</td>\n      <td>0.028889</td>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n      <td>-0.435780</td>\n      <td>0.331425</td>\n      <td>0.268465</td>\n      <td>0.071107</td>\n      <td>-0.299372</td>\n      <td>0.985231</td>\n      <td>-0.985231</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.075819</td>\n      <td>0.217299</td>\n      <td>0.371147</td>\n      <td>0.579821</td>\n      <td>0.615077</td>\n      <td>-0.087027</td>\n      <td>0.757976</td>\n      <td>0.822676</td>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n      <td>0.107885</td>\n      <td>-0.822214</td>\n      <td>-0.804575</td>\n      <td>0.809575</td>\n      <td>0.889488</td>\n      <td>-0.169053</td>\n      <td>0.169053</td>\n    </tr>\n    <tr>\n      <th>peak-rpm</th>\n      <td>0.279740</td>\n      <td>0.239543</td>\n      <td>-0.360305</td>\n      <td>-0.285970</td>\n      <td>-0.245800</td>\n      <td>-0.309974</td>\n      <td>-0.279361</td>\n      <td>-0.256733</td>\n      <td>-0.267392</td>\n      <td>-0.065713</td>\n      <td>-0.435780</td>\n      <td>0.107885</td>\n      <td>1.000000</td>\n      <td>-0.115413</td>\n      <td>-0.058598</td>\n      <td>-0.101616</td>\n      <td>0.115830</td>\n      <td>-0.475812</td>\n      <td>0.475812</td>\n    </tr>\n    <tr>\n      <th>city-mpg</th>\n      <td>-0.035527</td>\n      <td>-0.225016</td>\n      <td>-0.470606</td>\n      <td>-0.665192</td>\n      <td>-0.633531</td>\n      <td>-0.049800</td>\n      <td>-0.749543</td>\n      <td>-0.650546</td>\n      <td>-0.582027</td>\n      <td>-0.034696</td>\n      <td>0.331425</td>\n      <td>-0.822214</td>\n      <td>-0.115413</td>\n      <td>1.000000</td>\n      <td>0.972044</td>\n      <td>-0.686571</td>\n      <td>-0.949713</td>\n      <td>0.265676</td>\n      <td>-0.265676</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>0.036233</td>\n      <td>-0.181877</td>\n      <td>-0.543304</td>\n      <td>-0.698142</td>\n      <td>-0.680635</td>\n      <td>-0.104812</td>\n      <td>-0.794889</td>\n      <td>-0.679571</td>\n      <td>-0.591309</td>\n      <td>-0.035201</td>\n      <td>0.268465</td>\n      <td>-0.804575</td>\n      <td>-0.058598</td>\n      <td>0.972044</td>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n      <td>-0.930028</td>\n      <td>0.198690</td>\n      <td>-0.198690</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.082391</td>\n      <td>0.133999</td>\n      <td>0.584642</td>\n      <td>0.690628</td>\n      <td>0.751265</td>\n      <td>0.135486</td>\n      <td>0.834415</td>\n      <td>0.872335</td>\n      <td>0.543155</td>\n      <td>0.082310</td>\n      <td>0.071107</td>\n      <td>0.809575</td>\n      <td>-0.101616</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>0.110326</td>\n      <td>-0.110326</td>\n    </tr>\n    <tr>\n      <th>city-L/100km</th>\n      <td>0.066171</td>\n      <td>0.238567</td>\n      <td>0.476153</td>\n      <td>0.657373</td>\n      <td>0.673363</td>\n      <td>0.003811</td>\n      <td>0.785353</td>\n      <td>0.745059</td>\n      <td>0.554610</td>\n      <td>0.037300</td>\n      <td>-0.299372</td>\n      <td>0.889488</td>\n      <td>0.115830</td>\n      <td>-0.949713</td>\n      <td>-0.930028</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>-0.241282</td>\n      <td>0.241282</td>\n    </tr>\n    <tr>\n      <th>diesel</th>\n      <td>-0.196735</td>\n      <td>-0.101546</td>\n      <td>0.307237</td>\n      <td>0.211187</td>\n      <td>0.244356</td>\n      <td>0.281578</td>\n      <td>0.221046</td>\n      <td>0.070779</td>\n      <td>0.054458</td>\n      <td>0.241303</td>\n      <td>0.985231</td>\n      <td>-0.169053</td>\n      <td>-0.475812</td>\n      <td>0.265676</td>\n      <td>0.198690</td>\n      <td>0.110326</td>\n      <td>-0.241282</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n    </tr>\n    <tr>\n      <th>gas</th>\n      <td>0.196735</td>\n      <td>0.101546</td>\n      <td>-0.307237</td>\n      <td>-0.211187</td>\n      <td>-0.244356</td>\n      <td>-0.281578</td>\n      <td>-0.221046</td>\n      <td>-0.070779</td>\n      <td>-0.054458</td>\n      <td>-0.241303</td>\n      <td>-0.985231</td>\n      <td>0.169053</td>\n      <td>0.475812</td>\n      <td>-0.265676</td>\n      <td>-0.198690</td>\n      <td>-0.110326</td>\n      <td>0.241282</td>\n      <td>-1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Sometimes we would like to know the significant of the correlation estimate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<b>P-value</b>\n\n<p>What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.</p>\n\nBy convention, when the\n\n<ul>\n    <li>p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.</li>\n    <li>the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.</li>\n    <li>the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.</li>\n    <li>the p-value is $>$ 0.1: there is no evidence that the correlation is significant.</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can obtain this information using  \"stats\" module in the \"scipy\"  library.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from scipy import stats",
      "metadata": {
        "trusted": true
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Wheel-Base vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.5846418222655085  with a P-value of P = 8.076488270732243e-20\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Horsepower vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 44,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8095745670036559  with a P-value of P =  6.369057428260101e-48\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n\n<p>Since the p-value is $<$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Length vs. Price</h3>\n\nLet's calculate the  Pearson Correlation Coefficient and P-value of 'length' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 45,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.6906283804483643  with a P-value of P =  8.01647746615853e-30\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Width vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 46,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.7512653440522666  with a P-value of P = 9.200335510483739e-38\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Conclusion:\n\nSince the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (\\~0.751).\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Curb-Weight vs. Price\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\n\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 47,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8344145257702845  with a P-value of P =  2.189577238893816e-53\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Engine-Size vs. Price</h3>\n\nLet's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 48,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8723351674455188  with a P-value of P = 9.265491622196808e-64\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n\n<p>Since the p-value is $<$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Bore vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'bore' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =  \", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 49,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.54315538326266  with a P-value of P =   8.049189483935489e-17\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can relate the process for each 'city-mpg'  and 'highway-mpg':\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>City-mpg vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\n\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 50,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is -0.6865710067844684  with a P-value of P =  2.3211320655672453e-29\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Highway-mpg vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\n\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 51,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is -0.7046922650589534  with a P-value of P =  1.749547114447437e-31\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Conclusion:\n\nSince the p-value is < 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"anova\">6. ANOVA</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>ANOVA: Analysis of Variance</h3>\n<p>The Analysis of Variance  (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:</p>\n\n<p><b>F-test score</b>: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.</p>\n\n<p><b>P-value</b>:  P-value tells how statistically significant our calculated score value is.</p>\n\n<p>If our price variable is strongly correlated with the variable we are analyzing, we expect ANOVA to return a sizeable F-test score and a small p-value.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Drive Wheels</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.</p>\n\n<p>To see if different types of 'drive-wheels' impact  'price', we group the data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_test2=df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\ngrouped_test2.head(2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 52,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    drive-wheels    price\n0            rwd  13495.0\n1            rwd  16500.0\n3            fwd  13950.0\n4            4wd  17450.0\n5            fwd  15250.0\n136          4wd   7603.0",
            "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>drive-wheels</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>rwd</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rwd</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4wd</td>\n      <td>17450.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>fwd</td>\n      <td>15250.0</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>4wd</td>\n      <td>7603.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "df_gptest",
      "metadata": {
        "trusted": true
      },
      "execution_count": 53,
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    drive-wheels   body-style    price\n0            rwd  convertible  13495.0\n1            rwd  convertible  16500.0\n2            rwd    hatchback  16500.0\n3            fwd        sedan  13950.0\n4            4wd        sedan  17450.0\n..           ...          ...      ...\n196          rwd        sedan  16845.0\n197          rwd        sedan  19045.0\n198          rwd        sedan  21485.0\n199          rwd        sedan  22470.0\n200          rwd        sedan  22625.0\n\n[201 rows x 3 columns]",
            "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>drive-wheels</th>\n      <th>body-style</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rwd</td>\n      <td>hatchback</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>sedan</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4wd</td>\n      <td>sedan</td>\n      <td>17450.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>16845.0</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>19045.0</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>21485.0</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>22470.0</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>22625.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can obtain the values of the method group using the method \"get_group\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_test2.get_group('4wd')['price']",
      "metadata": {
        "trusted": true
      },
      "execution_count": 54,
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "4      17450.0\n136     7603.0\n140     9233.0\n141    11259.0\n144     8013.0\n145    11694.0\n150     7898.0\n151     8778.0\nName: price, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can use the function 'f_oneway' in the module 'stats' to obtain the <b>F-test score</b> and <b>P-value</b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# ANOVA\nf_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price'])  \n \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 55,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "This is a great result with a large F-test score showing a strong correlation and a P-value of almost 0 implying almost certain statistical significance. But does this mean all three tested groups are all this highly correlated?\n\nLet's examine them separately.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### fwd and rwd\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'])  \n \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val )",
      "metadata": {
        "trusted": true
      },
      "execution_count": 56,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 130.5533160959111 , P = 2.2355306355677845e-23\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's examine the other groups.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### 4wd and rwd\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price'])  \n   \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 8.580681368924756 , P = 0.004411492211225333\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>4wd and fwd</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('fwd')['price'])  \n \nprint(\"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 58,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 0.665465750252303 , P = 0.41620116697845655\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Conclusion: Important Variables</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:</p>\n\nContinuous numerical variables:\n\n<ul>\n    <li>Length</li>\n    <li>Width</li>\n    <li>Curb-weight</li>\n    <li>Engine-size</li>\n    <li>Horsepower</li>\n    <li>City-mpg</li>\n    <li>Highway-mpg</li>\n    <li>Wheel-base</li>\n    <li>Bore</li>\n</ul>\n\nCategorical variables:\n\n<ul>\n    <li>Drive-wheels</li>\n</ul>\n\n<p>As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Mahdi Noorian PhD</a>\n\nBahare Talayian\n\nEric Xiao\n\nSteven Dong\n\nParizad\n\nHima Vasudevan\n\n<a href=\"https://www.linkedin.com/in/fiorellawever/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Fiorella Wenver</a>\n\n<a href=\"https:// https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" >Yi Yao</a>.\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n| ----------------- | ------- | ---------- | ---------------------------------- |\n| 2020-10-30        | 2.1     | Lakshmi    | changed URL of csv                 |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    }
  ]
}