{"id":5,"date":"2020-05-07T14:06:24","date_gmt":"2020-05-07T14:06:24","guid":{"rendered":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/2020\/05\/07\/chapter-1\/"},"modified":"2023-01-20T16:17:19","modified_gmt":"2023-01-20T16:17:19","slug":"lecture-1","status":"publish","type":"chapter","link":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/chapter\/lecture-1\/","title":{"rendered":"Lecture 1"},"content":{"raw":"[nbconvert url=\"https:\/\/github.com\/Jero2760\/nbconvert\/blob\/master\/Python%20for%20DS\/lecture1.ipynb\"]\r\n<h2>Questions:<\/h2>\r\n[h5p id=\"1\"]","rendered":"<div class=\"notebook\">\n<div class=\"nbconvert-labels\">\n      <label class=\"github-link\"><br \/>\n        <a href=\"https:\/\/github.com\/Jero2760\/nbconvert\/blob\/master\/Python%20for%20DS\/lecture1.ipynb\" target=\"_blank\">Check it out on github<\/a><br \/>\n        <label class=\"github-last-update\"> Last updated: 13\/01\/2021 17:14:07<\/label><br \/>\n      <\/label>\n      <\/div>\n<div class=\"nbconvert\">\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><a href=\"https:\/\/colab.research.google.com\/github\/Tanu-N-Prabhu\/Python\/blob\/master\/Exploratory_data_Analysis.ipynb\" target=\"_parent\"><img decoding=\"async\" src=\"https:\/\/colab.research.google.com\/assets\/colab-badge.svg\" alt=\"Open In Colab\"><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h1 id=\"Exploratory-data-analysis-in-Python,-by-Tanu-N-Prabhu,-2019.\">Exploratory data analysis in Python, by Tanu N Prabhu, 2019.<a class=\"anchor-link\" href=\"#Exploratory-data-analysis-in-Python,-by-Tanu-N-Prabhu,-2019.\">\u00b6<\/a><\/h1>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"Let-us-understand-how-to-explore-the-data-in-python.\">Let us understand how to explore the data in python.<a class=\"anchor-link\" href=\"#Let-us-understand-how-to-explore-the-data-in-python.\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><img decoding=\"async\" src=\"https:\/\/moriohcdn.b-cdn.net\/ff3cc511fb.png\" alt=\"alt text\"><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Image Credits: Morioh<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"Introduction\">Introduction<a class=\"anchor-link\" href=\"#Introduction\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><strong>What is Exploratory Data Analysis ?<\/strong><\/p>\n<p>Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually. This step is very important especially when we arrive at modeling the data in order to apply Machine learning. Plotting in EDA consists of Histograms, Box plot, Scatter plot and many more. It often takes much time to explore the data. Through the process of EDA, we can ask to define the problem statement or definition on our data set which is very important.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><strong>How to perform Exploratory Data Analysis ?<\/strong><\/p>\n<p>This is one such question that everyone is keen on knowing the answer. Well, the answer is it depends on the data set that you are working. There is no one method or common methods in order to perform EDA, whereas in this tutorial you can understand some common methods and plots that would be used in the EDA process.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><strong>What data are we exploring today ?<\/strong><\/p>\n<p>Since I am a huge fan of cars, I got a very beautiful data-set of cars from Kaggle. The data-set can be downloaded from <a href=\"https:\/\/www.kaggle.com\/CooperUnion\/cardataset\">here<\/a>. To give a piece of brief information about the data set this data contains more of 10, 000 rows and more than 10 columns which contains features of the car such as Engine Fuel Type, Engine HP, Transmission Type, highway MPG, city MPG and many more. So in this tutorial, we will explore the data and make it ready for modeling.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"1.-Importing-the-required-libraries-for-EDA\">1. Importing the required libraries for EDA<a class=\"anchor-link\" href=\"#1.-Importing-the-required-libraries-for-EDA\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Below are the libraries that are used in order to perform EDA (Exploratory data analysis) in this tutorial.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[0]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">pd<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">numpy<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">np<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">seaborn<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">sns<\/span>                       <span class=\"c1\">#visualisation<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">plt<\/span>             <span class=\"c1\">#visualisation<\/span>\n<span class=\"o\">%<\/span><span class=\"k\">matplotlib<\/span> inline     \n<span class=\"n\">sns<\/span><span class=\"o\">.<\/span><span class=\"n\">set<\/span><span class=\"p\">(<\/span><span class=\"n\">color_codes<\/span><span class=\"o\">=<\/span><span class=\"kc\">True<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"2.-Loading-the-data-into-the-data-frame.\">2. Loading the data into the data frame.<a class=\"anchor-link\" href=\"#2.-Loading-the-data-into-the-data-frame.\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Loading the data into the pandas data frame is certainly one of the most important steps in EDA, as we can see that the value from the data set is comma-separated. So all we have to do is to just read the CSV into a data frame and pandas data frame does the job for us.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>To get or load the dataset into the notebook, all I did was one trivial step. In Google Colab at the left-hand side of the notebook, you will find a &gt; (greater than symbol). When you click that you will find a tab with three options, you just have to select Files. Then you can easily upload your file with the help of the Upload option. No need to mount to the google drive or use any specific libraries just upload the data set and your job is done. One thing to remember in this step is that uploaded files will get deleted when this runtime is recycled. This is how I got the data set into the notebook.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[2]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"o\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"data.csv\"<\/span><span class=\"p\">)<\/span>\n<span class=\"c1\"># To display the top 5 rows <\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>               \n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[2]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Model<\/th>\n<th>Year<\/th>\n<th>Engine Fuel Type<\/th>\n<th>Engine HP<\/th>\n<th>Engine Cylinders<\/th>\n<th>Transmission Type<\/th>\n<th>Driven_Wheels<\/th>\n<th>Number of Doors<\/th>\n<th>Market Category<\/th>\n<th>Vehicle Size<\/th>\n<th>Vehicle Style<\/th>\n<th>highway MPG<\/th>\n<th>city mpg<\/th>\n<th>Popularity<\/th>\n<th>MSRP<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>BMW<\/td>\n<td>1 Series M<\/td>\n<td>2011<\/td>\n<td>premium unleaded (required)<\/td>\n<td>335.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>2.0<\/td>\n<td>Factory Tuner,Luxury,High-Performance<\/td>\n<td>Compact<\/td>\n<td>Coupe<\/td>\n<td>26<\/td>\n<td>19<\/td>\n<td>3916<\/td>\n<td>46135<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>premium unleaded (required)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>2.0<\/td>\n<td>Luxury,Performance<\/td>\n<td>Compact<\/td>\n<td>Convertible<\/td>\n<td>28<\/td>\n<td>19<\/td>\n<td>3916<\/td>\n<td>40650<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>premium unleaded (required)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>2.0<\/td>\n<td>Luxury,High-Performance<\/td>\n<td>Compact<\/td>\n<td>Coupe<\/td>\n<td>28<\/td>\n<td>20<\/td>\n<td>3916<\/td>\n<td>36350<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>premium unleaded (required)<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>2.0<\/td>\n<td>Luxury,Performance<\/td>\n<td>Compact<\/td>\n<td>Coupe<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>3916<\/td>\n<td>29450<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>premium unleaded (required)<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>2.0<\/td>\n<td>Luxury<\/td>\n<td>Compact<\/td>\n<td>Convertible<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>3916<\/td>\n<td>34500<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[3]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">tail<\/span><span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>                        <span class=\"c1\"># To display the botton 5 rows<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[3]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Model<\/th>\n<th>Year<\/th>\n<th>Engine Fuel Type<\/th>\n<th>Engine HP<\/th>\n<th>Engine Cylinders<\/th>\n<th>Transmission Type<\/th>\n<th>Driven_Wheels<\/th>\n<th>Number of Doors<\/th>\n<th>Market Category<\/th>\n<th>Vehicle Size<\/th>\n<th>Vehicle Style<\/th>\n<th>highway MPG<\/th>\n<th>city mpg<\/th>\n<th>Popularity<\/th>\n<th>MSRP<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>11909<\/th>\n<td>Acura<\/td>\n<td>ZDX<\/td>\n<td>2012<\/td>\n<td>premium unleaded (required)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>AUTOMATIC<\/td>\n<td>all wheel drive<\/td>\n<td>4.0<\/td>\n<td>Crossover,Hatchback,Luxury<\/td>\n<td>Midsize<\/td>\n<td>4dr Hatchback<\/td>\n<td>23<\/td>\n<td>16<\/td>\n<td>204<\/td>\n<td>46120<\/td>\n<\/tr>\n<tr>\n<th>11910<\/th>\n<td>Acura<\/td>\n<td>ZDX<\/td>\n<td>2012<\/td>\n<td>premium unleaded (required)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>AUTOMATIC<\/td>\n<td>all wheel drive<\/td>\n<td>4.0<\/td>\n<td>Crossover,Hatchback,Luxury<\/td>\n<td>Midsize<\/td>\n<td>4dr Hatchback<\/td>\n<td>23<\/td>\n<td>16<\/td>\n<td>204<\/td>\n<td>56670<\/td>\n<\/tr>\n<tr>\n<th>11911<\/th>\n<td>Acura<\/td>\n<td>ZDX<\/td>\n<td>2012<\/td>\n<td>premium unleaded (required)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>AUTOMATIC<\/td>\n<td>all wheel drive<\/td>\n<td>4.0<\/td>\n<td>Crossover,Hatchback,Luxury<\/td>\n<td>Midsize<\/td>\n<td>4dr Hatchback<\/td>\n<td>23<\/td>\n<td>16<\/td>\n<td>204<\/td>\n<td>50620<\/td>\n<\/tr>\n<tr>\n<th>11912<\/th>\n<td>Acura<\/td>\n<td>ZDX<\/td>\n<td>2013<\/td>\n<td>premium unleaded (recommended)<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>AUTOMATIC<\/td>\n<td>all wheel drive<\/td>\n<td>4.0<\/td>\n<td>Crossover,Hatchback,Luxury<\/td>\n<td>Midsize<\/td>\n<td>4dr Hatchback<\/td>\n<td>23<\/td>\n<td>16<\/td>\n<td>204<\/td>\n<td>50920<\/td>\n<\/tr>\n<tr>\n<th>11913<\/th>\n<td>Lincoln<\/td>\n<td>Zephyr<\/td>\n<td>2006<\/td>\n<td>regular unleaded<\/td>\n<td>221.0<\/td>\n<td>6.0<\/td>\n<td>AUTOMATIC<\/td>\n<td>front wheel drive<\/td>\n<td>4.0<\/td>\n<td>Luxury<\/td>\n<td>Midsize<\/td>\n<td>Sedan<\/td>\n<td>26<\/td>\n<td>17<\/td>\n<td>61<\/td>\n<td>28995<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"3.-Checking-the-types-of-data\">3. Checking the types of data<a class=\"anchor-link\" href=\"#3.-Checking-the-types-of-data\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Here we check for the datatypes because sometimes the MSRP or the price of the car would be stored as a string, if in that case, we have to convert that string to the integer data only then we can plot the data via a graph. Here, in this case, the data is already in integer format so nothing to worry.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[4]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">dtypes<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[4]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>Make                  object\nModel                 object\nYear                   int64\nEngine Fuel Type      object\nEngine HP            float64\nEngine Cylinders     float64\nTransmission Type     object\nDriven_Wheels         object\nNumber of Doors      float64\nMarket Category       object\nVehicle Size          object\nVehicle Style         object\nhighway MPG            int64\ncity mpg               int64\nPopularity             int64\nMSRP                   int64\ndtype: object<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"4.-Dropping-irrelevant-columns\">4. Dropping irrelevant columns<a class=\"anchor-link\" href=\"#4.-Dropping-irrelevant-columns\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>This step is certainly needed in every EDA because sometimes there would be many columns that we never use in such cases dropping is the only solution. In this case, the columns such as Engine Fuel Type, Market Category, Vehicle style, Popularity, Number of doors, Vehicle Size doesn't make any sense to me so I just dropped for this instance.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[5]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">drop<\/span><span class=\"p\">([<\/span><span class=\"s1\">'Engine Fuel Type'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Market Category'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Vehicle Style'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Popularity'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Number of Doors'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Vehicle Size'<\/span><span class=\"p\">],<\/span> <span class=\"n\">axis<\/span><span class=\"o\">=<\/span><span class=\"mi\">1<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[5]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Model<\/th>\n<th>Year<\/th>\n<th>Engine HP<\/th>\n<th>Engine Cylinders<\/th>\n<th>Transmission Type<\/th>\n<th>Driven_Wheels<\/th>\n<th>highway MPG<\/th>\n<th>city mpg<\/th>\n<th>MSRP<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>BMW<\/td>\n<td>1 Series M<\/td>\n<td>2011<\/td>\n<td>335.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>26<\/td>\n<td>19<\/td>\n<td>46135<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>19<\/td>\n<td>40650<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>20<\/td>\n<td>36350<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>29450<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>34500<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"5.-Renaming-the-columns\">5. Renaming the columns<a class=\"anchor-link\" href=\"#5.-Renaming-the-columns\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>In this instance, most of the column names are very confusing to read, so I just tweaked their column names. This is a good approach it improves the readability of the data set.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[6]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">rename<\/span><span class=\"p\">(<\/span><span class=\"n\">columns<\/span><span class=\"o\">=<\/span><span class=\"p\">{<\/span><span class=\"s2\">\"Engine HP\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"HP\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"Engine Cylinders\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"Cylinders\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"Transmission Type\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"Transmission\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"Driven_Wheels\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"Drive Mode\"<\/span><span class=\"p\">,<\/span><span class=\"s2\">\"highway MPG\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"MPG-H\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"city mpg\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"MPG-C\"<\/span><span class=\"p\">,<\/span> <span class=\"s2\">\"MSRP\"<\/span><span class=\"p\">:<\/span> <span class=\"s2\">\"Price\"<\/span> <span class=\"p\">})<\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[6]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Model<\/th>\n<th>Year<\/th>\n<th>HP<\/th>\n<th>Cylinders<\/th>\n<th>Transmission<\/th>\n<th>Drive Mode<\/th>\n<th>MPG-H<\/th>\n<th>MPG-C<\/th>\n<th>Price<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>BMW<\/td>\n<td>1 Series M<\/td>\n<td>2011<\/td>\n<td>335.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>26<\/td>\n<td>19<\/td>\n<td>46135<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>19<\/td>\n<td>40650<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>20<\/td>\n<td>36350<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>29450<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>34500<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"6.-Dropping-the-duplicate-rows\">6. Dropping the duplicate rows<a class=\"anchor-link\" href=\"#6.-Dropping-the-duplicate-rows\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>This is often a handy thing to do because a huge data set as in this case contains more than 10, 000 rows often have some duplicate data which might be disturbing, so here I remove all the duplicate value from the data-set. For example prior to removing I had 11914 rows of data but after removing the duplicates 10925 data meaning that I had 989 of duplicate data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[7]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[7]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>(11914, 10)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[8]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">duplicate_rows_df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">duplicated<\/span><span class=\"p\">()]<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"number of duplicate rows: \"<\/span><span class=\"p\">,<\/span> <span class=\"n\">duplicate_rows_df<\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\">\n<pre>number of duplicate rows:  (989, 10)\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Now let us remove the duplicate data because it's ok to remove them.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[9]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">count<\/span><span class=\"p\">()<\/span>      <span class=\"c1\"># Used to count the number of rows<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[9]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>Make            11914\nModel           11914\nYear            11914\nHP              11845\nCylinders       11884\nTransmission    11914\nDrive Mode      11914\nMPG-H           11914\nMPG-C           11914\nPrice           11914\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>So seen above there are 11914 rows and we are removing 989 rows of duplicate data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[10]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">drop_duplicates<\/span><span class=\"p\">()<\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[10]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Model<\/th>\n<th>Year<\/th>\n<th>HP<\/th>\n<th>Cylinders<\/th>\n<th>Transmission<\/th>\n<th>Drive Mode<\/th>\n<th>MPG-H<\/th>\n<th>MPG-C<\/th>\n<th>Price<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>0<\/th>\n<td>BMW<\/td>\n<td>1 Series M<\/td>\n<td>2011<\/td>\n<td>335.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>26<\/td>\n<td>19<\/td>\n<td>46135<\/td>\n<\/tr>\n<tr>\n<th>1<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>19<\/td>\n<td>40650<\/td>\n<\/tr>\n<tr>\n<th>2<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>300.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>20<\/td>\n<td>36350<\/td>\n<\/tr>\n<tr>\n<th>3<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>29450<\/td>\n<\/tr>\n<tr>\n<th>4<\/th>\n<td>BMW<\/td>\n<td>1 Series<\/td>\n<td>2011<\/td>\n<td>230.0<\/td>\n<td>6.0<\/td>\n<td>MANUAL<\/td>\n<td>rear wheel drive<\/td>\n<td>28<\/td>\n<td>18<\/td>\n<td>34500<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[11]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">count<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[11]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>Make            10925\nModel           10925\nYear            10925\nHP              10856\nCylinders       10895\nTransmission    10925\nDrive Mode      10925\nMPG-H           10925\nMPG-C           10925\nPrice           10925\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"7.-Dropping-the-missing-or-null-values.\">7. Dropping the missing or null values.<a class=\"anchor-link\" href=\"#7.-Dropping-the-missing-or-null-values.\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>This is mostly similar to the previous step but in here all the missing values are detected and are dropped later. Now, this is not a good approach to do so, because many people just replace the missing values with the mean or the average of that column, but in this case, I just dropped that missing values. This is because there is nearly 100 missing value compared to 10, 000 values this is a small number and this is negligible so I just dropped those values.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[12]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">isnull<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">())<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\">\n<pre>Make             0\nModel            0\nYear             0\nHP              69\nCylinders       30\nTransmission     0\nDrive Mode       0\nMPG-H            0\nMPG-C            0\nPrice            0\ndtype: int64\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>This is the reason in the above step while counting both Cylinders and Horsepower (HP) had 10856 and 10895 over 10925 rows.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[13]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">dropna<\/span><span class=\"p\">()<\/span>    <span class=\"c1\"># Dropping the missing values.<\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">count<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[13]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>Make            10827\nModel           10827\nYear            10827\nHP              10827\nCylinders       10827\nTransmission    10827\nDrive Mode      10827\nMPG-H           10827\nMPG-C           10827\nPrice           10827\ndtype: int64<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Now we have removed all the rows which contain the Null or N\/A values (Cylinders and Horsepower (HP)).<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[14]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">isnull<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">sum<\/span><span class=\"p\">())<\/span>   <span class=\"c1\"># After dropping the values<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\">\n<pre>Make            0\nModel           0\nYear            0\nHP              0\nCylinders       0\nTransmission    0\nDrive Mode      0\nMPG-H           0\nMPG-C           0\nPrice           0\ndtype: int64\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"8.-Detecting-Outliers\">8. Detecting Outliers<a class=\"anchor-link\" href=\"#8.-Detecting-Outliers\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>An outlier is a point or set of points that are different from other points. Sometimes they can be very high or very low. It's often a good idea to detect and remove the outliers. Because outliers are one of the primary reasons for resulting in a less accurate model. Hence it's a good idea to remove them. The outlier detection and removing that I am going to perform is called IQR score technique. Often outliers can be seen with visualizations using a box plot. Shown below are the box plot of MSRP, Cylinders, Horsepower and EngineSize. Herein all the plots, you can find some points are outside the box they are none other than outliers. The technique of finding and removing outlier that I am performing in this assignment is taken help of a tutorial from<a href=\"https:\/\/towardsdatascience.com\/ways-to-detect-and-remove-the-outliers-404d16608dba\"> towards data science<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[15]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">sns<\/span><span class=\"o\">.<\/span><span class=\"n\">boxplot<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"o\">=<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'Price'<\/span><span class=\"p\">])<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[15]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f0d36a38be0&gt;<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output \">\n<img decoding=\"async\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWkAAAESCAYAAAA\/niRMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAGAYkQYAw4g0ABhGpAHAsP8H5yM8SLxc\/FgAAAAASUVORK5CYII=%0A\" class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[16]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">sns<\/span><span class=\"o\">.<\/span><span class=\"n\">boxplot<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"o\">=<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'HP'<\/span><span class=\"p\">])<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[16]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f0d369b3ba8&gt;<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output \">\n<img decoding=\"async\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWkAAAESCAYAAAA\/niRMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAcAwIg0AhhFpADDs\/wCR8VlxtL5a4gAAAABJRU5ErkJggg==%0A\" class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[17]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">sns<\/span><span class=\"o\">.<\/span><span class=\"n\">boxplot<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"o\">=<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'Cylinders'<\/span><span class=\"p\">])<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[17]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f0d3413ff28&gt;<\/pre>\n<\/div>\n<\/div>\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output \">\n<img decoding=\"async\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWkAAAESCAYAAAA\/niRMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ASUVORK5CYII=%0A\" class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[18]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">Q1<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">quantile<\/span><span class=\"p\">(<\/span><span class=\"mf\">0.25<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">Q3<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">quantile<\/span><span class=\"p\">(<\/span><span class=\"mf\">0.75<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">IQR<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Q3<\/span> <span class=\"o\">-<\/span> <span class=\"n\">Q1<\/span>\n<span class=\"nb\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">IQR<\/span><span class=\"p\">)<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output\" data-mime-type=\"text\/plain\">\n<pre>Year             9.0\nHP             130.0\nCylinders        2.0\nMPG-H            8.0\nMPG-C            6.0\nPrice        21327.5\ndtype: float64\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>Don't worry about the above values because it's not important to know each and every one of them because it's just important to know how to use this technique in order to remove the outliers.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[19]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span> <span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"o\">~<\/span><span class=\"p\">((<\/span><span class=\"n\">df<\/span> <span class=\"o\">&lt;<\/span> <span class=\"p\">(<\/span><span class=\"n\">Q1<\/span> <span class=\"o\">-<\/span> <span class=\"mf\">1.5<\/span> <span class=\"o\">*<\/span> <span class=\"n\">IQR<\/span><span class=\"p\">))<\/span> <span class=\"o\">|<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span> <span class=\"o\">&gt;<\/span> <span class=\"p\">(<\/span><span class=\"n\">Q3<\/span> <span class=\"o\">+<\/span> <span class=\"mf\">1.5<\/span> <span class=\"o\">*<\/span> <span class=\"n\">IQR<\/span><span class=\"p\">)))<\/span><span class=\"o\">.<\/span><span class=\"n\">any<\/span><span class=\"p\">(<\/span><span class=\"n\">axis<\/span><span class=\"o\">=<\/span><span class=\"mi\">1<\/span><span class=\"p\">)]<\/span>\n<span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[19]:<\/div>\n<div class=\"jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/plain\">\n<pre>(9191, 10)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p>As seen above there were around 1600 rows were outliers. But you cannot completely remove the outliers because even after you use the above technique there maybe 1\u00e2\u0080\u00932 outlier unremoved but that ok because there were more than 100 outliers. Something is better than nothing.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<hr>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h2 id=\"9.-Plot-different-features-against-one-another-(scatter),-against-frequency-(histogram)\">9. Plot different features against one another (scatter), against frequency (histogram)<a class=\"anchor-link\" href=\"#9.-Plot-different-features-against-one-another-(scatter),-against-frequency-(histogram)\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h3 id=\"Histogram\">Histogram<a class=\"anchor-link\" href=\"#Histogram\">\u00b6<\/a><\/h3>\n<p>Histogram refers to the frequency of occurrence of variables in an interval. In this case, there are mainly 10 different types of car manufacturing companies, but it is often important to know who has the most number of cars. To do this histogram is one of the trivial solutions which lets us know the total number of car manufactured by a different company.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[20]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">Make<\/span><span class=\"o\">.<\/span><span class=\"n\">value_counts<\/span><span class=\"p\">()<\/span><span class=\"o\">.<\/span><span class=\"n\">nlargest<\/span><span class=\"p\">(<\/span><span class=\"mi\">40<\/span><span class=\"p\">)<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">kind<\/span><span class=\"o\">=<\/span><span class=\"s1\">'bar'<\/span><span class=\"p\">,<\/span> <span class=\"n\">figsize<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span><span class=\"mi\">5<\/span><span class=\"p\">))<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Number of cars by make\"<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">ylabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Number of cars'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Make'<\/span><span class=\"p\">);<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output \">\n<img decoding=\"async\" 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ZGRYYFHREREZGRY4BEREREZGRZ4REREREaGBR4RERGRkWGBR0RERGRkWOARERERGRkW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%0Awb59+zB8+HDExsbyyvPi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Gdnc0e8T58+FAuP49UFUu4u7uzDWlPnTrF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%0A\" class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h3 id=\"Heat-Maps\">Heat Maps<a class=\"anchor-link\" href=\"#Heat-Maps\">\u00b6<\/a><\/h3>\n<p>Heat Maps is a type of plot which is necessary when we need to find the dependent variables. One of the best way to find the relationship between the features can be done using heat maps. In the below heat map we know that the price feature depends mainly on the Engine Size, Horsepower, and Cylinders.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[21]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">figure<\/span><span class=\"p\">(<\/span><span class=\"n\">figsize<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span><span class=\"mi\">5<\/span><span class=\"p\">))<\/span>\n<span class=\"n\">c<\/span><span class=\"o\">=<\/span> <span class=\"n\">df<\/span><span class=\"o\">.<\/span><span class=\"n\">corr<\/span><span class=\"p\">()<\/span>\n<span class=\"n\">sns<\/span><span class=\"o\">.<\/span><span class=\"n\">heatmap<\/span><span class=\"p\">(<\/span><span class=\"n\">c<\/span><span class=\"p\">,<\/span><span class=\"n\">cmap<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"BrBG\"<\/span><span class=\"p\">,<\/span><span class=\"n\">annot<\/span><span class=\"o\">=<\/span><span class=\"kc\">True<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">c<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child jp-OutputArea-executeResult\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\">Out[21]:<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult\" data-mime-type=\"text\/html\">\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }<\/p>\n<p>    .dataframe tbody tr th {\n        vertical-align: top;\n    }<\/p>\n<p>    .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>Year<\/th>\n<th>HP<\/th>\n<th>Cylinders<\/th>\n<th>MPG-H<\/th>\n<th>MPG-C<\/th>\n<th>Price<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Year<\/th>\n<td>1.000000<\/td>\n<td>0.326726<\/td>\n<td>-0.133920<\/td>\n<td>0.378479<\/td>\n<td>0.338145<\/td>\n<td>0.592983<\/td>\n<\/tr>\n<tr>\n<th>HP<\/th>\n<td>0.326726<\/td>\n<td>1.000000<\/td>\n<td>0.715237<\/td>\n<td>-0.443807<\/td>\n<td>-0.544551<\/td>\n<td>0.739042<\/td>\n<\/tr>\n<tr>\n<th>Cylinders<\/th>\n<td>-0.133920<\/td>\n<td>0.715237<\/td>\n<td>1.000000<\/td>\n<td>-0.703856<\/td>\n<td>-0.755540<\/td>\n<td>0.354013<\/td>\n<\/tr>\n<tr>\n<th>MPG-H<\/th>\n<td>0.378479<\/td>\n<td>-0.443807<\/td>\n<td>-0.703856<\/td>\n<td>1.000000<\/td>\n<td>0.939141<\/td>\n<td>-0.106320<\/td>\n<\/tr>\n<tr>\n<th>MPG-C<\/th>\n<td>0.338145<\/td>\n<td>-0.544551<\/td>\n<td>-0.755540<\/td>\n<td>0.939141<\/td>\n<td>1.000000<\/td>\n<td>-0.180515<\/td>\n<\/tr>\n<tr>\n<th>Price<\/th>\n<td>0.592983<\/td>\n<td>0.739042<\/td>\n<td>0.354013<\/td>\n<td>-0.106320<\/td>\n<td>-0.180515<\/td>\n<td>1.000000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<div 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JUKhU2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%0A9H\/B5rXjxU8C0AAAAABJRU5ErkJggg==%0A\" class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<h3 id=\"Scatterplot\">Scatterplot<a class=\"anchor-link\" href=\"#Scatterplot\">\u00b6<\/a><\/h3>\n<p>We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between Horsepower and Price and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-CodeCell jp-Notebook-cell   \">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">In\u00a0[22]:<\/div>\n<div class=\"jp-CodeMirrorEditor jp-Editor jp-InputArea-editor\" data-type=\"inline\">\n<div class=\"CodeMirror cm-s-jupyter\">\n<div class=\" highlight hl-ipython3\">\n<pre><span><\/span><span class=\"n\">fig<\/span><span class=\"p\">,<\/span> <span class=\"n\">ax<\/span> <span class=\"o\">=<\/span> <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">subplots<\/span><span class=\"p\">(<\/span><span class=\"n\">figsize<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span><span class=\"mi\">6<\/span><span class=\"p\">))<\/span>\n<span class=\"n\">ax<\/span><span class=\"o\">.<\/span><span class=\"n\">scatter<\/span><span class=\"p\">(<\/span><span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'HP'<\/span><span class=\"p\">],<\/span> <span class=\"n\">df<\/span><span class=\"p\">[<\/span><span class=\"s1\">'Price'<\/span><span class=\"p\">])<\/span>\n<span class=\"n\">ax<\/span><span class=\"o\">.<\/span><span class=\"n\">set_xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'HP'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">ax<\/span><span class=\"o\">.<\/span><span class=\"n\">set_ylabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Price'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span>\n<\/pre>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell-outputWrapper\">\n<div class=\"jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser\">\n<\/div>\n<div class=\"jp-OutputArea jp-Cell-outputArea\">\n<div class=\"jp-OutputArea-child\">\n<div class=\"jp-OutputPrompt jp-OutputArea-prompt\"><\/div>\n<div class=\"jp-RenderedImage jp-OutputArea-output \">\n<img decoding=\"async\" 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pJaNZ2I4JfJ86tD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%0AxzlzRMWJmT9ylFc7D6H7wDCm5OmhqaUL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class=\"\n\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"jp-Cell jp-MarkdownCell jp-Notebook-cell\">\n<div class=\"jp-Cell-inputWrapper\">\n<div class=\"jp-Collapser jp-InputCollapser jp-Cell-inputCollapser\">\n<\/div>\n<div class=\"jp-InputArea jp-Cell-inputArea\">\n<div class=\"jp-InputPrompt jp-InputArea-prompt\">\n<\/div>\n<div class=\"jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput \" data-mime-type=\"text\/markdown\">\n<p><strong>Hence the above are some of the steps involved in Exploratory data analysis, these are some general steps that you must follow in order to perform EDA. There are many more yet to come but for now, this is more than enough idea as to how to perform a good EDA given any data sets. Stay tuned for more updates.<\/strong><\/p>\n<h2 id=\"Thank-you.\">Thank you.<a class=\"anchor-link\" href=\"#Thank-you.\">\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/p><\/div>\n<h2>Questions:<\/h2>\n<div class=\"h5p-iframe-wrapper\"><iframe id=\"h5p-iframe-1\" class=\"h5p-iframe\" data-content-id=\"1\" style=\"height:1px\" src=\"about:blank\" frameBorder=\"0\" scrolling=\"no\" title=\"Q1\"><\/iframe><\/div>\n","protected":false},"author":1,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[47],"contributor":[],"license":[],"class_list":["post-5","chapter","type-chapter","status-publish","hentry","chapter-type-standard"],"part":3,"_links":{"self":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapters\/5","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":6,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapters\/5\/revisions"}],"predecessor-version":[{"id":118,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapters\/5\/revisions\/118"}],"part":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapters\/5\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/wp\/v2\/media?parent=5"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/pressbooks\/v2\/chapter-type?post=5"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/wp\/v2\/contributor?post=5"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/www.publiconsulting.com\/wordpress\/pythonfords\/wp-json\/wp\/v2\/license?post=5"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}