{"id":1867,"date":"2024-04-26T22:22:59","date_gmt":"2024-04-26T16:52:59","guid":{"rendered":"https:\/\/moodle.sit.ac.in\/blog\/?p=1867"},"modified":"2024-05-13T23:29:10","modified_gmt":"2024-05-13T17:59:10","slug":"data-science-and-its-applications-21ad62","status":"publish","type":"post","link":"https:\/\/moodle.sit.ac.in\/blog\/data-science-and-its-applications-21ad62\/","title":{"rendered":"DATA SCIENCE AND ITS APPLICATIONS &#8211; 21AD62"},"content":{"rendered":"\n<p>In this blog post, you will find solutions for the <strong>Data Science And Its Applications (21AD62)<\/strong> course work for the VI semester of <strong>VTU<\/strong> university. To follow along, you will need to set up a Python programming environment. We recommend using the Anaconda Python Distribution with Jupyter Notebook\/ Spyder as the integrated development environment (IDE). You can find the lab syllabus on the university&#8217;s website or here.<\/p>\n\n\n\n<p><strong>Syllabus<\/strong><\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/21AD62.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of 21AD62.\"><\/object><a id=\"wp-block-file--media-39b34869-6a19-4bdc-b752-0f7939471a76\" href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/21AD62.pdf\">21AD62<\/a><a href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/21AD62.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-39b34869-6a19-4bdc-b752-0f7939471a76\">Download<\/a><\/div>\n\n\n\n<p>For detailed instructions on setting up the Python programming environment on Ubuntu, please refer to my previous blog, which can be found below.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-myblogosphere wp-block-embed-myblogosphere\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"VDYJEb3uww\"><a href=\"https:\/\/moodle.sit.ac.in\/blog\/setting-up-anaconda-python-programming-environment\/\">Setting up Anaconda Python Programming Environment on Ubuntu<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;Setting up Anaconda Python Programming Environment on Ubuntu&#8221; &#8212; MyBlogosphere\" data-src=\"https:\/\/moodle.sit.ac.in\/blog\/setting-up-anaconda-python-programming-environment\/embed\/#?secret=Vd47IhohHw#?secret=VDYJEb3uww\" data-secret=\"VDYJEb3uww\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>If you are looking for step-by-step instructions on how to set up the Python programming environment on a Windows system, I have provided detailed guidance in my previous blog. You can access the blog below for all the information you need.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-myblogosphere wp-block-embed-myblogosphere\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"2PqnbIKJXB\"><a href=\"https:\/\/moodle.sit.ac.in\/blog\/a-step-by-step-guide-to-setting-up-anaconda-python-distribution-on-windows\/\">A Step-by-Step Guide to Setting up Anaconda Python Distribution on Windows<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;A Step-by-Step Guide to Setting up Anaconda Python Distribution on Windows&#8221; &#8212; MyBlogosphere\" data-src=\"https:\/\/moodle.sit.ac.in\/blog\/a-step-by-step-guide-to-setting-up-anaconda-python-distribution-on-windows\/embed\/#?secret=gOCHdExOP0#?secret=2PqnbIKJXB\" data-secret=\"2PqnbIKJXB\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>After getting the necessary development environment setup, Now lets focus on the solutions.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><span style=\"color: #ff0000;\"><a href=\"#aioseo-module-1\" title=\"Module 1\">Module 1<\/a><\/span>\n<ol class=\"wp-block-list\" style=\"list-style-type:lower-alpha\">\n<li><span style=\"color: #ff0000;\"><a href=\"#m1p3\" title=\"Students performance in the final exams\">Students performance in the final exams<\/a><\/span><\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#m1p4\" title=\"Histogram to check the frequency distribution\">Histogram to check the frequency distribution<\/a><\/span><\/li>\n<\/ol>\n<\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#aioseo-module-2\" title=\"Module 2\"><span style=\"color: #ff0000;\">Module<\/span> 2<\/a><\/span>\n<ol class=\"wp-block-list\" style=\"list-style-type:lower-alpha\">\n<li><span style=\"color: #ff0000;\"><a href=\"#m2p1\" title=\"Kaggle Book Data set\">Kaggle Book Data set<\/a><\/span><\/li>\n<\/ol>\n<\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#aioseo-module-3\" title=\"Module 3\"><span style=\"color: #ff0000;\">Module<\/span> 3<\/a><\/span>\n<ol class=\"wp-block-list\" style=\"list-style-type:lower-alpha\">\n<li><span style=\"color: #ff0000;\"><a href=\"#m3p1\" title=\"Logistic Regression\">Logistic Regression<\/a><\/span><\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#m3p2\" title=\"SVM classifier\">SVM classifier<\/a><\/span><\/li>\n<\/ol>\n<\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#aioseo-module-4\" title=\"Module 4\"><span style=\"color: #ff0000;\">Module<\/span> 4<\/a><\/span>\n<ol class=\"wp-block-list\" style=\"list-style-type:lower-alpha\">\n<li><span style=\"color: #ff0000;\"><a href=\"#m4p1\" title=\"Decision Tree based ID3 algorithm\">Decision Tree based ID3 algorithm<\/a><\/span><\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#m4p2\" title=\"Clustering\">Clustering<\/a><\/span><\/li>\n<\/ol>\n<\/li>\n\n\n\n<li><span style=\"color: #ff0000;\"><a href=\"#aioseo-module-5\" title=\"Module 5\"><span style=\"color: #ff0000;\">Module<\/span> 5<\/a><\/span>\n<ol class=\"wp-block-list\" style=\"list-style-type:lower-alpha\">\n<li><span style=\"color: #ff0000;\"><a href=\"#m5p1\" title=\"Mini Project\">Mini Project<\/a><\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"aioseo-module-1\">Module 1<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Question 3<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"m1p3\">Students performance in the final exams<\/h3>\n\n\n\n<p>A study was conducted to understand the effect of number of hours the students spent studying on their performance in the final exams. Write a code to plot line chart with number of hours spent studying on x-axis and score in final exam on y-axis. Use a red \u2018*\u2019 as the point character, label the axes and give the plot a title.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-5.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"563\" height=\"204\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-5.png?resize=563%2C204&#038;ssl=1\" alt=\"\" class=\"wp-image-2115 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-5.png?w=563&amp;ssl=1 563w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-5.png?resize=300%2C109&amp;ssl=1 300w\" data-sizes=\"(max-width: 563px) 100vw, 563px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 563px; --smush-placeholder-aspect-ratio: 563\/204;\" \/><\/a><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Python Code<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"import matplotlib.pyplot as plt\n\nhours = [10,9,2,15,10,16,11,16]\nscore = [95,80,10,50,45,98,38,93]\n\n# Plotting the line chart\nplt.plot(hours, score, marker='*', color='red', linestyle='-')\n\n# Adding labels and title\nplt.xlabel('Number of Hours Studied')\nplt.ylabel('Score in Final Exam')\nplt.title('Effect of Hours Studied on Exam Score')\n\n# Displaying the plot\nplt.grid(True)\nplt.show()\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> matplotlib.pyplot <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> plt<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">hours <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> [<\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">9<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">15<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">16<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">11<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">16<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">score <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> [<\/span><span style=\"color: #79B8FF\">95<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">80<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">50<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">45<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">98<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">38<\/span><span style=\"color: #E1E4E8\">,<\/span><span style=\"color: #79B8FF\">93<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Plotting the line chart<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.plot(hours, score, <\/span><span style=\"color: #FFAB70\">marker<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;*&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">color<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;red&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">linestyle<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;-&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Adding labels and title<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.xlabel(<\/span><span style=\"color: #9ECBFF\">&#39;Number of Hours Studied&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.ylabel(<\/span><span style=\"color: #9ECBFF\">&#39;Score in Final Exam&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.title(<\/span><span style=\"color: #9ECBFF\">&#39;Effect of Hours Studied on Exam Score&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Displaying the plot<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.grid(<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.show()<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-c1a937a723e859bcdea304f57b2a8c69\"><a href=\"https:\/\/drive.google.com\/file\/d\/1v0v4ycyMyIzSsgpHun5E9UDA2exdlxs9\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"\">M1P3 Program<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Output<\/h4>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-6.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"571\" height=\"453\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-6.png?resize=571%2C453&#038;ssl=1\" alt=\"\" class=\"wp-image-2116 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-6.png?w=571&amp;ssl=1 571w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-6.png?resize=300%2C238&amp;ssl=1 300w\" data-sizes=\"(max-width: 571px) 100vw, 571px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 571px; --smush-placeholder-aspect-ratio: 571\/453;\" \/><\/a><\/figure>\n\n\n\n<p>The program above demonstrates a clear trend: generally, the more hours students study, the better they perform on the final exam. However, there are some cases where this relationship isn&#8217;t quite as straightforward, yielding slightly different outcomes.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Question 4<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"m1p4\">Histogram to check the frequency distribution<\/h3>\n\n\n\n<p>For the given dataset mtcars.csv (www.kaggle.com\/ruiromanini\/mtcars), plot a histogram to check the frequency distribution of the variable \u2018mpg\u2019 (Miles per gallon)<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Python Code<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"import pandas as pd\nimport matplotlib.pyplot as plt\n\n# Load the dataset\nmtcars = pd.read_csv('mtcars.csv')  # Replace 'path_to_your_mtcars.csv' with the actual path to your mtcars.csv file\n\n# Plotting the histogram\nplt.hist(mtcars['mpg'], bins=10, color='skyblue', edgecolor='black')\n\n# Adding labels and title\nplt.xlabel('Miles per gallon (mpg)')\nplt.ylabel('Frequency')\nplt.title('Histogram of Miles per gallon (mpg)')\n\n# Displaying the plot\nplt.show()\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> pandas <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> pd<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> matplotlib.pyplot <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> plt<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Load the dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">mtcars <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pd.read_csv(<\/span><span style=\"color: #9ECBFF\">&#39;mtcars.csv&#39;<\/span><span style=\"color: #E1E4E8\">)  <\/span><span style=\"color: #6A737D\"># Replace &#39;path_to_your_mtcars.csv&#39; with the actual path to your mtcars.csv file<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Plotting the histogram<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.hist(mtcars[<\/span><span style=\"color: #9ECBFF\">&#39;mpg&#39;<\/span><span style=\"color: #E1E4E8\">], <\/span><span style=\"color: #FFAB70\">bins<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">color<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;skyblue&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">edgecolor<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;black&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Adding labels and title<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.xlabel(<\/span><span style=\"color: #9ECBFF\">&#39;Miles per gallon (mpg)&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.ylabel(<\/span><span style=\"color: #9ECBFF\">&#39;Frequency&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.title(<\/span><span style=\"color: #9ECBFF\">&#39;Histogram of Miles per gallon (mpg)&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Displaying the plot<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.show()<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<p>The required file <code>mtcars.csv<\/code> can be found below<\/p>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-eb273e84-2053-4914-ab0d-7e2338b134c7\" href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/mtcars.csv\">mtcars.csv<\/a><a href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/mtcars.csv\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-eb273e84-2053-4914-ab0d-7e2338b134c7\">Download<\/a><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-845e759cfcc451b8dd10c8063571725d\"><a href=\"https:\/\/drive.google.com\/file\/d\/1iXH-vLRKVqlIBwiT2TPlPv-2mxypKMuQ\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M1P4 Program\">M1P4 Program<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Output<\/h4>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-7.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"561\" height=\"453\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-7.png?resize=561%2C453&#038;ssl=1\" alt=\"\" class=\"wp-image-2118 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-7.png?w=561&amp;ssl=1 561w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-7.png?resize=300%2C242&amp;ssl=1 300w\" data-sizes=\"(max-width: 561px) 100vw, 561px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 561px; --smush-placeholder-aspect-ratio: 561\/453;\" \/><\/a><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"aioseo-module-2\">Module 2<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\">Question 1<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m2p1\">Kaggle Book Data set<\/h3>\n\n\n\n<p>Consider the books dataset BL-Flickr-Images-Book.csv from Kaggle (https:\/\/www.kaggle.com\/adeyoyintemidayo\/publication-of-books) which contains information about books. Write a program to demonstrate the following.<br><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Import the data into a DataFrame<\/li>\n\n\n\n<li>Find and drop the columns which are irrelevant for the book information.<\/li>\n\n\n\n<li>Change the Index of the DataFrame<\/li>\n\n\n\n<li>Tidy up fields in the data such as date of publication with the help of simple regular expression.<\/li>\n\n\n\n<li>Combine str methods with NumPy to clean columns<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Python Code<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"import pandas as pd\nimport numpy as np\n\n# Import the data into a DataFrame\ndf = pd.read_csv('BL-Flickr-Images-Book.csv')\n\n# Display the first few rows of the DataFrame\nprint(&quot;Original DataFrame:&quot;)\nprint(df.head())\n\n# Find and drop the columns which are irrelevant for the book information\nirrelevant_columns = ['Edition Statement', 'Corporate Author', 'Corporate Contributors', 'Former owner', 'Engraver', 'Contributors', 'Issuance type', 'Shelfmarks']\ndf.drop(columns=irrelevant_columns, inplace=True)\n\n# Change the Index of the DataFrame\ndf.set_index('Identifier', inplace=True)\n\n# Tidy up fields in the data such as date of publication with the help of simple regular expression\ndf['Date of Publication'] = df['Date of Publication'].str.extract(r'^(\\d{4})', expand=False)\n\n# Combine str methods with NumPy to clean columns\ndf['Place of Publication'] = np.where(df['Place of Publication'].str.contains('London'), 'London', df['Place of Publication'].str.replace('-', ' '))\n\n# Display the cleaned DataFrame\nprint(&quot;\\nCleaned DataFrame:&quot;)\nprint(df.head())\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> pandas <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> pd<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> numpy <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> np<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Import the data into a DataFrame<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pd.read_csv(<\/span><span style=\"color: #9ECBFF\">&#39;BL-Flickr-Images-Book.csv&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Display the first few rows of the DataFrame<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Original DataFrame:&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(df.head())<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Find and drop the columns which are irrelevant for the book information<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">irrelevant_columns <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> [<\/span><span style=\"color: #9ECBFF\">&#39;Edition Statement&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Corporate Author&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Corporate Contributors&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Former owner&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Engraver&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Contributors&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Issuance type&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Shelfmarks&#39;<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df.drop(<\/span><span style=\"color: #FFAB70\">columns<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">irrelevant_columns, <\/span><span style=\"color: #FFAB70\">inplace<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Change the Index of the DataFrame<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df.set_index(<\/span><span style=\"color: #9ECBFF\">&#39;Identifier&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">inplace<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Tidy up fields in the data such as date of publication with the help of simple regular expression<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df[<\/span><span style=\"color: #9ECBFF\">&#39;Date of Publication&#39;<\/span><span style=\"color: #E1E4E8\">] <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> df[<\/span><span style=\"color: #9ECBFF\">&#39;Date of Publication&#39;<\/span><span style=\"color: #E1E4E8\">].str.extract(<\/span><span style=\"color: #F97583\">r<\/span><span style=\"color: #9ECBFF\">&#39;<\/span><span style=\"color: #79B8FF\">^(\\d<\/span><span style=\"color: #F97583\">{4}<\/span><span style=\"color: #79B8FF\">)<\/span><span style=\"color: #9ECBFF\">&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">expand<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">False<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Combine str methods with NumPy to clean columns<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df[<\/span><span style=\"color: #9ECBFF\">&#39;Place of Publication&#39;<\/span><span style=\"color: #E1E4E8\">] <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> np.where(df[<\/span><span style=\"color: #9ECBFF\">&#39;Place of Publication&#39;<\/span><span style=\"color: #E1E4E8\">].str.contains(<\/span><span style=\"color: #9ECBFF\">&#39;London&#39;<\/span><span style=\"color: #E1E4E8\">), <\/span><span style=\"color: #9ECBFF\">&#39;London&#39;<\/span><span style=\"color: #E1E4E8\">, df[<\/span><span style=\"color: #9ECBFF\">&#39;Place of Publication&#39;<\/span><span style=\"color: #E1E4E8\">].str.replace(<\/span><span style=\"color: #9ECBFF\">&#39;-&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39; &#39;<\/span><span style=\"color: #E1E4E8\">))<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Display the cleaned DataFrame<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;<\/span><span style=\"color: #79B8FF\">\\n<\/span><span style=\"color: #9ECBFF\">Cleaned DataFrame:&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(df.head())<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<p>The required file <code>BL-Flickr-Images-Book.csv<\/code> can be found below<\/p>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-0d4e1d09-d9aa-4fcd-b341-5c75993ee76b\" href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/BL-Flickr-Images-Book.csv\">BL-Flickr-Images-Book.csv<\/a><a href=\"https:\/\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/BL-Flickr-Images-Book.csv\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-0d4e1d09-d9aa-4fcd-b341-5c75993ee76b\">Download<\/a><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-5ed4d143c9ff999324a0ca56a69c0a2c\"><a href=\"https:\/\/drive.google.com\/file\/d\/1QhRMv61IIMDMBRZDEXTvLHA5UEgWpYd7\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M2P1 Program\">M2P1 Program<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Output<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"Original DataFrame:\n   Identifier             Edition Statement      Place of Publication  \\\n0         206                           NaN                    London   \n1         216                           NaN  London; Virtue &amp; Yorston   \n2         218                           NaN                    London   \n3         472                           NaN                    London   \n4         480  A new edition, revised, etc.                    London   \n\n  Date of Publication              Publisher  \\\n0         1879 [1878]       S. Tinsley &amp; Co.   \n1                1868           Virtue &amp; Co.   \n2                1869  Bradbury, Evans &amp; Co.   \n3                1851          James Darling   \n4                1857   Wertheim &amp; Macintosh   \n\n                                               Title     Author  \\\n0                  Walter Forbes. [A novel.] By A. A      A. A.   \n1  All for Greed. [A novel. The dedication signed...  A., A. A.   \n2  Love the Avenger. By the author of \u201cAll for Gr...  A., A. A.   \n3  Welsh Sketches, chiefly ecclesiastical, to the...  A., E. S.   \n4  [The World in which I live, and my place in it...  A., E. S.   \n\n                                   Contributors  Corporate Author  \\\n0                               FORBES, Walter.               NaN   \n1  BLAZE DE BURY, Marie Pauline Rose - Baroness               NaN   \n2  BLAZE DE BURY, Marie Pauline Rose - Baroness               NaN   \n3                   Appleyard, Ernest Silvanus.               NaN   \n4                           BROOME, John Henry.               NaN   \n\n   Corporate Contributors Former owner  Engraver Issuance type  \\\n0                     NaN          NaN       NaN   monographic   \n1                     NaN          NaN       NaN   monographic   \n2                     NaN          NaN       NaN   monographic   \n3                     NaN          NaN       NaN   monographic   \n4                     NaN          NaN       NaN   monographic   \n\n                                          Flickr URL  \\\n0  http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...   \n1  http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...   \n2  http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...   \n3  http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...   \n4  http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...   \n\n                            Shelfmarks  \n0    British Library HMNTS 12641.b.30.  \n1    British Library HMNTS 12626.cc.2.  \n2    British Library HMNTS 12625.dd.1.  \n3  British Library HMNTS 10369.bbb.15.  \n4     British Library HMNTS 9007.d.28.  \n\nCleaned DataFrame:\n           Place of Publication Date of Publication              Publisher  \\\nIdentifier                                                                   \n206                      London                1879       S. Tinsley &amp; Co.   \n216                      London                1868           Virtue &amp; Co.   \n218                      London                1869  Bradbury, Evans &amp; Co.   \n472                      London                1851          James Darling   \n480                      London                1857   Wertheim &amp; Macintosh   \n\n                                                        Title     Author  \\\nIdentifier                                                                 \n206                         Walter Forbes. [A novel.] By A. A      A. A.   \n216         All for Greed. [A novel. The dedication signed...  A., A. A.   \n218         Love the Avenger. By the author of \u201cAll for Gr...  A., A. A.   \n472         Welsh Sketches, chiefly ecclesiastical, to the...  A., E. S.   \n480         [The World in which I live, and my place in it...  A., E. S.   \n\n                                                   Flickr URL  \nIdentifier                                                     \n206         http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...  \n216         http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...  \n218         http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...  \n472         http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...  \n480         http:\/\/www.flickr.com\/photos\/britishlibrary\/ta...  \" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #E1E4E8\">Original <\/span><span style=\"color: #B392F0\">DataFrame<\/span><span style=\"color: #E1E4E8\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">   Identifier             Edition Statement      Place <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> Publication  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">206<\/span><span style=\"color: #E1E4E8\">                           <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">                    London   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">216<\/span><span style=\"color: #E1E4E8\">                           <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">  London; Virtue <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Yorston   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">218<\/span><span style=\"color: #E1E4E8\">                           <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">                    London   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">472<\/span><span style=\"color: #E1E4E8\">                           <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">                    London   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">480<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #F97583\">new<\/span><span style=\"color: #E1E4E8\"> edition, revised, etc.                    London   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">  Date <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> Publication              Publisher  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">         <\/span><span style=\"color: #79B8FF\">1879<\/span><span style=\"color: #E1E4E8\"> [<\/span><span style=\"color: #79B8FF\">1878<\/span><span style=\"color: #E1E4E8\">]       <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">. Tinsley <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">                <\/span><span style=\"color: #79B8FF\">1868<\/span><span style=\"color: #E1E4E8\">           Virtue <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">                <\/span><span style=\"color: #79B8FF\">1869<\/span><span style=\"color: #E1E4E8\">  Bradbury, Evans <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">                <\/span><span style=\"color: #79B8FF\">1851<\/span><span style=\"color: #E1E4E8\">          James Darling   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">                <\/span><span style=\"color: #79B8FF\">1857<\/span><span style=\"color: #E1E4E8\">   Wertheim <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Macintosh   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                                               Title     Author  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">                  Walter Forbes. [<\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\"> novel.] By <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">      <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">  All for Greed. [<\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\"> novel. The dedication signed<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">  Love the Avenger. By the author <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> \u201cAll for Gr<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">  Welsh Sketches, chiefly ecclesiastical, to the<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">E<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">  [The World <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> which <\/span><span style=\"color: #79B8FF\">I<\/span><span style=\"color: #E1E4E8\"> live, and my place <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> it<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">E<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                                   Contributors  Corporate Author  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">                               <\/span><span style=\"color: #79B8FF\">FORBES<\/span><span style=\"color: #E1E4E8\">, Walter.               NaN   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">BLAZE<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">DE<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">BURY<\/span><span style=\"color: #E1E4E8\">, Marie Pauline Rose <\/span><span style=\"color: #F97583\">-<\/span><span style=\"color: #E1E4E8\"> Baroness               <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">BLAZE<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">DE<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">BURY<\/span><span style=\"color: #E1E4E8\">, Marie Pauline Rose <\/span><span style=\"color: #F97583\">-<\/span><span style=\"color: #E1E4E8\"> Baroness               <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">                   Appleyard, Ernest Silvanus.               NaN   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">                           <\/span><span style=\"color: #79B8FF\">BROOME<\/span><span style=\"color: #E1E4E8\">, John Henry.               NaN   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">   Corporate Contributors Former owner  Engraver Issuance type  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">                     <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">          <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   monographic   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">                     <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">          <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   monographic   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">                     <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">          <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   monographic   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">                     <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">          <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   monographic   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">                     <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">          <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">NaN<\/span><span style=\"color: #E1E4E8\">   monographic   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                                          Flickr <\/span><span style=\"color: #79B8FF\">URL<\/span><span style=\"color: #E1E4E8\">  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">  http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">  http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">  http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">  http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">  http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                            Shelfmarks  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">    British Library <\/span><span style=\"color: #79B8FF\">HMNTS<\/span><span style=\"color: #E1E4E8\"> 12641.b.<\/span><span style=\"color: #79B8FF\">30.<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">    British Library <\/span><span style=\"color: #79B8FF\">HMNTS<\/span><span style=\"color: #E1E4E8\"> 12626.cc.<\/span><span style=\"color: #79B8FF\">2.<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">    British Library <\/span><span style=\"color: #79B8FF\">HMNTS<\/span><span style=\"color: #E1E4E8\"> 12625.dd.<\/span><span style=\"color: #79B8FF\">1.<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">  British Library <\/span><span style=\"color: #79B8FF\">HMNTS<\/span><span style=\"color: #E1E4E8\"> 10369.bbb.<\/span><span style=\"color: #79B8FF\">15.<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">4<\/span><span style=\"color: #E1E4E8\">     British Library <\/span><span style=\"color: #79B8FF\">HMNTS<\/span><span style=\"color: #E1E4E8\"> 9007.d.<\/span><span style=\"color: #79B8FF\">28.<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Cleaned DataFrame:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">           Place <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> Publication Date <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> Publication              Publisher  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Identifier                                                                   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">206<\/span><span style=\"color: #E1E4E8\">                      London                <\/span><span style=\"color: #79B8FF\">1879<\/span><span style=\"color: #E1E4E8\">       <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">. Tinsley <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">216<\/span><span style=\"color: #E1E4E8\">                      London                <\/span><span style=\"color: #79B8FF\">1868<\/span><span style=\"color: #E1E4E8\">           Virtue <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">218<\/span><span style=\"color: #E1E4E8\">                      London                <\/span><span style=\"color: #79B8FF\">1869<\/span><span style=\"color: #E1E4E8\">  Bradbury, Evans <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Co.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">472<\/span><span style=\"color: #E1E4E8\">                      London                <\/span><span style=\"color: #79B8FF\">1851<\/span><span style=\"color: #E1E4E8\">          James Darling   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">480<\/span><span style=\"color: #E1E4E8\">                      London                <\/span><span style=\"color: #79B8FF\">1857<\/span><span style=\"color: #E1E4E8\">   Wertheim <\/span><span style=\"color: #F97583\">&amp;<\/span><span style=\"color: #E1E4E8\"> Macintosh   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                                                        Title     Author  \\<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Identifier                                                                 <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">206<\/span><span style=\"color: #E1E4E8\">                         Walter Forbes. [<\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\"> novel.] By <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">      <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">216<\/span><span style=\"color: #E1E4E8\">         All for Greed. [<\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\"> novel. The dedication signed<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">218<\/span><span style=\"color: #E1E4E8\">         Love the Avenger. By the author <\/span><span style=\"color: #F97583\">of<\/span><span style=\"color: #E1E4E8\"> \u201cAll for Gr<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">472<\/span><span style=\"color: #E1E4E8\">         Welsh Sketches, chiefly ecclesiastical, to the<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">E<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">480<\/span><span style=\"color: #E1E4E8\">         [The World <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> which <\/span><span style=\"color: #79B8FF\">I<\/span><span style=\"color: #E1E4E8\"> live, and my place <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> it<\/span><span style=\"color: #F97583\">...<\/span><span style=\"color: #E1E4E8\">  <\/span><span style=\"color: #79B8FF\">A<\/span><span style=\"color: #E1E4E8\">., <\/span><span style=\"color: #79B8FF\">E<\/span><span style=\"color: #E1E4E8\">. <\/span><span style=\"color: #79B8FF\">S<\/span><span style=\"color: #E1E4E8\">.   <\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">                                                   Flickr <\/span><span style=\"color: #79B8FF\">URL<\/span><span style=\"color: #E1E4E8\">  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Identifier                                                     <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">206<\/span><span style=\"color: #E1E4E8\">         http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">216<\/span><span style=\"color: #E1E4E8\">         http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">218<\/span><span style=\"color: #E1E4E8\">         http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">472<\/span><span style=\"color: #E1E4E8\">         http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">480<\/span><span style=\"color: #E1E4E8\">         http:<\/span><span style=\"color: #6A737D\">\/\/www.flickr.com\/photos\/britishlibrary\/ta...  <\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"aioseo-module-3\">Module 3<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\">Question 1<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m3p1\"> Logistic Regression<\/h3>\n\n\n\n<p>Train a regularized logistic regression classifier on the iris dataset (https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/iris\/ or the inbuilt iris dataset) using sklearn. Train the model with the following hyperparameter C = 1e4 and report the best classification accuracy.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Python Code<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"from sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import make_pipeline\n\n# Load the Iris dataset\niris = load_iris()\nX = iris.data\ny = iris.target\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Create a pipeline with StandardScaler and LogisticRegression with regularization\npipeline = make_pipeline(StandardScaler(), LogisticRegression(C=1e4, max_iter=1000))\n\n# Train the model\npipeline.fit(X_train, y_train)\n\n# Calculate the accuracy on the testing set\naccuracy = pipeline.score(X_test, y_test)\nprint(&quot;Classification accuracy:&quot;, accuracy)\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.datasets <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> load_iris<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.model_selection <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> train_test_split<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.linear_model <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> LogisticRegression<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.preprocessing <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> StandardScaler<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.pipeline <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> make_pipeline<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Load the Iris dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">iris <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> load_iris()<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> iris.data<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">y <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> iris.target<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Split the data into training and testing sets<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X_train, X_test, y_train, y_test <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> train_test_split(X, y, <\/span><span style=\"color: #FFAB70\">test_size<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">0.2<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">random_state<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">42<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Create a pipeline with StandardScaler and LogisticRegression with regularization<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">pipeline <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> make_pipeline(StandardScaler(), LogisticRegression(<\/span><span style=\"color: #FFAB70\">C<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">1e4<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">max_iter<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">1000<\/span><span style=\"color: #E1E4E8\">))<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Train the model<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">pipeline.fit(X_train, y_train)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Calculate the accuracy on the testing set<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">accuracy <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pipeline.score(X_test, y_test)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Classification accuracy:&quot;<\/span><span style=\"color: #E1E4E8\">, accuracy)<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-43c86e43d5db25648e957fbc455e3f60\"><a href=\"https:\/\/drive.google.com\/file\/d\/1LqAJgWhI3pOiW-Z3rI1CRD-rR8X4g_Hw\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M3P1 Program\">M3P1 Program<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Output<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"Classification accuracy: 1.0\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #E1E4E8\">Classification <\/span><span style=\"color: #B392F0\">accuracy<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">1.0<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Question 2<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m3p2\">SVM classifier<\/h3>\n\n\n\n<p>Train an SVM classifier on the iris dataset using sklearn. Try different kernels and the associated hyperparameters. Train model with the following set of hyperparameters RBF-kernel, gamma=0.5,<br>one-vs-rest classifier, no-feature-normalization. Also try C=0.01,1,10C=0.01,1,10. For the above set of hyperparameters, find the best classification accuracy along with total number of support vectors on the test data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Python Code<\/h3>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"from sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\n\n# Load the Iris dataset\niris = load_iris()\nX = iris.data\ny = iris.target\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Set of hyperparameters to try\nhyperparameters = [\n    {'kernel': 'rbf', 'gamma': 0.5, 'C': 0.01},\n    {'kernel': 'rbf', 'gamma': 0.5, 'C': 1},\n    {'kernel': 'rbf', 'gamma': 0.5, 'C': 10}\n]\n\nbest_accuracy = 0\nbest_model = None\nbest_support_vectors = None\n\n# Train SVM models with different hyperparameters and find the best accuracy\nfor params in hyperparameters:\n    model = SVC(kernel=params['kernel'], gamma=params['gamma'], C=params['C'], decision_function_shape='ovr')\n    model.fit(X_train, y_train)\n    accuracy = model.score(X_test, y_test)\n    support_vectors = model.n_support_.sum()\n    print(f&quot;For hyperparameters: {params}, Accuracy: {accuracy}, Total Support Vectors: {support_vectors}&quot;)\n    if accuracy &gt; best_accuracy:\n        best_accuracy = accuracy\n        best_model = model\n        best_support_vectors = support_vectors\n\nprint(&quot;\\nBest accuracy:&quot;, best_accuracy)\nprint(&quot;Total support vectors on test data:&quot;, best_support_vectors)\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.datasets <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> load_iris<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.model_selection <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> train_test_split<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.svm <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">SVC<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Load the Iris dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">iris <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> load_iris()<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> iris.data<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">y <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> iris.target<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Split the data into training and testing sets<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X_train, X_test, y_train, y_test <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> train_test_split(X, y, <\/span><span style=\"color: #FFAB70\">test_size<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">0.2<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">random_state<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">42<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Set of hyperparameters to try<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">hyperparameters <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> [<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.01<\/span><span style=\"color: #E1E4E8\">},<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">},<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">}<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">best_accuracy <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">0<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">best_model <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">None<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">best_support_vectors <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">None<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Train SVM models with different hyperparameters and find the best accuracy<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">for<\/span><span style=\"color: #E1E4E8\"> params <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> hyperparameters:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    model <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> SVC(<\/span><span style=\"color: #FFAB70\">kernel<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">params[<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">], <\/span><span style=\"color: #FFAB70\">gamma<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">params[<\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">], <\/span><span style=\"color: #FFAB70\">C<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">params[<\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">], <\/span><span style=\"color: #FFAB70\">decision_function_shape<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;ovr&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    model.fit(X_train, y_train)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    accuracy <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> model.score(X_test, y_test)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    support_vectors <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> model.n_support_.sum()<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #F97583\">f<\/span><span style=\"color: #9ECBFF\">&quot;For hyperparameters: <\/span><span style=\"color: #79B8FF\">{<\/span><span style=\"color: #E1E4E8\">params<\/span><span style=\"color: #79B8FF\">}<\/span><span style=\"color: #9ECBFF\">, Accuracy: <\/span><span style=\"color: #79B8FF\">{<\/span><span style=\"color: #E1E4E8\">accuracy<\/span><span style=\"color: #79B8FF\">}<\/span><span style=\"color: #9ECBFF\">, Total Support Vectors: <\/span><span style=\"color: #79B8FF\">{<\/span><span style=\"color: #E1E4E8\">support_vectors<\/span><span style=\"color: #79B8FF\">}<\/span><span style=\"color: #9ECBFF\">&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #F97583\">if<\/span><span style=\"color: #E1E4E8\"> accuracy <\/span><span style=\"color: #F97583\">&gt;<\/span><span style=\"color: #E1E4E8\"> best_accuracy:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        best_accuracy <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> accuracy<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        best_model <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> model<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        best_support_vectors <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> support_vectors<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;<\/span><span style=\"color: #79B8FF\">\\n<\/span><span style=\"color: #9ECBFF\">Best accuracy:&quot;<\/span><span style=\"color: #E1E4E8\">, best_accuracy)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Total support vectors on test data:&quot;<\/span><span style=\"color: #E1E4E8\">, best_support_vectors)<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-68a417204c74c5ca45eda571515889b7\"><a href=\"https:\/\/drive.google.com\/file\/d\/1aIzTwzDcfm1zvViLv0CKKLfhO1srry2q\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M3P2 Program\">M3P2 Program<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Output<\/h3>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"For hyperparameters: {'kernel': 'rbf', 'gamma': 0.5, 'C': 0.01}, Accuracy: 0.3, Total Support Vectors: 120\nFor hyperparameters: {'kernel': 'rbf', 'gamma': 0.5, 'C': 1}, Accuracy: 1.0, Total Support Vectors: 39\nFor hyperparameters: {'kernel': 'rbf', 'gamma': 0.5, 'C': 10}, Accuracy: 1.0, Total Support Vectors: 31\n\nBest accuracy: 1.0\nTotal support vectors on test data: 39\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #E1E4E8\">For hyperparameters: {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.01<\/span><span style=\"color: #E1E4E8\">}, Accuracy: <\/span><span style=\"color: #79B8FF\">0.3<\/span><span style=\"color: #E1E4E8\">, Total Support Vectors: <\/span><span style=\"color: #79B8FF\">120<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">For hyperparameters: {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">}, Accuracy: <\/span><span style=\"color: #79B8FF\">1.0<\/span><span style=\"color: #E1E4E8\">, Total Support Vectors: <\/span><span style=\"color: #79B8FF\">39<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">For hyperparameters: {<\/span><span style=\"color: #9ECBFF\">&#39;kernel&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #9ECBFF\">&#39;rbf&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;gamma&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.5<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;C&#39;<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">}, Accuracy: <\/span><span style=\"color: #79B8FF\">1.0<\/span><span style=\"color: #E1E4E8\">, Total Support Vectors: <\/span><span style=\"color: #79B8FF\">31<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Best accuracy: <\/span><span style=\"color: #79B8FF\">1.0<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Total support vectors on test data: <\/span><span style=\"color: #79B8FF\">39<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"aioseo-module-4\">Module 4<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\">Question 1<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m4p1\">Decision Tree based ID3 algorithm<\/h3>\n\n\n\n<p>Consider the following dataset. Write a program to demonstrate the working of the decision tree<br>based ID3 algorithm.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-8.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"363\" height=\"239\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-8.png?resize=363%2C239&#038;ssl=1\" alt=\"\" class=\"wp-image-2133 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-8.png?w=363&amp;ssl=1 363w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-8.png?resize=300%2C198&amp;ssl=1 300w\" data-sizes=\"(max-width: 363px) 100vw, 363px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 363px; --smush-placeholder-aspect-ratio: 363\/239;\" \/><\/a><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Python Code<\/h3>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"from sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport pandas as pd\nfrom io import StringIO\nfrom IPython.display import Image  \nimport pydotplus\n\n# Define the dataset\ndata = {\n    'Price': ['Low', 'Low', 'Low', 'Low', 'Low', 'Med', 'Med', 'Med', 'Med', 'High', 'High', 'High', 'High'],\n    'Maintenance': ['Low', 'Med', 'Low', 'Med', 'High', 'Med', 'Med', 'High', 'High', 'Med', 'Med', 'High', 'High'],\n    'Capacity': ['2', '4', '4', '4', '4', '4', '4', '2', '5', '4', '2', '2', '5'],\n    'Airbag': ['No', 'Yes', 'No', 'No', 'No', 'No', 'Yes', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes'],\n    'Profitable': [1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1]\n}\n\ndf = pd.DataFrame(data)\n\n# Convert categorical variables into numerical ones\ndf = pd.get_dummies(df, columns=['Price', 'Maintenance', 'Airbag'])\n\n# Separate features and target variable\nX = df.drop('Profitable', axis=1)\ny = df['Profitable']\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Create a decision tree classifier\nclf = DecisionTreeClassifier(criterion='entropy')\n\n# Train the classifier on the training data\nclf.fit(X_train, y_train)\n\n# Predict on the testing data\ny_pred = clf.predict(X_test)\n\n# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\nprint(&quot;Accuracy:&quot;, accuracy)\n\n# Visualize the decision tree\ndot_data = StringIO()\nexport_graphviz(clf, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names=X.columns)\ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \nImage(graph.create_png())\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.tree <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> DecisionTreeClassifier, export_graphviz<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.model_selection <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> train_test_split<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.metrics <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> accuracy_score<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> pandas <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> pd<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> io <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> StringIO<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> IPython.display <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> Image  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> pydotplus<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Define the dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">data <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> {<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #9ECBFF\">&#39;Price&#39;<\/span><span style=\"color: #E1E4E8\">: [<\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">],<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #9ECBFF\">&#39;Maintenance&#39;<\/span><span style=\"color: #E1E4E8\">: [<\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Low&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Med&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;High&#39;<\/span><span style=\"color: #E1E4E8\">],<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #9ECBFF\">&#39;Capacity&#39;<\/span><span style=\"color: #E1E4E8\">: [<\/span><span style=\"color: #9ECBFF\">&#39;2&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;2&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;5&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;4&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;2&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;2&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;5&#39;<\/span><span style=\"color: #E1E4E8\">],<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #9ECBFF\">&#39;Airbag&#39;<\/span><span style=\"color: #E1E4E8\">: [<\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;No&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Yes&#39;<\/span><span style=\"color: #E1E4E8\">],<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #9ECBFF\">&#39;Profitable&#39;<\/span><span style=\"color: #E1E4E8\">: [<\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">}<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pd.DataFrame(data)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Convert categorical variables into numerical ones<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">df <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pd.get_dummies(df, <\/span><span style=\"color: #FFAB70\">columns<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">[<\/span><span style=\"color: #9ECBFF\">&#39;Price&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Maintenance&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #9ECBFF\">&#39;Airbag&#39;<\/span><span style=\"color: #E1E4E8\">])<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Separate features and target variable<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> df.drop(<\/span><span style=\"color: #9ECBFF\">&#39;Profitable&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">axis<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">y <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> df[<\/span><span style=\"color: #9ECBFF\">&#39;Profitable&#39;<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Split the data into training and testing sets<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X_train, X_test, y_train, y_test <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> train_test_split(X, y, <\/span><span style=\"color: #FFAB70\">test_size<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">0.2<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">random_state<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">42<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Create a decision tree classifier<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">clf <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> DecisionTreeClassifier(<\/span><span style=\"color: #FFAB70\">criterion<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;entropy&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Train the classifier on the training data<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">clf.fit(X_train, y_train)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Predict on the testing data<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">y_pred <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> clf.predict(X_test)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Calculate accuracy<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">accuracy <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> accuracy_score(y_test, y_pred)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Accuracy:&quot;<\/span><span style=\"color: #E1E4E8\">, accuracy)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Visualize the decision tree<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">dot_data <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> StringIO()<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">export_graphviz(clf, <\/span><span style=\"color: #FFAB70\">out_file<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">dot_data, <\/span><span style=\"color: #FFAB70\">filled<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">rounded<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">special_characters<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">True<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">feature_names<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">X.columns)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">graph <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> pydotplus.graph_from_dot_data(dot_data.getvalue())  <\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">Image(graph.create_png())<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-b7b55144352b299c76c8ce51d96b3bba\"><a href=\"https:\/\/drive.google.com\/file\/d\/1nIbHQHAPR6hd-JEx65YKmByE4UwPTJP1\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M4P1 Program\">M4P1 Program<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Output<\/h3>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"Accuracy: 0.6666666666666666\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #B392F0\">Accuracy<\/span><span style=\"color: #E1E4E8\">: <\/span><span style=\"color: #79B8FF\">0.6666666666666666<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/download.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"609\" height=\"775\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/download.png?resize=609%2C775&#038;ssl=1\" alt=\"\" class=\"wp-image-2135 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/download.png?w=609&amp;ssl=1 609w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/download.png?resize=236%2C300&amp;ssl=1 236w\" data-sizes=\"(max-width: 609px) 100vw, 609px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 609px; --smush-placeholder-aspect-ratio: 609\/775;\" \/><\/a><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Question 2<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m4p2\">Clustering<\/h3>\n\n\n\n<p>Consider the dataset spiral.txt (https:\/\/bit.ly\/2Lm75Ly). The first two columns in the dataset corresponds to the co-ordinates of each data point. The third column corresponds to the actual cluster label. Compute the rand index for the following methods:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>K \u2013 means Clustering<\/li>\n\n\n\n<li>Single \u2013 link Hierarchical Clustering<\/li>\n\n\n\n<li>Complete link hierarchical clustering.<\/li>\n\n\n\n<li>Also visualize the dataset and which algorithm will be able to recover the true clusters.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Python Code<\/h3>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"import numpy as np\nfrom sklearn.cluster import KMeans, AgglomerativeClustering\nfrom sklearn.metrics import adjusted_rand_score\nimport matplotlib.pyplot as plt\n\n# Load the dataset\ndata = np.loadtxt(&quot;Spiral.txt&quot;, delimiter=&quot;,&quot;, skiprows=1)\nX = data[:, :2]  # Features\ny_true = data[:, 2]  # Actual cluster labels\n\n# Visualize the dataset\nplt.figure(figsize=(8, 6))\nplt.scatter(X[:, 0], X[:, 1], c=y_true, cmap='viridis')\nplt.title('True Clusters')\nplt.xlabel('X1')\nplt.ylabel('X2')\nplt.show()\n\n# K-means clustering\n# kmeans = KMeans(n_clusters=3, random_state=42)\nkmeans = KMeans(n_clusters=3, random_state=42, n_init=10)\nkmeans_clusters = kmeans.fit_predict(X)\n\n# Single-link Hierarchical Clustering\nsingle_link = AgglomerativeClustering(n_clusters=3, linkage='single')\nsingle_link_clusters = single_link.fit_predict(X)\n\n# Complete-link Hierarchical Clustering\ncomplete_link = AgglomerativeClustering(n_clusters=3, linkage='complete')\ncomplete_link_clusters = complete_link.fit_predict(X)\n\n# Compute the Rand Index\nrand_index_kmeans = adjusted_rand_score(y_true, kmeans_clusters)\nrand_index_single_link = adjusted_rand_score(y_true, single_link_clusters)\nrand_index_complete_link = adjusted_rand_score(y_true, complete_link_clusters)\n\nprint(&quot;Rand Index for K-means Clustering:&quot;, rand_index_kmeans)\nprint(&quot;Rand Index for Single-link Hierarchical Clustering:&quot;, rand_index_single_link)\nprint(&quot;Rand Index for Complete-link Hierarchical Clustering:&quot;, rand_index_complete_link)\n\n# This code will compute the Rand Index for each clustering method and provide a visualization of the true clusters.\n# The Rand Index ranges from 0 to 1, where 1 indicates perfect clustering agreement with the true clusters. \n# The method with a higher Rand Index is better at recovering the true clusters.\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> numpy <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> np<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.cluster <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> KMeans, AgglomerativeClustering<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> sklearn.metrics <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> adjusted_rand_score<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> matplotlib.pyplot <\/span><span style=\"color: #F97583\">as<\/span><span style=\"color: #E1E4E8\"> plt<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Load the dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">data <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> np.loadtxt(<\/span><span style=\"color: #9ECBFF\">&quot;Spiral.txt&quot;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">delimiter<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&quot;,&quot;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">skiprows<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">X <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> data[:, :<\/span><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">]  <\/span><span style=\"color: #6A737D\"># Features<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">y_true <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> data[:, <\/span><span style=\"color: #79B8FF\">2<\/span><span style=\"color: #E1E4E8\">]  <\/span><span style=\"color: #6A737D\"># Actual cluster labels<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Visualize the dataset<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.figure(<\/span><span style=\"color: #FFAB70\">figsize<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #79B8FF\">8<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #79B8FF\">6<\/span><span style=\"color: #E1E4E8\">))<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.scatter(X[:, <\/span><span style=\"color: #79B8FF\">0<\/span><span style=\"color: #E1E4E8\">], X[:, <\/span><span style=\"color: #79B8FF\">1<\/span><span style=\"color: #E1E4E8\">], <\/span><span style=\"color: #FFAB70\">c<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\">y_true, <\/span><span style=\"color: #FFAB70\">cmap<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;viridis&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.title(<\/span><span style=\"color: #9ECBFF\">&#39;True Clusters&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.xlabel(<\/span><span style=\"color: #9ECBFF\">&#39;X1&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.ylabel(<\/span><span style=\"color: #9ECBFF\">&#39;X2&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">plt.show()<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># K-means clustering<\/span><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># kmeans = KMeans(n_clusters=3, random_state=42)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">kmeans <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> KMeans(<\/span><span style=\"color: #FFAB70\">n_clusters<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">random_state<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">42<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">n_init<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">10<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">kmeans_clusters <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> kmeans.fit_predict(X)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Single-link Hierarchical Clustering<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">single_link <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> AgglomerativeClustering(<\/span><span style=\"color: #FFAB70\">n_clusters<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">linkage<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;single&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">single_link_clusters <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> single_link.fit_predict(X)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Complete-link Hierarchical Clustering<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">complete_link <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> AgglomerativeClustering(<\/span><span style=\"color: #FFAB70\">n_clusters<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #79B8FF\">3<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">linkage<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;complete&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">complete_link_clusters <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> complete_link.fit_predict(X)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Compute the Rand Index<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">rand_index_kmeans <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> adjusted_rand_score(y_true, kmeans_clusters)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">rand_index_single_link <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> adjusted_rand_score(y_true, single_link_clusters)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">rand_index_complete_link <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> adjusted_rand_score(y_true, complete_link_clusters)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Rand Index for K-means Clustering:&quot;<\/span><span style=\"color: #E1E4E8\">, rand_index_kmeans)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Rand Index for Single-link Hierarchical Clustering:&quot;<\/span><span style=\"color: #E1E4E8\">, rand_index_single_link)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Rand Index for Complete-link Hierarchical Clustering:&quot;<\/span><span style=\"color: #E1E4E8\">, rand_index_complete_link)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># This code will compute the Rand Index for each clustering method and provide a visualization of the true clusters.<\/span><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># The Rand Index ranges from 0 to 1, where 1 indicates perfect clustering agreement with the true clusters. <\/span><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># The method with a higher Rand Index is better at recovering the true clusters.<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-88f01625c5e614ee55fe18806c4fbd77\"><a href=\"https:\/\/drive.google.com\/file\/d\/1A9v0CKiFuGRD9kN6XuXShWwpOu0PPyBe\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M4P2 Program\">M4P2 Program<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Output<\/h3>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-9.png?ssl=1\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"749\" height=\"607\" data-src=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-9.png?resize=749%2C607&#038;ssl=1\" alt=\"\" class=\"wp-image-2141 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-9.png?w=749&amp;ssl=1 749w, https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/image-9.png?resize=300%2C243&amp;ssl=1 300w\" data-sizes=\"(max-width: 749px) 100vw, 749px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 749px; --smush-placeholder-aspect-ratio: 749\/607;\" \/><\/a><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"aioseo-module-5\">Module 5<\/h2>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\" id=\"m5p1\">Mini Project<\/h3>\n\n\n\n<p class=\"has-text-align-justify\">Simple web scrapping in social media<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Python Code<\/h4>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#24292e\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"import requests\nfrom bs4 import BeautifulSoup\n\n# URL of the Instagram profile you want to scrape\nurl = 'https:\/\/www.instagram.com\/openai\/'\n\n# Send a GET request to the URL\nresponse = requests.get(url)\n\nprint(response.status_code)\n\n# Check if the request was successful (status code 200)\nif response.status_code == 200:\n    # Parse the HTML content of the page\n    soup = BeautifulSoup(response.text, 'html.parser')\n\n    # Find all post elements\n    posts = soup.find_all('div', class_='v1Nh3')\n\n    # Extract data from each post\n    for post in posts:\n        print(&quot;Hi&quot;)\n        # Extract post link\n        post_link = post.find('a')['href']\n\n        # Extract post image URL\n        image_url = post.find('img')['src']\n\n        print(f&quot;Post Link: {post_link}&quot;)\n        print(f&quot;Image URL: {image_url}&quot;)\n        print(&quot;------&quot;)\nelse:\n    print(&quot;Failed to retrieve data from Instagram&quot;)\n\" style=\"color:#e1e4e8;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki github-dark\" style=\"background-color: #24292e\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> requests<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">from<\/span><span style=\"color: #E1E4E8\"> bs4 <\/span><span style=\"color: #F97583\">import<\/span><span style=\"color: #E1E4E8\"> BeautifulSoup<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># URL of the Instagram profile you want to scrape<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">url <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #9ECBFF\">&#39;https:\/\/www.instagram.com\/openai\/&#39;<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Send a GET request to the URL<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">response <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> requests.get(url)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(response.status_code)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #6A737D\"># Check if the request was successful (status code 200)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">if<\/span><span style=\"color: #E1E4E8\"> response.status_code <\/span><span style=\"color: #F97583\">==<\/span><span style=\"color: #E1E4E8\"> <\/span><span style=\"color: #79B8FF\">200<\/span><span style=\"color: #E1E4E8\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #6A737D\"># Parse the HTML content of the page<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    soup <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> BeautifulSoup(response.text, <\/span><span style=\"color: #9ECBFF\">&#39;html.parser&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #6A737D\"># Find all post elements<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    posts <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> soup.find_all(<\/span><span style=\"color: #9ECBFF\">&#39;div&#39;<\/span><span style=\"color: #E1E4E8\">, <\/span><span style=\"color: #FFAB70\">class_<\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #9ECBFF\">&#39;v1Nh3&#39;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #6A737D\"># Extract data from each post<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #F97583\">for<\/span><span style=\"color: #E1E4E8\"> post <\/span><span style=\"color: #F97583\">in<\/span><span style=\"color: #E1E4E8\"> posts:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Hi&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #6A737D\"># Extract post link<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        post_link <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> post.find(<\/span><span style=\"color: #9ECBFF\">&#39;a&#39;<\/span><span style=\"color: #E1E4E8\">)[<\/span><span style=\"color: #9ECBFF\">&#39;href&#39;<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #6A737D\"># Extract post image URL<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        image_url <\/span><span style=\"color: #F97583\">=<\/span><span style=\"color: #E1E4E8\"> post.find(<\/span><span style=\"color: #9ECBFF\">&#39;img&#39;<\/span><span style=\"color: #E1E4E8\">)[<\/span><span style=\"color: #9ECBFF\">&#39;src&#39;<\/span><span style=\"color: #E1E4E8\">]<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #F97583\">f<\/span><span style=\"color: #9ECBFF\">&quot;Post Link: <\/span><span style=\"color: #79B8FF\">{<\/span><span style=\"color: #E1E4E8\">post_link<\/span><span style=\"color: #79B8FF\">}<\/span><span style=\"color: #9ECBFF\">&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #F97583\">f<\/span><span style=\"color: #9ECBFF\">&quot;Image URL: <\/span><span style=\"color: #79B8FF\">{<\/span><span style=\"color: #E1E4E8\">image_url<\/span><span style=\"color: #79B8FF\">}<\/span><span style=\"color: #9ECBFF\">&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">        <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;------&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F97583\">else<\/span><span style=\"color: #E1E4E8\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #E1E4E8\">    <\/span><span style=\"color: #79B8FF\">print<\/span><span style=\"color: #E1E4E8\">(<\/span><span style=\"color: #9ECBFF\">&quot;Failed to retrieve data from Instagram&quot;<\/span><span style=\"color: #E1E4E8\">)<\/span><\/span>\n<span class=\"line\"><\/span><\/code><\/pre><\/div>\n\n\n\n<p>To run this program online click on the link below and use Google Colab to run this program<\/p>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-d1530ac48743e715921e685fd73e723e\"><a href=\"https:\/\/drive.google.com\/file\/d\/1LnAGwCu34-nvu_CEGwbMVfJWLvqhsCF5\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\" title=\"M5P1 Program\">M5P1 Program<\/a><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>If you are also looking for other Lab Manuals, head over to my following blog :<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-myblogosphere wp-block-embed-myblogosphere\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"P5NvHWqO1q\"><a href=\"https:\/\/moodle.sit.ac.in\/blog\/vtu-lab-manuals-using-foss\/\">VTU Lab Manuals using FOSS<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;VTU Lab Manuals using FOSS&#8221; &#8212; MyBlogosphere\" data-src=\"https:\/\/moodle.sit.ac.in\/blog\/vtu-lab-manuals-using-foss\/embed\/#?secret=oHtQC05vGP#?secret=P5NvHWqO1q\" data-secret=\"P5NvHWqO1q\" width=\"500\" height=\"282\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n","protected":false},"excerpt":{"rendered":"<p>In this blog post, you will find solutions for the Data Science And Its Applications (21AD62) course work for the VI semester of VTU university. To follow along, you will need to set up a Python programming environment. We recommend using the Anaconda Python Distribution with Jupyter Notebook\/ Spyder as the integrated development environment (IDE). [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2157,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"site-container-style":"default","site-container-layout":"default","site-sidebar-layout":"default","disable-article-header":"default","disable-site-header":"default","disable-site-footer":"default","disable-content-area-spacing":"default","_jetpack_memberships_contains_paid_content":false,"footnotes":"[]","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[341,345,343,78,346,342,26,142,152,35,141,77,147,146,40,150,148,81,344,43],"class_list":["post-1867","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-programming","tag-21ad62","tag-6th-semester","tag-anaconda-python-distribution","tag-cse","tag-data-science","tag-data-science-and-its-applications","tag-foss","tag-histogram","tag-jupyter-notebook","tag-lab-manual","tag-linear-plot","tag-matplotlib","tag-numpy","tag-pandas","tag-python","tag-python-program","tag-solution-manual","tag-spyder","tag-vi-semester","tag-vtu"],"aioseo_notices":[],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/moodle.sit.ac.in\/blog\/wp-content\/uploads\/2024\/04\/download.jpeg?fit=226%2C223&ssl=1","jetpack-related-posts":[],"jetpack_sharing_enabled":true,"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/posts\/1867","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/comments?post=1867"}],"version-history":[{"count":37,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/posts\/1867\/revisions"}],"predecessor-version":[{"id":2292,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/posts\/1867\/revisions\/2292"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/media\/2157"}],"wp:attachment":[{"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/media?parent=1867"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/categories?post=1867"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/moodle.sit.ac.in\/blog\/wp-json\/wp\/v2\/tags?post=1867"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}