{"id":398,"date":"2024-11-10T14:55:58","date_gmt":"2024-11-10T14:55:58","guid":{"rendered":"https:\/\/techkubo.com\/python\/?p=398"},"modified":"2025-06-20T17:59:41","modified_gmt":"2025-06-20T17:59:41","slug":"machine-learning-mean-median-and-mode","status":"publish","type":"post","link":"https:\/\/techkubo.com\/python\/machine-learning-mean-median-and-mode\/","title":{"rendered":"Machine Learning &#8211; Mean, Median, and Mode"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Mean, Median, and Mode? What are these?<\/h2>\n\n\n\n<p>In Machine Learning, statistical measures like <strong>Mean, Median, and Mode <\/strong>are used to summarize and analyze data. These measures help us understand the distribution of values in a data set and identify patterns which is essential in predicting future outcomes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Key Concepts:<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Mean: <\/strong>The average value of all the numbers.<\/li>\n\n\n\n<li><strong>Median: <\/strong>The middle value when the numbers are sorted in order.<\/li>\n\n\n\n<li><strong>Mode: <\/strong>The value that appears most frequently.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Example: Jeepney Travel Speeds<\/h2>\n\n\n\n<p>Let&#8217;s say we have recorded the travel speeds (in kilometers per hour) of  10 jeepneys in a busy area in Metro Manila: <br><br><strong>speed = [20, 18, 25, 22, 28, 19, 30, 25, 27, 25]<\/strong><br><br>Our goal is to calculate the <strong>average speed, <\/strong>the <strong>middle speed, <\/strong>and the most <strong>common speed.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 1: Calculating the Mean<\/h2>\n\n\n\n<p>The <strong>mean <\/strong>is the sum of all the values divided by the total number of values. It gives us the average.<\/p>\n\n\n\n<p><strong>Formula: <\/strong><br><br>Mean = (<strong>20 + 18 + 25 + 22 + 28 + 19 + 30 + 25 + 27 + 25<\/strong>) \/ <strong>10<\/strong> = <strong>23.9 <\/strong><br><br>In Python, you can calculate the mean using the <strong>NumPy <\/strong>module:<\/p>\n\n\n\n<div class=\"wp-block-codemirror-blocks-code-block code-block\"><pre class=\"CodeMirror\" data-setting=\"{&quot;showPanel&quot;:true,&quot;languageLabel&quot;:&quot;language&quot;,&quot;fullScreenButton&quot;:true,&quot;copyButton&quot;:true,&quot;mode&quot;:&quot;python&quot;,&quot;mime&quot;:&quot;text\/x-python&quot;,&quot;theme&quot;:&quot;seti&quot;,&quot;lineNumbers&quot;:true,&quot;styleActiveLine&quot;:false,&quot;lineWrapping&quot;:false,&quot;readOnly&quot;:true,&quot;fileName&quot;:&quot;&quot;,&quot;language&quot;:&quot;Python&quot;,&quot;maxHeight&quot;:&quot;400px&quot;,&quot;modeName&quot;:&quot;python&quot;}\">import numpy\n\nspeed = [20, 18, 25, 22, 28, 19, 30, 25, 27, 25]\nx = numpy.mean(speed)\n\nprint(x)<\/pre><\/div>\n\n\n\n<p>The average speed is <strong>23.9 km\/h.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 2: Finding the Median<\/h2>\n\n\n\n<p>The <strong>median <\/strong>is the middle value of the data when it is sorted in ascending order. If there&#8217;s an even number of data points the median is the average of the two middle numbers.<br><br><strong>Sorted Data: <\/strong><br><strong>[18, 19, 20, 22, 25, 25, 25, 27, 28, 30]<\/strong><br><br>The <strong>median <\/strong>here is <strong>24 <\/strong>as it is the middle value in the sorted list.<br><br>To calculate the <strong>median <\/strong>in Python you can use the <strong>NumPy <\/strong>module:<\/p>\n\n\n\n<div class=\"wp-block-codemirror-blocks-code-block code-block\"><pre class=\"CodeMirror\" data-setting=\"{&quot;showPanel&quot;:true,&quot;languageLabel&quot;:&quot;language&quot;,&quot;fullScreenButton&quot;:true,&quot;copyButton&quot;:true,&quot;mode&quot;:&quot;python&quot;,&quot;mime&quot;:&quot;text\/x-python&quot;,&quot;theme&quot;:&quot;seti&quot;,&quot;lineNumbers&quot;:true,&quot;styleActiveLine&quot;:false,&quot;lineWrapping&quot;:false,&quot;readOnly&quot;:true,&quot;fileName&quot;:&quot;&quot;,&quot;language&quot;:&quot;Python&quot;,&quot;maxHeight&quot;:&quot;400px&quot;,&quot;modeName&quot;:&quot;python&quot;}\">import numpy\n\nspeed = [20, 18, 25, 22, 28, 19, 30, 25, 27, 25]\nx = numpy.mean(speed)\n\nprint(x)<\/pre><\/div>\n\n\n\n<p>The <strong>median <\/strong>speed is <strong>25 km\/h.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 3: Finding the Mode<\/h2>\n\n\n\n<p>The <strong>mode <\/strong>is the value that appears most frequently in the data set. In this case <strong>25 <\/strong>appears thrice in the list which is more frequent than any other number.<br><br>To calculate the mode in Python, you can use the <strong>SciPy <\/strong>module:<\/p>\n\n\n\n<div class=\"wp-block-codemirror-blocks-code-block code-block\"><pre class=\"CodeMirror\" data-setting=\"{&quot;showPanel&quot;:true,&quot;languageLabel&quot;:&quot;language&quot;,&quot;fullScreenButton&quot;:true,&quot;copyButton&quot;:true,&quot;mode&quot;:&quot;python&quot;,&quot;mime&quot;:&quot;text\/x-python&quot;,&quot;theme&quot;:&quot;seti&quot;,&quot;lineNumbers&quot;:true,&quot;styleActiveLine&quot;:false,&quot;lineWrapping&quot;:false,&quot;readOnly&quot;:true,&quot;fileName&quot;:&quot;&quot;,&quot;language&quot;:&quot;Python&quot;,&quot;maxHeight&quot;:&quot;400px&quot;,&quot;modeName&quot;:&quot;python&quot;}\">from scipy import stats\n\nspeed = [20, 18, 25, 22, 28, 19, 30, 25, 27, 25]\nx = .mean(speed)\n\nprint(x)<\/pre><\/div>\n\n\n\n<p><\/p>\n\n\n\n<p>These statistical concepts are vital in Machine Learning. Understanding how to calculate and interpret the mean, median, and mode will help us better analyze real-world data in every use-case we want to by just simply tracking the jeepney speeds in Metro Manila, to predict store sales, or tasks that are data-driven.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mean, Median, and Mode? What are these? In Machine Learning, statistical measures like Mean, Median, and Mode are used to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-398","post","type-post","status-publish","format-standard","hentry","category-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning - Mean, Median, and Mode - Python Tutorial<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/techkubo.com\/python\/machine-learning-mean-median-and-mode\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning - Mean, Median, and Mode - Python Tutorial\" \/>\n<meta property=\"og:description\" content=\"Mean, Median, and Mode? 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