{"id":50767,"date":"2025-06-02T16:04:38","date_gmt":"2025-06-02T10:34:38","guid":{"rendered":"https:\/\/www.iquanta.in\/blog\/?p=50767"},"modified":"2025-06-02T16:04:40","modified_gmt":"2025-06-02T10:34:40","slug":"linear-regression-explained-a-beginners-guide-with-python-code","status":"publish","type":"post","link":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/","title":{"rendered":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code"},"content":{"rendered":"\n<p>Linear Regression is one of the most basic and important concepts in data science and <a href=\"https:\/\/www.iquanta.in\/blog\/what-is-machine-learning-an-introduction-for-beginners\/\">machine learning<\/a>. But do not worry because it is not as scary as it sounds. Think of it like this that you are trying to find a straight line that best fits a bunch of dots on a graph. That line helps you predict future values based on past data.<\/p>\n\n\n\n<p>For example if you know how many hours you studied and the marks you scored then linear regression can help you guess your marks next time based on study hours. It is used in real life to predict prices sales temperatures and many other things.<\/p>\n\n\n\n<p>In this blog we will explain Linear Regression in a super simple way. We will talk about what it is how it works some easy math and how to do it using Python. No complicated formulas just clear and easy steps. Let us begin<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/chat.whatsapp.com\/B6weknl7133BQXjPva0pgB\"><img fetchpriority=\"high\" decoding=\"async\" width=\"864\" height=\"129\" src=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123.png\" alt=\"Linear Regression\" class=\"wp-image-49677\" srcset=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123.png 864w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123-300x45.png 300w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123-768x115.png 768w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123-150x22.png 150w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/05\/image-123-696x104.png 696w\" sizes=\"(max-width: 864px) 100vw, 864px\" \/><\/a><\/figure><\/div>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_77 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#What_is_Linear_Regression\" >What is Linear Regression?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Mathematics_Behind_Linear_Regression\" >Mathematics Behind Linear Regression?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Assumptions_of_Linear_Regression\" >Assumptions of Linear Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Types_of_Linear_Regression\" >Types of Linear Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Linear_Regression_in_Python\" >Linear Regression in Python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Advantages_of_Linear_Regression\" >Advantages of Linear Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Applications_of_Linear_Regression\" >Applications of Linear Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-linear-regression\"><span class=\"ez-toc-section\" id=\"What_is_Linear_Regression\"><\/span><strong>What is Linear Regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Linear Regression is a method used to find the relationship between two things. One thing is the input and the other is the output. For example if you study more hours you may score more marks. So here study hours is the input and marks is the output.<\/p>\n\n\n\n<p>Linear Regression helps us draw a straight line that fits the data. This line shows the trend between the input and output. Once we have this line we can use it to make predictions. For example if you studied for 5 hours the line can help predict your marks.<\/p>\n\n\n\n<p>It is called linear because the line is straight. It works best when the data follows a straight pattern. If the data is all over the place or forms a curve then this method may not work well. In short Linear Regression helps us make smart guesses based on past data using a straight line.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-mathematics-behind-linear-regression\"><span class=\"ez-toc-section\" id=\"Mathematics_Behind_Linear_Regression\"><\/span><strong>Mathematics Behind Linear Regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The main idea of Linear Regression is to find a line that best fits your data points. The line helps predict one value from another.<\/p>\n\n\n\n<p>The equation of this line looks like this:<\/p>\n\n\n\n<p><strong>y = mx + c<\/strong><\/p>\n\n\n\n<p>Here,<\/p>\n\n\n\n<ul>\n<li>y is the value we want to predict<\/li>\n\n\n\n<li>x is the input value<\/li>\n\n\n\n<li>m is the slope of the line (how steep the line is)<\/li>\n\n\n\n<li>c is the point where the line crosses the y-axis (called the intercept)<\/li>\n<\/ul>\n\n\n\n<p>The goal is to find the best values of m and c so the line is as close as possible to all the points in your data.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/chat.whatsapp.com\/B6weknl7133BQXjPva0pgB\"><img decoding=\"async\" width=\"864\" height=\"129\" src=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image.png\" alt=\"\" class=\"wp-image-50795\" srcset=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image.png 864w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-300x45.png 300w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-768x115.png 768w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-150x22.png 150w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-696x104.png 696w\" sizes=\"(max-width: 864px) 100vw, 864px\" \/><\/a><\/figure><\/div>\n\n\n<p>To measure how close the line is to the points we use something called the Mean Squared Error. This means we look at the distance between the actual points and the line and try to make that distance as small as possible. We use a method called Gradient Descent to help find the best slope and intercept by improving guesses step by step.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-assumptions-of-linear-regression\"><span class=\"ez-toc-section\" id=\"Assumptions_of_Linear_Regression\"><\/span><strong>Assumptions of Linear Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>When we use Linear Regression there are some important things we assume to be true. These assumptions help make sure our predictions are good.<\/p>\n\n\n\n<ol>\n<li>Linearity<br>This means the relationship between input and output should be a straight line. If it is not straight then linear regression might not work well.<\/li>\n\n\n\n<li>Independence<br>The data points should be independent of each other. One data point should not affect another.<\/li>\n\n\n\n<li>Homoscedasticity<br>This is a big word that means the spread of errors or differences between the actual and predicted values should be the same across all inputs.<\/li>\n\n\n\n<li>Normality<br>The errors or differences between the actual and predicted values should follow a normal distribution or bell curve.<\/li>\n\n\n\n<li>No multicollinearity<br>If you have many inputs then they should not be too closely related to each other. If they are very similar it can confuse the model. These assumptions help linear regression give better and more reliable results.<\/li>\n<\/ol>\n\n\n\n<p class=\"has-text-align-center\"><a href=\"https:\/\/chat.whatsapp.com\/B6weknl7133BQXjPva0pgB\"><img decoding=\"async\" src=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image.png\" alt=\"This image has an empty alt attribute; its file name is image.png\"><\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-types-of-linear-regression\"><span class=\"ez-toc-section\" id=\"Types_of_Linear_Regression\"><\/span><strong>Types of Linear Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>There are mainly two types of Linear Regression:<\/p>\n\n\n\n<ol>\n<li>Simple Linear Regression<br>This is when you have only one input variable to predict the output. For example predicting marks based on hours studied.<\/li>\n\n\n\n<li>Multiple Linear Regression<br>This is when you use more than one input variable to predict the output. For example predicting house price based on size number of rooms and location.<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Type of Linear Regression<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>Description<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>Example<\/strong><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Simple Linear Regression<\/td><td class=\"has-text-align-center\" data-align=\"center\">Uses one input variable to predict the output<\/td><td class=\"has-text-align-center\" data-align=\"center\">Predicting marks based on hours studied<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Multiple Linear Regression<\/td><td class=\"has-text-align-center\" data-align=\"center\">Uses two or more input variables to predict the output<\/td><td class=\"has-text-align-center\" data-align=\"center\">Predicting house price based on size and rooms<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Ridge Regression<\/td><td class=\"has-text-align-center\" data-align=\"center\">Adds a penalty to reduce complexity and avoid overfitting when input variables are many or related<\/td><td class=\"has-text-align-center\" data-align=\"center\">Handling many related features in data<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">Lasso Regression<\/td><td class=\"has-text-align-center\" data-align=\"center\">Adds a penalty to shrink some input variables to zero for feature selection<\/td><td class=\"has-text-align-center\" data-align=\"center\">Selecting important features by ignoring less useful ones<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-linear-regression-in-python\"><span class=\"ez-toc-section\" id=\"Linear_Regression_in_Python\"><\/span><strong>Linear Regression in Python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.linear_model import LinearRegression\r\nimport numpy as np\r\n\r\nX = np.array(&#091;&#091;1], &#091;2], &#091;3], &#091;4], &#091;5]])\r\ny = np.array(&#091;50, 55, 65, 70, 75])\r\n\r\nmodel = LinearRegression()\r\nmodel.fit(X, y)\r\n\r\nhours = np.array(&#091;&#091;6]])\r\npredicted_marks = model.predict(hours)\r\n\r\nprint(f\"Predicted marks for studying 6 hours: {predicted_marks&#091;0]:.2f}\")\r<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-advantages-of-linear-regression\"><span class=\"ez-toc-section\" id=\"Advantages_of_Linear_Regression\"><\/span><strong>Advantages of Linear Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ol>\n<li>Linear Regression is simple to understand and easy to use for beginners.<\/li>\n\n\n\n<li>It trains very fast even when working with large amounts of data.<\/li>\n\n\n\n<li>It gives accurate predictions when the data shows a clear straight line relationship.<\/li>\n\n\n\n<li>The model provides clear insights about how input features affect the output.<\/li>\n\n\n\n<li>It is often used as a basic starting point before moving to more complex models.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-applications-of-linear-regression\"><span class=\"ez-toc-section\" id=\"Applications_of_Linear_Regression\"><\/span><strong>Applications of Linear Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ol>\n<li>Linear regression helps estimate the price of a house based on size location and number of rooms.<\/li>\n\n\n\n<li>Businesses use it to predict future sales based on past sales data and market trends.<\/li>\n\n\n\n<li>It can help forecast temperature or rainfall based on historical weather data.<\/li>\n\n\n\n<li>Doctors use it to find relationships between lifestyle habits and health outcomes like blood pressure.<\/li>\n\n\n\n<li>Investors use linear regression to predict stock prices based on past performance and economic indicators.<\/li>\n<\/ol>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/chat.whatsapp.com\/B6weknl7133BQXjPva0pgB\"><img decoding=\"async\" width=\"864\" height=\"129\" src=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1.png\" alt=\"\" class=\"wp-image-50796\" srcset=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1.png 864w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1-300x45.png 300w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1-768x115.png 768w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1-150x22.png 150w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-1-696x104.png 696w\" sizes=\"(max-width: 864px) 100vw, 864px\" \/><\/a><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Linear Regression is a basic yet powerful tool to understand relationships between data points. It helps us draw a straight line that best fits the data and use it to make predictions. Because it is easy to use and fast it is widely used in many fields like business medicine weather and finance.<\/p>\n\n\n\n<p>Learning Linear Regression gives you a strong foundation to explore more advanced machine learning techniques. With practice you can apply it using programming languages like Python and solve real world problems. Remember that it works best when data shows a straight line pattern and the assumptions are met. If not you may need to try other methods. Overall Linear Regression is a great starting point for anyone interested in data analysis and prediction.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/chat.whatsapp.com\/B6weknl7133BQXjPva0pgB\"><img loading=\"lazy\" decoding=\"async\" width=\"864\" height=\"129\" src=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2.png\" alt=\"\" class=\"wp-image-50797\" srcset=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2.png 864w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2-300x45.png 300w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2-768x115.png 768w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2-150x22.png 150w, https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2-696x104.png 696w\" sizes=\"(max-width: 864px) 100vw, 864px\" \/><\/a><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><strong>Frequently Asked Questions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>What is Linear Regression?<\/strong><br>Linear Regression is a method to find the best straight line that predicts one value from another.<\/p>\n\n\n\n<p><strong>When should I use Linear Regression?<\/strong><br>Use it when your data shows a straight line relationship between input and output.<\/p>\n\n\n\n<p><strong>Can Linear Regression handle multiple inputs?<\/strong><br>Yes Multiple Linear Regression can use two or more inputs to predict the output.<\/p>\n\n\n\n<p><strong>Is Linear Regression good for all data types?<\/strong><br>No It works best when data has a linear pattern If data is curved or very complex other methods are better.<\/p>\n\n\n\n<p><strong>Which language is best for Linear Regression?<\/strong><br>Python is very popular because it has easy libraries like scikit-learn to build Linear Regression models quickly.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Linear Regression is one of the most basic and important concepts in data science and machine learning. But do not worry because it is not as scary as it sounds. Think of it like this that you are trying to find a straight line that best fits a bunch of dots on a graph. That [&hellip;]<\/p>\n","protected":false},"author":560,"featured_media":50790,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1074,1073],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.4 (Yoast SEO v21.9.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Linear Regression Explained: A Beginner\u2019s Guide with Python Code - iQuanta<\/title>\n<meta name=\"description\" content=\"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Linear Regression Explained: A Beginner\u2019s Guide with Python Code\" \/>\n<meta property=\"og:description\" content=\"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\" \/>\n<meta property=\"og:site_name\" content=\"iQuanta\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/facebook.com\/iquanta.in\" \/>\n<meta property=\"article:published_time\" content=\"2025-06-02T10:34:38+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-02T10:34:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/Your-paragraph-text-30.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1600\" \/>\n\t<meta property=\"og:image:height\" content=\"900\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Nidhi Goswami\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Nidhi Goswami\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\"},\"author\":{\"name\":\"Nidhi Goswami\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/ec8c8c25d0526dd86557b6fed064f7f3\"},\"headline\":\"Linear Regression Explained: A Beginner\u2019s Guide with Python Code\",\"datePublished\":\"2025-06-02T10:34:38+00:00\",\"dateModified\":\"2025-06-02T10:34:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\"},\"wordCount\":1188,\"publisher\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/#organization\"},\"articleSection\":[\"Data Analytics\",\"iSkills\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\",\"url\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\",\"name\":\"Linear Regression Explained: A Beginner\u2019s Guide with Python Code - iQuanta\",\"isPartOf\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/#website\"},\"datePublished\":\"2025-06-02T10:34:38+00:00\",\"dateModified\":\"2025-06-02T10:34:40+00:00\",\"description\":\"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.iquanta.in\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Linear Regression Explained: A Beginner\u2019s Guide with Python Code\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#website\",\"url\":\"https:\/\/www.iquanta.in\/blog\/\",\"name\":\"iQuanta | Cat Preparation Online\",\"description\":\"Building Learning Networks\",\"publisher\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.iquanta.in\/blog\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#organization\",\"name\":\"IQuanta\",\"url\":\"https:\/\/www.iquanta.in\/blog\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2018\/08\/IQuanta-1.png\",\"contentUrl\":\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2018\/08\/IQuanta-1.png\",\"width\":525,\"height\":200,\"caption\":\"IQuanta\"},\"image\":{\"@id\":\"https:\/\/www.iquanta.in\/blog\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/facebook.com\/iquanta.in\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/ec8c8c25d0526dd86557b6fed064f7f3\",\"name\":\"Nidhi Goswami\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/21d234d87afd924b217d26b25a3cf1ee?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/21d234d87afd924b217d26b25a3cf1ee?s=96&d=mm&r=g\",\"caption\":\"Nidhi Goswami\"},\"url\":\"https:\/\/www.iquanta.in\/blog\/author\/nidhigoswami\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code - iQuanta","description":"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/","og_locale":"en_US","og_type":"article","og_title":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code","og_description":"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.","og_url":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/","og_site_name":"iQuanta","article_publisher":"https:\/\/facebook.com\/iquanta.in","article_published_time":"2025-06-02T10:34:38+00:00","article_modified_time":"2025-06-02T10:34:40+00:00","og_image":[{"width":1600,"height":900,"url":"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/Your-paragraph-text-30.jpg","type":"image\/jpeg"}],"author":"Nidhi Goswami","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Nidhi Goswami","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#article","isPartOf":{"@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/"},"author":{"name":"Nidhi Goswami","@id":"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/ec8c8c25d0526dd86557b6fed064f7f3"},"headline":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code","datePublished":"2025-06-02T10:34:38+00:00","dateModified":"2025-06-02T10:34:40+00:00","mainEntityOfPage":{"@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/"},"wordCount":1188,"publisher":{"@id":"https:\/\/www.iquanta.in\/blog\/#organization"},"articleSection":["Data Analytics","iSkills"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/","url":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/","name":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code - iQuanta","isPartOf":{"@id":"https:\/\/www.iquanta.in\/blog\/#website"},"datePublished":"2025-06-02T10:34:38+00:00","dateModified":"2025-06-02T10:34:40+00:00","description":"Learn Linear Regression basics with easy explanations math examples and Python code. Predict data trends simply.","breadcrumb":{"@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.iquanta.in\/blog\/linear-regression-explained-a-beginners-guide-with-python-code\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.iquanta.in\/blog\/"},{"@type":"ListItem","position":2,"name":"Linear Regression Explained: A Beginner\u2019s Guide with Python Code"}]},{"@type":"WebSite","@id":"https:\/\/www.iquanta.in\/blog\/#website","url":"https:\/\/www.iquanta.in\/blog\/","name":"iQuanta | Cat Preparation Online","description":"Building Learning Networks","publisher":{"@id":"https:\/\/www.iquanta.in\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.iquanta.in\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.iquanta.in\/blog\/#organization","name":"IQuanta","url":"https:\/\/www.iquanta.in\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.iquanta.in\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2018\/08\/IQuanta-1.png","contentUrl":"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2018\/08\/IQuanta-1.png","width":525,"height":200,"caption":"IQuanta"},"image":{"@id":"https:\/\/www.iquanta.in\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/facebook.com\/iquanta.in"]},{"@type":"Person","@id":"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/ec8c8c25d0526dd86557b6fed064f7f3","name":"Nidhi Goswami","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.iquanta.in\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/21d234d87afd924b217d26b25a3cf1ee?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/21d234d87afd924b217d26b25a3cf1ee?s=96&d=mm&r=g","caption":"Nidhi Goswami"},"url":"https:\/\/www.iquanta.in\/blog\/author\/nidhigoswami\/"}]}},"_links":{"self":[{"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/posts\/50767"}],"collection":[{"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/comments?post=50767"}],"version-history":[{"count":8,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/posts\/50767\/revisions"}],"predecessor-version":[{"id":50799,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/posts\/50767\/revisions\/50799"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/media\/50790"}],"wp:attachment":[{"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/media?parent=50767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/categories?post=50767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iquanta.in\/blog\/wp-json\/wp\/v2\/tags?post=50767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}