{"id":182969,"date":"2024-03-13T09:41:00","date_gmt":"2024-03-13T08:41:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=182969"},"modified":"2024-03-12T22:21:15","modified_gmt":"2024-03-12T21:21:15","slug":"linear-regression-with-python-how-does-it-work","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/linear-regression-with-python-how-does-it-work","title":{"rendered":"Linear regression with Python: How does it work?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"182969\" class=\"elementor elementor-182969\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c7daa83 e-flex e-con-boxed e-con\" data-id=\"c7daa83\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-67eace3 elementor-widget elementor-widget-heading\" data-id=\"67eace3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">A key algorithm in Machine Learning, linear regression is used to establish relationships between one or more variables. To put this algorithm into practice with ease, data scientists can turn to programming languages, particularly Python. So how do you use linear regression with Python? DataScientest answers the question.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-057f9bf e-flex e-con-boxed e-con\" data-id=\"057f9bf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-18c670f elementor-widget elementor-widget-heading\" data-id=\"18c670f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is linear regression?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a290101 elementor-widget elementor-widget-text-editor\" data-id=\"a290101\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Before looking at the practical use of <strong>linear regression with Python, we need to go back to basics.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e106de elementor-widget elementor-widget-heading\" data-id=\"4e106de\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Linear regression - Definition<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-18c027e elementor-widget elementor-widget-text-editor\" data-id=\"18c027e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The<strong> linear regression model<\/strong> is a supervised learning algorithm used to predict a continuous target variable (dependent variable) from one or more explanatory variables (independent or predictive variables). In other words, it establishes relationships between 2 or more variables.<\/p><p>When there is only one explanatory variable, we speak of simple linear regression. On the other hand, if there are several, we speak of<strong> multiple linear regression.<\/strong><\/p><p>Whether simple or multiple, linear regression can be used with Python.<\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;PyCharm: all about the most popular Python IDE&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/pycharm-all-about-the-most-popular-python-ide\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/pycharm-all-about-the-most-popular-python-ide\" target=\"_blank\" rel=\"noopener\">PyCharm: all about the most popular Python IDE<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Pandas : the Python library dedicated to Data Science&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/pandas-the-python-library\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/pandas-the-python-library\" target=\"_blank\" rel=\"noopener\">Pandas : the Python library dedicated to Data Science<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Seaborn : all about the Data Visualization tool in Python&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/seaborn_and_data_visualization\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/seaborn_and_data_visualization\" target=\"_blank\" rel=\"noopener\">Seaborn : all about the Data Visualization tool in Python<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;PyWin32: Unveiling the Python Extension Exclusively for Windows Systems&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/pywin32-unveiling-the-python-extension-exclusively-for-windows-systems\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/pywin32-unveiling-the-python-extension-exclusively-for-windows-systems\" target=\"_blank\" rel=\"noopener\">PyWin32: Unveiling the Python Extension Exclusively for Windows Systems<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;SymPy: everything you need to know about the Python symbolic computation library &quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/sympy-everything-you-need-to-know-about-the-python-symbolic-computation-library\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/sympy-everything-you-need-to-know-about-the-python-symbolic-computation-library\" target=\"_blank\" rel=\"noopener\">SymPy: everything you need to know about the Python symbolic computation library <\/a><\/td><\/tr><\/tbody><\/table><p>\u00a0<\/p><p>\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7919425 elementor-widget elementor-widget-heading\" data-id=\"7919425\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Linear regression - mathematical translations<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e3cfe30 elementor-widget elementor-widget-text-editor\" data-id=\"e3cfe30\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The mathematical equation for linear regression is as follows:<\/p><pre style=\"padding-left: 40px;\"><strong><i>Y = \u0398<sub>0<\/sub> + \u0398<sub>1<\/sub>x<sub>1<\/sub> + ... \u0398<sub>n<\/sub>x<sub>n<\/sub><\/i><\/strong><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11f5471 elementor-widget elementor-widget-text-editor\" data-id=\"11f5471\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In this equation :<\/p><ul><li>Y corresponds to the explanatory value ;<\/li><li>\u03b8 corresponds to the bias term or parameter vector;<\/li><li>x1, x2&#8230;, xn correspond to the entity values.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c608aa6 elementor-widget elementor-widget-text-editor\" data-id=\"c608aa6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>From a visual point of view,<strong> linear regression<\/strong> applies when the training data represents a point cloud. In this case, the aim is to identify the straight line that most closely <strong>approximates the set of points.<\/strong><\/p><p>To ensure that this line is as accurate as possible, we measure the mean squared error.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b23a3d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3b23a3d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">All you need to know about linear regression with Python<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9f30bea e-flex e-con-boxed e-con\" data-id=\"9f30bea\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-24efb46 elementor-widget elementor-widget-heading\" data-id=\"24efb46\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Use cases for linear regression<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-38dfc4c elementor-widget elementor-widget-text-editor\" data-id=\"38dfc4c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>If <strong>linear regression<\/strong> is the first<a href=\"https:\/\/datascientest.com\/en\/algorithm-what-is-it\"> algorithm<\/a> used in machine learning, it&#8217;s because it can be applied in so many different ways.<\/p><p><strong>For example:<\/strong><\/p><ul><li>Identify factors influencing the profitability of an investment;<\/li><li>Predict future sales by analyzing past sales;<\/li><li>Anticipate consumer behavior;<\/li><li>Predict the price of a house based on its characteristics;<\/li><li>etc.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6c36bd elementor-widget elementor-widget-text-editor\" data-id=\"f6c36bd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>And for every linear regression application, you can use Python.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f7fc6eb e-flex e-con-boxed e-con\" data-id=\"f7fc6eb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-edaabd8 elementor-widget elementor-widget-heading\" data-id=\"edaabd8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How to use linear regression with Python?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b260cab elementor-widget elementor-widget-text-editor\" data-id=\"b260cab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To explain<strong> linear regression with Python,<\/strong> let&#8217;s take a concrete example. Here&#8217;s the starting hypothesis:<\/p><ul><li><strong>A restaurateur<\/strong> who already owns several restaurants in several cities wants to expand his business by setting up in different locations.<\/li><li><strong>To analyze the next cities in which to set up, the restaurateur has two sets of data at his disposal<\/strong>: the profits made in the cities where he is already established, and the populations of the cities.<\/li><\/ul><p>Since the aim is to make as much profit as possible in the town where he&#8217;s going to set up, he needs to predict the profit made in the town where he&#8217;s going to set up (dependent variable = Y) as a function of its population (independent variable = X).<\/p><p>So how do you evaluate the <strong>linear regression model<\/strong> with Python? Here&#8217;s how.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-00fea2b elementor-widget elementor-widget-heading\" data-id=\"00fea2b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Formatting data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e69580a elementor-widget elementor-widget-text-editor\" data-id=\"e69580a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To<strong> model linear regression with Python<\/strong>, you need to prepare the training data in the right format. Ideally, you should prepare a CSV file with two columns: one for the population (independent variable) and another for the benefits (independent variable). Here&#8217;s what such a file might look like:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff3fec3 elementor-widget elementor-widget-html\" data-id=\"ff3fec3\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style type=\"text\/css\">\r\n.tg  {border-collapse:collapse;border-spacing:0;}\r\n.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\r\n  overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;\r\n  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg .tg-649r{background-color:#efefef;font-family:Tahoma, Geneva, sans-serif !important;font-size:16px;text-align:center;\r\n  vertical-align:top}\r\n.tg .tg-7vx0{font-family:Tahoma, Geneva, sans-serif !important;font-size:16px;font-weight:bold;text-align:center;vertical-align:top}\r\n<\/style>\r\n<table class=\"tg\" style=\"undefined;table-layout: fixed; width: 200px\">\r\n<colgroup>\r\n<col style=\"width: 100px\">\r\n<col style=\"width: 100px\">\r\n<\/colgroup>\r\n<thead>\r\n  <tr>\r\n    <th class=\"tg-7vx0\"><span style=\"font-style:normal;text-decoration:none;color:#000;background-color:transparent\">Population<\/span><\/th>\r\n    <th class=\"tg-7vx0\"><span style=\"font-style:normal;text-decoration:none;color:#000;background-color:transparent\">Profits <\/span><\/th>\r\n  <\/tr>\r\n<\/thead>\r\n<tbody>\r\n  <tr>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">811 000<\/span><\/td>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">175 000 \u20ac<\/span><\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">757 000<\/span><\/td>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">91 300 \u20ac<\/span><\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">551 000<\/span><\/td>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">21 000 \u20ac<\/span><\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">372 000<\/span><\/td>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">- 6 000 \u20ac<\/span><\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">\u2026<\/span><\/td>\r\n    <td class=\"tg-649r\"><span style=\"font-weight:400;font-style:normal;text-decoration:none;color:#000\">\u2026<\/span><\/td>\r\n  <\/tr>\r\n<\/tbody>\r\n<\/table>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e790d2 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"8e790d2\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Linear regression with Python<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e9790f3 elementor-widget elementor-widget-heading\" data-id=\"e9790f3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Load data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c0602d elementor-widget elementor-widget-text-editor\" data-id=\"0c0602d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>This training data must then be loaded into Python. Thanks to the Pandas library, you can easily read CSV files. Here&#8217;s how to do it.<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">import pandas as pd\u00a0<\/span>\n<span style=\"font-weight: 400;\">df=pd.read_csv(\"D:\\DEV\\PYTHON_PROGRAMMING\\donnees-d-entrainement-regression-lineaire.csv\")<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3cd0c6 elementor-widget elementor-widget-text-editor\" data-id=\"d3cd0c6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The read_csv() function returns a two-dimensional array containing the dependent and independent variables. But to use linear regression with Python, you need to separate the two columns into two Python variables.<\/p><p>For the first column corresponding to population size :<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">X = df.iloc[0:len(df),0]<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7b1a2 elementor-widget elementor-widget-text-editor\" data-id=\"cb7b1a2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>For the second column corresponding to profits :<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">Y = df.iloc[0:len(df),1]<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c42ba6 elementor-widget elementor-widget-text-editor\" data-id=\"2c42ba6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>This gives you a simple table containing the entire training data set.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c7bbea4 elementor-widget elementor-widget-heading\" data-id=\"c7bbea4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Visualize data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e623b81 elementor-widget elementor-widget-text-editor\" data-id=\"e623b81\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To better understand linear regression with <a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-language\">Python<\/a>, it can be useful to visualize it. This will enable you to identify points and better understand dispersions.<\/p><p>To obtain a scatter plot, you can use <a href=\"https:\/\/datascientest.com\/en\/matplotlib-master-data-visualization-in-python\">Matplotlib,<\/a> a Python library. Here&#8217;s how to get it:<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">import matplotlib.pyplot as plt<\/span>\n<span style=\"font-weight: 400;\">axes = plt.axes()<\/span>\n<span style=\"font-weight: 400;\">axes.grid()<\/span>\n<span style=\"font-weight: 400;\">plt.scatter(X,Y) <\/span>\n<span style=\"font-weight: 400;\">plt.show()<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20993a0 elementor-widget elementor-widget-heading\" data-id=\"20993a0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Apply the algorithm<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f551306 elementor-widget elementor-widget-text-editor\" data-id=\"f551306\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>From there, the aim is to find a predictive function F(X) with population size as input and expected profits as output.<\/p><p>To model linear regression with Python, the easiest way is to use the Scikit Learn library by typing this query:<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">from sklearn.linear_model import LinearRegression.<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d8d3c01 elementor-widget elementor-widget-text-editor\" data-id=\"d8d3c01\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>From there, you can build your template. Here&#8217;s the code to write:<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">reg = LinearRegression(normalize=True)<\/span>\n<span style=\"font-weight: 400;\">reg.fit(x,y)<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb408da elementor-widget elementor-widget-text-editor\" data-id=\"fb408da\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>And to find the line f(x)=ax+b with minimum squared error, type :<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">a = reg.coef_<\/span>\n<span style=\"font-weight: 400;\">b = reg.intercept.<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-004d964 elementor-widget elementor-widget-heading\" data-id=\"004d964\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Make predictions<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa31eea elementor-widget elementor-widget-text-editor\" data-id=\"aa31eea\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To plot the linear regression curve with Python, simply type the code below:<\/p><pre><span style=\"background: #e2e2e2;\"><span style=\"font-weight: 400;\">ordonne = np.linspace<\/span>\n<span style=\"font-weight: 400;\">plt.scatter(x,y)<\/span>\n<span style=\"font-weight: 400;\">plt.plot(ordonne,a*ordonne+b,color='r')<\/span><\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ec270f4 e-flex e-con-boxed e-con\" data-id=\"ec270f4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-18715ab elementor-widget elementor-widget-heading\" data-id=\"18715ab\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Master linear regression with Python<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1a2af5d elementor-widget elementor-widget-text-editor\" data-id=\"1a2af5d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Linear regression<\/strong> is undoubtedly the algorithm to master in data science. And if using it <strong>via Python still seems complex, that&#8217;s only temporary.<\/strong><\/p><p>With the right training, you&#8217;ll be able to evaluate any machine learning algorithm across different programming languages.<\/p><p>But which course should you choose? DataScientest, of course. Discover our program.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6eca5c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e6eca5c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn all about linear regression in Python<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-afccf6a elementor-widget elementor-widget-html\" data-id=\"afccf6a\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is linear regression?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The linear regression model is a supervised learning algorithm that predicts a continuous target variable (dependent variable) from one or more explanatory variables (independent or predictive variables). In other words, it establishes relationships between 2 or more variables. When there is only one explanatory variable, we speak of simple linear regression. On the other hand, if there are several, we speak of multiple linear regression. Whether simple or multiple, linear regression can be used with Python.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Linear regression use cases\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"If linear regression is the first algorithm used in machine learning, it's because it can be applied in so many different ways. For example: Identify the factors influencing the profitability of an investment; Predict future sales by analyzing past sales; Anticipate consumer behavior; Predict the price of a house based on its characteristics; etc.\"\n    }\n  }]\n}\n<\/script>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>A key algorithm in Machine Learning, linear regression is used to establish relationships between one or more variables. To put this algorithm into practice with ease, data scientists can turn to programming languages, particularly Python. So how do you use linear regression with Python? DataScientest answers the question. What is linear regression? Before looking at [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":182971,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Linear regression with Python: How does it work?<\/title>\n<meta name=\"description\" content=\"Delve into the mechanics of linear regression with Python. 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