{"id":224011,"date":"2024-01-19T06:30:00","date_gmt":"2024-01-19T06:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=224011"},"modified":"2024-01-12T15:58:24","modified_gmt":"2024-01-12T15:58:24","slug":"regression-lineaire-python","status":"publish","type":"post","link":"https:\/\/datascientest.com\/regression-lineaire-python","title":{"rendered":"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"224011\" class=\"elementor elementor-224011\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d919085 e-flex e-con-boxed e-con\" data-id=\"d919085\" 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-127a6e7 elementor-widget elementor-widget-heading\" data-id=\"127a6e7\" 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\">Algorithme incontournable en Machine Learning, la r\u00e9gression lin\u00e9aire permet d\u2019\u00e9tablir des relations entre une ou plusieurs variables. Pour mettre en pratique cet algorithme en toute simplicit\u00e9, les data scientists peuvent se tourner vers les langages de programmation, et notamment Python. Alors comment utiliser la r\u00e9gression lin\u00e9aire avec Python ? DataScientest r\u00e9pond \u00e0 la 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-3e3d72b e-flex e-con-boxed e-con\" data-id=\"3e3d72b\" 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-324fde0 elementor-widget elementor-widget-heading\" data-id=\"324fde0\" 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\">Qu\u2019est-ce que la r\u00e9gression lin\u00e9aire ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-60f08ad elementor-widget elementor-widget-text-editor\" data-id=\"60f08ad\" 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><span style=\"font-weight: 400;\">Avant de voir l\u2019utilisation pratique de la r\u00e9gression lin\u00e9aire avec Python, il convient de revenir aux bases.<\/span><\/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-dea9b46 elementor-widget elementor-widget-heading\" data-id=\"dea9b46\" 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\">R\u00e9gression lin\u00e9aire - D\u00e9finition<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59aaafc elementor-widget elementor-widget-text-editor\" data-id=\"59aaafc\" 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><span style=\"font-weight: 400;\">Le <a href=\"https:\/\/datascientest.com\/regression-logistique-quest-ce-que-cest\">mod\u00e8le de r\u00e9gression lin\u00e9aire<\/a> est un <\/span><b>algorithme d\u2019apprentissage supervis\u00e9 <\/b><span style=\"font-weight: 400;\">qui permet de pr\u00e9dire une variable cible continue (variable d\u00e9pendante) gr\u00e2ce \u00e0 une ou plusieurs variables explicatives (variables ind\u00e9pendantes ou pr\u00e9dictives). En d\u2019autres termes, il s\u2019agit d<\/span><b>\u2019\u00e9tablir les relations entre 2 ou plusieurs variables.\u00a0<\/b><\/p><p><span style=\"font-weight: 400;\">Lorsqu\u2019il n\u2019y a qu\u2019une variable explicative, on parle de r\u00e9gression lin\u00e9aire simple. En revanche, s\u2019il y en a plusieurs, on parle de r\u00e9gression lin\u00e9aire multiple.<\/span><\/p><p><span style=\"font-weight: 400;\">Qu\u2019elle soit simple ou multiple, la r\u00e9gression lin\u00e9aire peut \u00eatre utilis\u00e9e <a href=\"https:\/\/datascientest.com\/python-tout-savoir\">avec Python<\/a>.<\/span><\/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-3cbffe1 elementor-widget elementor-widget-heading\" data-id=\"3cbffe1\" 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\">R\u00e9gression lin\u00e9aire - traductions math\u00e9matiques<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a228a5f elementor-widget elementor-widget-text-editor\" data-id=\"a228a5f\" 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><span style=\"font-weight: 400;\">L\u2019\u00e9quation math\u00e9matique de la r\u00e9gression lin\u00e9aire se traduit comme suit :<\/span><\/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-a001351 elementor-widget elementor-widget-text-editor\" data-id=\"a001351\" 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><span style=\"font-weight: 400;\">Dans cette \u00e9quation :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><em>Y<\/em> correspond \u00e0 la valeur explicative ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><em>\u03b8<\/em> correspond au terme de biais ou vecteur de param\u00e8tres\u00a0 ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><em>x<sub>1<\/sub>, x<sub>2<\/sub>\u2026, x<sub>n<\/sub><\/em> correspondent aux valeurs des entit\u00e9s.<\/span><\/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-49466c1 elementor-widget elementor-widget-text-editor\" data-id=\"49466c1\" 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><span style=\"font-weight: 400;\">D\u2019un point de vue visuel, la r\u00e9gression lin\u00e9aire s\u2019applique lorsque <\/span><b>les donn\u00e9es d&rsquo;entra\u00eenement repr\u00e9sentent un nuage de point. <\/b><span style=\"font-weight: 400;\">L\u2019objectif est alors d\u2019identifier la droite qui se rapproche le plus possible de l\u2019ensemble de points. Pour \u00eatre s\u00fbr que cette droite soit la plus pr\u00e9cise possible, il convient de mesurer <\/span><span style=\"font-weight: 400;\">l\u2019erreur quadratique moyenne (ou mean squared error).<\/span><\/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-e6328f5 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e6328f5\" 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\/formation-data-scientist\">\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\">Tout savoir de la r\u00e9gression lin\u00e9aire avec 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-bfb0952 e-flex e-con-boxed e-con\" data-id=\"bfb0952\" 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-28f4597 elementor-widget elementor-widget-heading\" data-id=\"28f4597\" 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\">Les cas d\u2019utilisation de la r\u00e9gression lin\u00e9aire<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7374964 elementor-widget elementor-widget-text-editor\" data-id=\"7374964\" 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<span style=\"font-weight: 400;\">Si la r\u00e9gression lin\u00e9aire est le premier <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\">algorithme utilis\u00e9 en machine learning<\/a>, c\u2019est parce que <\/span><b>ses cas d\u2019application sont multiples.<\/b><span style=\"font-weight: 400;\"> Par exemple :\u00a0<\/span>\n<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifier les facteurs influen\u00e7ant la rentabilit\u00e9 d\u2019un investissement ;\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pr\u00e9voir les ventes futures en analysant les ventes pass\u00e9es ;\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Anticiper le comportement des consommateurs ;\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pr\u00e9dire le prix d\u2019une maison en fonction de ses caract\u00e9ristiques ; <\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">etc.<\/span><\/li>\n<\/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-7f61626 elementor-widget elementor-widget-text-editor\" data-id=\"7f61626\" 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><span style=\"font-weight: 400;\">Et pour chaque cas d\u2019application de la r\u00e9gression lin\u00e9aire, il est possible d\u2019utiliser Python.\u00a0<\/span><\/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-97b6841 e-flex e-con-boxed e-con\" data-id=\"97b6841\" 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-a1ad333 elementor-widget elementor-widget-heading\" data-id=\"a1ad333\" 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\">Comment utiliser la r\u00e9gression lin\u00e9aire avec Python ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30ed668 elementor-widget elementor-widget-text-editor\" data-id=\"30ed668\" 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><span style=\"font-weight: 400;\">Pour vous expliquer la r\u00e9gression lin\u00e9aire avec Python, il convient de prendre un exemple concret. Voici l\u2019hypoth\u00e8se de d\u00e9part :\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Un restaurateur poss\u00e9dant d\u00e9j\u00e0 plusieurs restaurants dans plusieurs villes souhaite d\u00e9velopper son activit\u00e9 en s\u2019implantant \u00e0 diff\u00e9rents endroits. Pour analyser les prochaines villes o\u00f9 il va s\u2019implanter, le restaurateur dispose de deux <a href=\"https:\/\/datascientest.com\/datasets-top-5-des-sites-ou-trouver-des-jeux-de-donnees-de-qualite\">groupes de donn\u00e9es<\/a> : les b\u00e9n\u00e9fices r\u00e9alis\u00e9s dans les villes o\u00f9 il est d\u00e9j\u00e0 implant\u00e9 et les populations des villes.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">L\u2019objectif \u00e9tant de faire un maximum de b\u00e9n\u00e9fices dans la ville o\u00f9 il va s\u2019implanter, il faut pr\u00e9dire le gain effectu\u00e9 dans la ville o\u00f9 il va s\u2019implanter (variable d\u00e9pendante = Y) en fonction de la population de celle-ci (variable ind\u00e9pendante = X).\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Alors comment \u00e9valuer le mod\u00e8le de r\u00e9gression lin\u00e9aire avec Python ? Voici les d\u00e9marches \u00e0 suivre. <\/span><\/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-f940e5c elementor-widget elementor-widget-heading\" data-id=\"f940e5c\" 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\">Formater les donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5ca202a elementor-widget elementor-widget-text-editor\" data-id=\"5ca202a\" 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><span style=\"font-weight: 400;\">Pour mod\u00e9liser la r\u00e9gression lin\u00e9aire avec Python, il convient de pr\u00e9parer les donn\u00e9es d&rsquo;entra\u00eenement au bon format. L\u2019id\u00e9al est de <\/span><b>pr\u00e9parer un fichier CSV <\/b><span style=\"font-weight: 400;\">avec deux colonnes : une pour la population (variable ind\u00e9pendante) et une autre pour les b\u00e9n\u00e9fices (variable ind\u00e9pendante). Voici \u00e0 quoi peut ressembler ce fichier :<\/span><\/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-ac17892 elementor-widget elementor-widget-html\" data-id=\"ac17892\" 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\">B\u00e9n\u00e9fices <\/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-fdc8c32 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"fdc8c32\" 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\/formation-data-scientist\">\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\">Savoir utiliser la r\u00e9gression lin\u00e9aire avec 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-4334f6b elementor-widget elementor-widget-heading\" data-id=\"4334f6b\" 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\">Charger des donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48aa6d5 elementor-widget elementor-widget-text-editor\" data-id=\"48aa6d5\" 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<span style=\"font-weight: 400;\">Ces donn\u00e9es d\u2019entra\u00eenement doivent ensuite \u00eatre charg\u00e9es dans Python. <\/span><b>Gr\u00e2ce \u00e0 la librairie Pandas<\/b><span style=\"font-weight: 400;\"> vous pouvez facilement lire des fichiers CSV. Voici la manipulation. <\/span>\n<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-261f385 elementor-widget elementor-widget-text-editor\" data-id=\"261f385\" 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<span style=\"font-weight: 400;\">La fonction <\/span><i><span style=\"font-weight: 400;\">read_csv()<\/span><\/i><span style=\"font-weight: 400;\"> renvoie \u00e0 un tableau de deux dimensions contenant les variables d\u00e9pendantes et ind\u00e9pendantes. Mais pour utiliser la r\u00e9gression lin\u00e9aire avec Python,\u00a0 il faut s\u00e9parer les deux colonnes dans deux variables Python.<\/span>\n\n<span style=\"font-weight: 400;\">Pour la premi\u00e8re colonne correspondant \u00e0 la taille de la population :<\/span>\n<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-944707c elementor-widget elementor-widget-text-editor\" data-id=\"944707c\" 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<span style=\"font-weight: 400;\">Pour la deuxi\u00e8me colonne correspondant aux b\u00e9n\u00e9fices\u00a0<\/span><span style=\"font-weight: 400;\">:<\/span>\n<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-31f5abc elementor-widget elementor-widget-text-editor\" data-id=\"31f5abc\" 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><span style=\"font-weight: 400;\">Cela vous permet d\u2019obtenir un simple tableau contenant l\u2019ensemble du jeu de donn\u00e9es d&rsquo;entra\u00eenement. <\/span><\/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-1e9cb66 elementor-widget elementor-widget-heading\" data-id=\"1e9cb66\" 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\">Visualiser des donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92ea6b2 elementor-widget elementor-widget-text-editor\" data-id=\"92ea6b2\" 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><span style=\"font-weight: 400;\">Pour mieux comprendre la r\u00e9gression lin\u00e9aire avec Python, il peut \u00eatre utile de la visualiser. Cela vous permettra d&rsquo;identifier les points et de mieux comprendre les dispersions.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Afin d\u2019obtenir un <a href=\"https:\/\/datascientest.com\/scatter-plot\">graphe de type nuage de points<\/a>, vous pouvez <\/span><strong>utiliser<\/strong> <a href=\"https:\/\/datascientest.com\/matplotlib-tout-savoir\">Matplotlib<\/a><b>, <\/b><span style=\"font-weight: 400;\">une librairie Python. Voici comment l\u2019obtenir\u00a0 :<\/span><\/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-dc029bb elementor-widget elementor-widget-heading\" data-id=\"dc029bb\" 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\">Appliquer l\u2019algorithme<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fa8941 elementor-widget elementor-widget-text-editor\" data-id=\"5fa8941\" 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<span style=\"font-weight: 400;\">\u00c0 partir de l\u00e0, l\u2019objectif est de trouver une fonction pr\u00e9dictive F(X) avec pour donn\u00e9es d\u2019entr\u00e9es la taille de la population, et pour donn\u00e9es de sortie les b\u00e9n\u00e9fices esp\u00e9r\u00e9s.\u00a0<\/span>\n\n<span style=\"font-weight: 400;\">Pour mod\u00e9liser la r\u00e9gression lin\u00e9aire avec Python, le plus simple est <\/span><b>d\u2019utiliser la biblioth\u00e8que <\/b><a href=\"https:\/\/datascientest.com\/tout-savoir-sur-scikit-learn\">Scikit Learn<\/a><span style=\"font-weight: 400;\"> en tapant cette requ\u00eate :<\/span>\n<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-d56e464 elementor-widget elementor-widget-text-editor\" data-id=\"d56e464\" 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<span style=\"font-weight: 400;\">\u00c0 partir de l\u00e0, vous pouvez construire votre mod\u00e8le. Voici le code \u00e0 r\u00e9diger : <\/span>\n<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-4630f2c elementor-widget elementor-widget-text-editor\" data-id=\"4630f2c\" 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<span style=\"font-weight: 400;\">Et pour trouver la droite f(x)=ax+b avec une erreur quadratique minimale, il convient de taper : <\/span>\n<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-01a7ee7 elementor-widget elementor-widget-heading\" data-id=\"01a7ee7\" 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\">Effectuer des pr\u00e9dictions<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4df2dac elementor-widget elementor-widget-text-editor\" data-id=\"4df2dac\" 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<span style=\"font-weight: 400;\">Pour <\/span><b>tracer la courbe de r\u00e9gression lin\u00e9aire avec Python,<\/b><span style=\"font-weight: 400;\"> il suffit de taper le code ci-dessous :<\/span>\n<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-6e19d0b e-flex e-con-boxed e-con\" data-id=\"6e19d0b\" 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-1df2808 elementor-widget elementor-widget-heading\" data-id=\"1df2808\" 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\">Ma\u00eetrisez la r\u00e9gression lin\u00e9aire avec Python<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3841a65 elementor-widget elementor-widget-text-editor\" data-id=\"3841a65\" 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><span style=\"font-weight: 400;\">La r\u00e9gression lin\u00e9aire est sans aucun doute l\u2019<a href=\"https:\/\/datascientest.com\/algorithme-tout-savoir\">algorithme \u00e0 ma\u00eetriser<\/a> absolument en data science. Et si son utilisation via Python vous para\u00eet encore complexe, ce n\u2019est que temporaire. En suivant la bonne formation, vous serez capable d\u2019\u00e9valuer n\u2019importe quel algorithme de machine learning \u00e0 travers diff\u00e9rents langages de programmation. Mais quelle formation choisir ? DataScientest, bien s\u00fbr. <a href=\"https:\/\/datascientest.com\/formation-data-scientist\">D\u00e9couvrez notre programme<\/a>.<\/span><\/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-bb4ded8 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"bb4ded8\" 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\/formation-data-scientist\">\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\">Tout apprendre de la r\u00e9gression lin\u00e9aire en 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-d5f7a11 elementor-widget elementor-widget-html\" data-id=\"d5f7a11\" 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\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu\u2019est-ce que la r\u00e9gression lin\u00e9aire ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le mod\u00e8le de r\u00e9gression lin\u00e9aire est un algorithme d\u2019apprentissage supervis\u00e9 qui permet de pr\u00e9dire une variable cible continue (variable d\u00e9pendante) gr\u00e2ce \u00e0 une ou plusieurs variables explicatives (variables ind\u00e9pendantes ou pr\u00e9dictives). En d\u2019autres termes, il s\u2019agit d\u2019\u00e9tablir les relations entre 2 ou plusieurs variables. Lorsqu\u2019il n\u2019y a qu\u2019une variable explicative, on parle de r\u00e9gression lin\u00e9aire simple. En revanche, s\u2019il y en a plusieurs, on parle de r\u00e9gression lin\u00e9aire multiple. Qu\u2019elle soit simple ou multiple, la r\u00e9gression lin\u00e9aire peut \u00eatre utilis\u00e9e avec Python.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Les cas d\u2019utilisation de la r\u00e9gression lin\u00e9aire\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Si la r\u00e9gression lin\u00e9aire est le premier algorithme utilis\u00e9 en machine learning, c\u2019est parce que ses cas d\u2019application sont multiples. Par exemple : Identifier les facteurs influen\u00e7ant la rentabilit\u00e9 d\u2019un investissement ; Pr\u00e9voir les ventes futures en analysant les ventes pass\u00e9es ; Anticiper le comportement des consommateurs ; Pr\u00e9dire le prix d\u2019une maison en fonction de ses caract\u00e9ristiques ; etc.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\r\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>Algorithme incontournable en Machine Learning, la r\u00e9gression lin\u00e9aire permet d\u2019\u00e9tablir des relations entre une ou plusieurs variables. Pour mettre en pratique cet algorithme en toute simplicit\u00e9, les data scientists peuvent se tourner vers les langages de programmation, et notamment Python. Alors comment utiliser la r\u00e9gression lin\u00e9aire avec Python ? DataScientest r\u00e9pond \u00e0 la question. Qu\u2019est-ce [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":224013,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"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>La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?<\/title>\n<meta name=\"description\" content=\"Apprenez \u00e0 mod\u00e9liser les algorithmes de r\u00e9gression lin\u00e9aire avec Python ; du formatage des donn\u00e9es jusqu\u2019aux analyses pr\u00e9dictives. Mode d\u2019emploi.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/regression-lineaire-python\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/regression-lineaire-python\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2024-01-19T06:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-12T15:58:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/regression-lineaire-python.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"J\u00e9r\u00e9my Robert\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"J\u00e9r\u00e9my Robert\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/regression-lineaire-python\",\"url\":\"https:\/\/datascientest.com\/regression-lineaire-python\",\"name\":\"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2024-01-19T06:30:00+00:00\",\"dateModified\":\"2024-01-12T15:58:24+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\"},\"description\":\"Apprenez \u00e0 mod\u00e9liser les algorithmes de r\u00e9gression lin\u00e9aire avec Python ; du formatage des donn\u00e9es jusqu\u2019aux analyses pr\u00e9dictives. Mode d\u2019emploi.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/regression-lineaire-python\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\",\"name\":\"J\u00e9r\u00e9my Robert\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"caption\":\"J\u00e9r\u00e9my Robert\"},\"description\":\"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. Avant de rejoindre DataScientest, il a travaill\u00e9 en tant que Consultant Business Intelligence chez DEAPDATA, o\u00f9 il a g\u00e9r\u00e9 des projets dans les domaines de l'assurance, bancaire et industrie. Il a \u00e9galement \u00e9t\u00e9 Co-fondateur et pr\u00e9sident de Tripzer. En tant que consultant ind\u00e9pendant, J\u00e9r\u00e9my a propos\u00e9 des formations sur MicroStrategy pour divers niveaux professionnels. Il est dipl\u00f4m\u00e9 de l'Universit\u00e9 Paris Cit\u00e9 avec un Master MIAGE, combinant comp\u00e9tences en informatique et gestion.\",\"sameAs\":[\"https:\/\/www.linkedin.com\/in\/jeremyrobert1\/\",\"Data Science\"],\"url\":\"https:\/\/datascientest.com\/author\/jeremyrobert\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?","description":"Apprenez \u00e0 mod\u00e9liser les algorithmes de r\u00e9gression lin\u00e9aire avec Python ; du formatage des donn\u00e9es jusqu\u2019aux analyses pr\u00e9dictives. Mode d\u2019emploi.","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:\/\/datascientest.com\/regression-lineaire-python","og_locale":"fr_FR","og_type":"article","og_title":"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?","og_url":"https:\/\/datascientest.com\/regression-lineaire-python","og_site_name":"Formation Data Science | DataScientest.com","article_published_time":"2024-01-19T06:30:00+00:00","article_modified_time":"2024-01-12T15:58:24+00:00","og_image":[{"width":1200,"height":600,"url":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/regression-lineaire-python.png","type":"image\/png"}],"author":"J\u00e9r\u00e9my Robert","twitter_card":"summary_large_image","twitter_misc":{"\u00c9crit par":"J\u00e9r\u00e9my Robert","Dur\u00e9e de lecture estim\u00e9e":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/datascientest.com\/regression-lineaire-python","url":"https:\/\/datascientest.com\/regression-lineaire-python","name":"La r\u00e9gression lin\u00e9aire avec Python : Comment \u00e7a fonctionne ?","isPartOf":{"@id":"https:\/\/datascientest.com\/#website"},"datePublished":"2024-01-19T06:30:00+00:00","dateModified":"2024-01-12T15:58:24+00:00","author":{"@id":"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925"},"description":"Apprenez \u00e0 mod\u00e9liser les algorithmes de r\u00e9gression lin\u00e9aire avec Python ; du formatage des donn\u00e9es jusqu\u2019aux analyses pr\u00e9dictives. Mode d\u2019emploi.","inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/regression-lineaire-python"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/#website","url":"https:\/\/datascientest.com\/","name":"Formation Data Science | DataScientest.com","description":"Leader de la formation en Data Science","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Person","@id":"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925","name":"J\u00e9r\u00e9my Robert","image":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/datascientest.com\/#\/schema\/person\/image\/","url":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg","contentUrl":"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg","caption":"J\u00e9r\u00e9my Robert"},"description":"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. Avant de rejoindre DataScientest, il a travaill\u00e9 en tant que Consultant Business Intelligence chez DEAPDATA, o\u00f9 il a g\u00e9r\u00e9 des projets dans les domaines de l'assurance, bancaire et industrie. Il a \u00e9galement \u00e9t\u00e9 Co-fondateur et pr\u00e9sident de Tripzer. En tant que consultant ind\u00e9pendant, J\u00e9r\u00e9my a propos\u00e9 des formations sur MicroStrategy pour divers niveaux professionnels. Il est dipl\u00f4m\u00e9 de l'Universit\u00e9 Paris Cit\u00e9 avec un Master MIAGE, combinant comp\u00e9tences en informatique et gestion.","sameAs":["https:\/\/www.linkedin.com\/in\/jeremyrobert1\/","Data Science"],"url":"https:\/\/datascientest.com\/author\/jeremyrobert"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts\/224011"}],"collection":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/users\/78"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/comments?post=224011"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/posts\/224011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/media\/224013"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/media?parent=224011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/categories?post=224011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/wp-json\/wp\/v2\/tags?post=224011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}