{"id":181822,"date":"2024-03-20T07:29:00","date_gmt":"2024-03-20T06:29:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=181822"},"modified":"2026-02-06T08:19:41","modified_gmt":"2026-02-06T07:19:41","slug":"pycaret-everything-you-need-to-know-about-this-python-library","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/pycaret-everything-you-need-to-know-about-this-python-library","title":{"rendered":"PyCaret: Everything you need to know about this Python library"},"content":{"rendered":"<style>\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><p><strong>Inspired by a group of citizen data scientists, Pycaret aims to democratize machine learning for everyone. So what exactly is it? And above all, what are its functionalities? That&#8217;s what we&#8217;re going to find out in this article.<\/strong><\/p>\t\t\n\t\t\t<h3>What is PyCaret?<\/h3>\t\t\n\t\t<p><strong>Pycaret<\/strong> is an <a href=\"https:\/\/liora.io\/en\/open-source-definition-use-cases-and-more\">open source<\/a>, low-code Machine Learning library based on Python. This solution automates the end-to-end machine learning workflow. By automating tasks and managing ML models<strong>, PyCaret<\/strong> speeds up the experimentation cycle. As a result, data scientists are much more productive and able to develop even more powerful <a href=\"https:\/\/liora.io\/en\/data-poisoning-a-threat-to-machine-learning-models\">machine learning models.<\/a><\/p><p><strong>PyCaret<\/strong> is more than just a Python-based ML library. And for good reason, it encompasses several machine learning libraries and frameworks. For example: scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, etc.<\/p><p>Best of all, it&#8217;s a ready-to-deploy <a href=\"https:\/\/liora.io\/en\/exploring-imageio-a-comprehensive-guide-to-the-python-library\">Python library<\/a>. In other words, every step of an ML experience can be reproduced from one environment to another.<\/p><p><strong>Good to know:<\/strong> PyCaret also integrates with many other solutions, such as Microsoft Power BI, <a href=\"https:\/\/liora.io\/en\/what-is-tableau\">Tableau<\/a>, Alteryx and KNIME. So you can add a layer of machine learning to all your business intelligence work. And it&#8217;s easy to do.<\/p>\t\t\n\t\t\t<style>\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/09\/pycaret-Liora1.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Learn more about PyCaret<\/a><\/div><\/div>\n\n\t\t\t<h3>Why use PyCaret?<\/h3>\t\t\n\t\t\t<h4>A low-code library<\/h4>\t\t\n\t\t<p>As the library is based on <strong>low code<\/strong>, PyCaret doesn&#8217;t require hundreds of lines of code, just a few. Even when it comes to performing complex machine learning tasks, <strong>PyCaret<\/strong> remains low code.<\/p><p>This means that data scientists can concentrate more on analyzing datasets. They spend less time coding, and more time generating relevant <strong>predictive analyses<\/strong> and training efficient <a href=\"https:\/\/liora.io\/en\/data-poisoning-a-threat-to-machine-learning-models\">machine learning models.<\/a><\/p>\t\t\n\t\t\t<h4>Data processing<\/h4>\t\t\n\t\t<p><a href=\"https:\/\/liora.io\/en\/automl-and-machine-learning-automation-a-threat-to-data-scientists\">Data scientists<\/a> can choose from multiple data preprocessing functions to save precious time in data processing. Here are just a few examples of the functions available in the Python library:<\/p><ul><li><strong>Data preparation:<\/strong> before exploring data to deploy models, it needs to be processed. <strong>PyCaret<\/strong> can identify missing values, data types and eliminate outliers.<\/li><li><strong>Scalability and transformation:<\/strong> this involves both normalizing data and modifying the shape of the<a href=\"https:\/\/liora.io\/en\/understanding-kurtosis-calculating-outlier-frequency-in-statistical-distributions\"> distribution<\/a> if necessary.<\/li><li><strong>Engineering:<\/strong> in particular to create links between data sets.<\/li><\/ul>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/09\/pycaret-Liora2.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Learn to master PyCaret<\/a><\/div><\/div>\n\n\t\t\t<h4>The modules<\/h4>\t\t\n\t\t<p><strong>PyCaret<\/strong> works through modules, each encapsulating a specific task:<\/p><ul><li><strong>Supervised Machine Learning models:<\/strong> these include <a href=\"https:\/\/liora.io\/en\/classification-algorithms-definition-and-main-models\">classification<\/a> and regression. This allows you to predict class labels and continuous variables.<\/li><li><strong>Unsupervised Machine Learning models:<\/strong> these involve clustering (the grouping of certain populations according to common characteristics) and anomaly detection (data that doesn&#8217;t fit the pattern).<\/li><li><strong>Time series:<\/strong> this involves forecasting time series to inform strategic <a href=\"https:\/\/liora.io\/en\/data-storytelling-using-data-to-convey-powerful-messages\">decision-making<\/a>.<\/li><li><strong>Data sets:<\/strong> use this module to access PyCaret&#8217;s extensive range of ML <a href=\"https:\/\/liora.io\/en\/datasets-top-5-places-to-find-quality-datasets\">data sets.<\/a><\/li><\/ul>\t\t\n\t\t\t<h4>The functions<\/h4>\t\t\n\t\t<p>For all these modules, PyCaret groups together coherent sets of actions capable of automating the data scientist&#8217;s workflow. Here are the main functions available on this <a href=\"https:\/\/liora.io\/en\/python-the-most-popular-language\">ML library in Python<\/a>:<\/p><ul><li>analytics data exploration ;<\/li><li>ML model deployment ;<\/li><li>model training ;<\/li><li>iteration.<\/li><\/ul><p>By automating <a href=\"https:\/\/liora.io\/en\/automl-and-machine-learning-automation-a-threat-to-data-scientists\">Machine Learning <\/a>tasks, these functions reduce the cycle time between hypothesis and understanding in a machine learning experience.<\/p><p>Thanks to its low-code solution, its data preprocessing solution, its modules and its numerous functions,<strong> PyCaret<\/strong> aims to democratize machine learning for everyone. Not just data scientists with solid technical expertise. But also those able to perform simple, moderately sophisticated analyses.<\/p><p>However, <strong>PyCaret<\/strong> is also very useful for <a href=\"https:\/\/liora.io\/en\/data-strategist-everything-you-need-to-know-about-this-data-profession\">experienced data scientists<\/a>, enabling them to increase their productivity exponentially.<\/p>\t\t\n\t\t\t<h3>Join Liora to develop your Machine Learning skills<\/h3>\t\t\n\t\t<p>While <strong>PyCaret<\/strong> aims to democratize <strong>Machine Learning<\/strong> for everyone, the development of machine learning models capable of solving complex problems still requires specific technical skills. And it&#8217;s these skills that companies are looking for.<\/p><p>It is therefore essential to train in data science. Liora makes it possible.<\/p><p>Through our training courses, you&#8217;ll learn everything you need to know about <a href=\"https:\/\/liora.io\/en\/machine-learning-engineer-bootcamp-why-is-it-interesting\">Machine Learning<\/a>, from data exploration to model deployment, training and iteration. In doing so, you&#8217;ll become operational as soon as the course is over.<\/p><p><em>Ready to start a new career?<\/em> Come and join us.<\/p>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/09\/pycaret-Liora3.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-scientist\">Start your PyCaret training<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Inspired by a group of citizen data scientists, Pycaret aims to democratize machine learning for everyone. So what exactly is it? And above all, what are its functionalities? That&#8217;s what we&#8217;re going to find out in this article. What is PyCaret? Pycaret is an open source, low-code Machine Learning library based on Python. This solution [&hellip;]<\/p>\n","protected":false},"author":76,"featured_media":181824,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-181822","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/181822","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/76"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=181822"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/181822\/revisions"}],"predecessor-version":[{"id":205936,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/181822\/revisions\/205936"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/181824"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=181822"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=181822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}