{"id":22776,"date":"2021-04-30T07:50:00","date_gmt":"2021-04-30T07:50:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/supercharge-ml-experiments-with-pycaret-and-gradio\/"},"modified":"2023-08-23T13:18:09","modified_gmt":"2023-08-23T13:18:09","slug":"supercharge-ml-experiments-with-pycaret-and-gradio","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/supercharge-ml-experiments-with-pycaret-and-gradio\/","title":{"rendered":"Supercharge Your Machine Learning Experiments With PyCaret And Gradio"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"22776\" class=\"elementor elementor-22776\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-32e484e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"32e484e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e6e1915\" data-id=\"e6e1915\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5de82ff elementor-widget elementor-widget-text-editor\" data-id=\"5de82ff\" data-element_type=\"widget\" data-e-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\t\t<p class=\"has-medium-font-size\">A step-by-step tutorial to develop and interact with machine learning pipelines rapidly<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-542f58e elementor-widget elementor-widget-heading\" data-id=\"542f58e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19d8c99 elementor-widget elementor-widget-text-editor\" data-id=\"19d8c99\" data-element_type=\"widget\" data-e-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\t\t<p id=\"3462\">This tutorial is a step-by-step, beginner-friendly explanation of how you can integrate&nbsp;<a href=\"https:\/\/www.pycaret.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">PyCaret<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/www.gradio.app\/\" target=\"_blank\" rel=\"noreferrer noopener\">Gradio<\/a>, the two powerful open-source libraries in Python, and supercharge your machine learning experimentation within minutes.<\/p>\n<p id=\"237c\">This tutorial is a \u201chello world\u201d example, I have used&nbsp;<a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/iris\" target=\"_blank\" rel=\"noreferrer noopener\">Iris Dataset<\/a>&nbsp;from UCI, which is a multiclassification problem where the goal is to predict the class of iris plants. The code given in this example can be reproduced on any other dataset, without any major modifications.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9462c44 elementor-widget elementor-widget-heading\" data-id=\"9462c44\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">PyCaret<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-141fb37 elementor-widget elementor-widget-text-editor\" data-id=\"141fb37\" data-element_type=\"widget\" data-e-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\t\t<p id=\"3754\">PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in <a href=\"https:\/\/www.experfy.com\/blog\/software\/install-python-anaconda-on-windows\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python<\/a> for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.<\/p>\n<p id=\"f065\">PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient.<\/p>\n<p id=\"ad64\">PyCaret is&nbsp;<strong>simple and<\/strong>&nbsp;<strong>easy to use<\/strong>. All the operations performed in PyCaret are sequentially stored in a&nbsp;<strong>Pipeline<\/strong>&nbsp;that is fully automated for&nbsp;<strong>deployment.&nbsp;<\/strong>Whether it\u2019s imputing missing values, one-hot-encoding, transforming categorical data, feature engineering, or even hyperparameter tuning, PyCaret automates all of it.<\/p>\n<p id=\"29c1\">To learn more about PyCaret, check out their&nbsp;<a href=\"https:\/\/www.github.com\/pycaret\/pycaret\" rel=\"noopener\">GitHub<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14d7a64 elementor-widget elementor-widget-heading\" data-id=\"14d7a64\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Gradio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c1426fd elementor-widget elementor-widget-text-editor\" data-id=\"c1426fd\" data-element_type=\"widget\" data-e-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\t\t<p id=\"ccaf\">Gradio is an open-source Python library for creating customizable UI components around your machine learning models. Gradio makes it easy for you to \u201cplay around\u201d with your model in your browser by dragging and dropping in your own images, pasting your own text, recording your own voice, etc., and seeing what the model outputs.<\/p>\n<p id=\"57cf\">Gradio is useful for:<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab13adb elementor-widget elementor-widget-text-editor\" data-id=\"ab13adb\" data-element_type=\"widget\" data-e-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\t\t<ul><li>Creating quick demos around your trained ML pipelines<\/li><li>Getting live feedback on model performance<\/li><li>Debugging your model interactively during development<\/li><\/ul>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-339dc93 elementor-widget elementor-widget-text-editor\" data-id=\"339dc93\" data-element_type=\"widget\" data-e-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\t\t<p id=\"acc2\">To learn more about Gradio, check out their&nbsp;<a href=\"https:\/\/github.com\/gradio-app\/gradio\" target=\"_blank\" rel=\"noreferrer noopener\">GitHub<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c893075 elementor-widget elementor-widget-image\" data-id=\"c893075\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"882\" height=\"252\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ.png\" class=\"attachment-large size-large wp-image-19270\" alt=\"Supercharge Your Machine Learning Experiments With PyCaret And Gradio\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ.png 882w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ-300x86.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ-768x219.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ-610x174.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1CLPbvtAvxkI5MbnPFE59sQ-750x214.png 750w\" sizes=\"(max-width: 882px) 100vw, 882px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">The workflow for PyCaret and Gradio<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6370dcb elementor-widget elementor-widget-heading\" data-id=\"6370dcb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Installing PyCaret<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bbd252d elementor-widget elementor-widget-text-editor\" data-id=\"bbd252d\" data-element_type=\"widget\" data-e-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\t\t<p id=\"9c49\">Installing PyCaret is very easy and takes only a few minutes. We strongly recommend using a virtual environment to avoid potential conflicts with other libraries.<\/p>\n<p id=\"69cd\">PyCaret\u2019s default installation is a slim version of pycaret which only installs hard dependencies that are&nbsp;<a href=\"https:\/\/github.com\/pycaret\/pycaret\/blob\/master\/requirements.txt\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">listed here<\/a>.<\/p>\n<pre class=\"wp-block-preformatted\"><strong># install slim version (default)<br><\/strong>pip install pycaret<strong># install the full version<\/strong><br>pip install pycaret[full]<\/pre>\n<p id=\"f1dd\">When you install the full version of pycaret, all the optional dependencies as&nbsp;<a href=\"https:\/\/github.com\/pycaret\/pycaret\/blob\/master\/requirements-optional.txt\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">listed here<\/a>&nbsp;are also installed.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9ceb79a elementor-widget elementor-widget-heading\" data-id=\"9ceb79a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Installing Gradio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6530560 elementor-widget elementor-widget-text-editor\" data-id=\"6530560\" data-element_type=\"widget\" data-e-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\t\t<p id=\"b6d2\">You can install gradio from pip.<\/p>\n<pre class=\"wp-block-preformatted\">pip install gradio<\/pre>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fb7861 elementor-widget elementor-widget-heading\" data-id=\"0fb7861\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Let\u2019s get started<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-95d8b7f elementor-widget elementor-widget-text-editor\" data-id=\"95d8b7f\" data-element_type=\"widget\" data-e-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\t\t<pre class=\"wp-block-preformatted\"><strong># load the iris dataset from pycaret repo<\/strong><br>from pycaret.datasets import get_data<br>data = get_data('iris')<\/pre>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96e4af8 elementor-widget elementor-widget-image\" data-id=\"96e4af8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"505\" height=\"188\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1qttXFQnZ3atRv_qb9FtVTw.png\" class=\"attachment-large size-large wp-image-19271\" alt=\"Sample rows from iris dataset\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1qttXFQnZ3atRv_qb9FtVTw.png 505w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1qttXFQnZ3atRv_qb9FtVTw-300x112.png 300w\" sizes=\"(max-width: 505px) 100vw, 505px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Sample rows from iris dataset<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c0ceec9 elementor-widget elementor-widget-heading\" data-id=\"c0ceec9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Initialize Setup<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84d0612 elementor-widget elementor-widget-text-editor\" data-id=\"84d0612\" data-element_type=\"widget\" data-e-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\t\t<pre class=\"wp-block-preformatted\"><strong># initialize setup<\/strong><br>from pycaret.classification import *<br>s = setup(data, target = 'species', session_id = 123)<\/pre>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cabd7c8 elementor-widget elementor-widget-image\" data-id=\"cabd7c8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"397\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-1024x397.png\" class=\"attachment-large size-large wp-image-19272\" alt=\"Supercharge Your Machine Learning Experiments With PyCaret And Gradio\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-1024x397.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-300x116.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-768x298.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-610x237.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-750x291.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg-1140x442.png 1140w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1m5Sgz4IGqGEKNjbar6hGfg.png 1222w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1444555 elementor-widget elementor-widget-text-editor\" data-id=\"1444555\" data-element_type=\"widget\" data-e-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\t\t<p id=\"b567\">Whenever you initialize the&nbsp;<code>setup<\/code>&nbsp;function in PyCaret, it profiles the dataset and infers the data types for all input features. In this case, you can see all the four features (<em>sepal_length, sepal_width, petal_length, and petal_width<\/em>) are identified correctly as Numeric datatype. You can press enter to continue.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-efa16ae elementor-widget elementor-widget-image\" data-id=\"efa16ae\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"605\" height=\"482\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1MNchQT8Y7E_Lsg-66CVSFg.png\" class=\"attachment-large size-large wp-image-19273\" alt=\"output from setup - truncated for display\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1MNchQT8Y7E_Lsg-66CVSFg.png 605w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1MNchQT8Y7E_Lsg-66CVSFg-300x239.png 300w\" sizes=\"(max-width: 605px) 100vw, 605px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Output from setup \u2014 truncated for display<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2dce972 elementor-widget elementor-widget-text-editor\" data-id=\"2dce972\" data-element_type=\"widget\" data-e-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\t\t<p id=\"9b3c\">Common to all modules in PyCaret, the&nbsp;<code>setup<\/code>&nbsp;function is the first and the only mandatory step to start any machine learning experiment in PyCaret. Besides performing some basic processing tasks by default, PyCaret also offers a wide array of pre-processing features such as&nbsp;<a href=\"https:\/\/pycaret.org\/normalization\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">scaling and transformation<\/a>,&nbsp;<a href=\"https:\/\/pycaret.org\/feature-interaction\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">feature engineering<\/a>,&nbsp;<a href=\"https:\/\/pycaret.org\/feature-importance\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">feature selection<\/a>, and several key data preparatory steps such as&nbsp;<a href=\"https:\/\/pycaret.org\/one-hot-encoding\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">one-hot-encoding<\/a>,&nbsp;<a href=\"https:\/\/pycaret.org\/missing-values\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">missing values imputation<\/a>,&nbsp;<a href=\"https:\/\/pycaret.org\/fix-imbalance\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">over-sampling\/under-sampling<\/a>, etc. To learn more about all the preprocessing functionalities in PyCaret, you can see this&nbsp;<a href=\"https:\/\/pycaret.org\/preprocessing\/\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">link<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d655512 elementor-widget elementor-widget-image\" data-id=\"d655512\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"438\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-1024x438.png\" class=\"attachment-large size-large wp-image-19274\" alt=\"Supercharge Your Machine Learning Experiments With PyCaret And Gradio\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-1024x438.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-300x128.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-768x329.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-610x261.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg-750x321.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/17AOrLPzJWLFH90asByQqsg.png 1121w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><a href=\"https:\/\/pycaret.org\/preprocessing\/\" target=\"_blank\" class=\"broken_link\" rel=\"noopener\">https:\/\/pycaret.org\/preprocessing\/<\/a><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4ac11b elementor-widget elementor-widget-heading\" data-id=\"e4ac11b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Compare Models<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d32961 elementor-widget elementor-widget-text-editor\" data-id=\"5d32961\" data-element_type=\"widget\" data-e-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\t\t<p id=\"6138\">This is the first step we recommend in the workflow of\u00a0<em>any<\/em>\u00a0supervised experiment in PyCaret. This function trains all the available models in the model library using default hyperparameters and evaluates performance metrics using cross-validation.<\/p><p id=\"33d6\">The output of this function is a table showing the mean cross-validated scores for all the models. The number of folds can be defined using the\u00a0<code>fold<\/code>parameter (default = 10 folds). The table is sorted (highest to lowest) by the metric of choice which can be defined using the\u00a0<code>sort<\/code>parameter (default = \u2018Accuracy\u2019).<\/p><pre class=\"wp-block-preformatted\">best = compare_models(n_select = 15)<br \/>compare_model_results = pull()<\/pre><p id=\"eaef\"><code>n_select<\/code>\u00a0parameter in the setup function controls the return of trained models. In this case, I am setting it to 15, meaning return the top 15 models as a list.\u00a0<code>pull<\/code>\u00a0function in the second line stores the output of\u00a0<code>compare_models<\/code>\u00a0as\u00a0<code>pd.DataFrame<\/code>\u00a0.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cff5323 elementor-widget elementor-widget-image\" data-id=\"cff5323\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-1024x559.png\" class=\"attachment-large size-large wp-image-19275\" alt=\"Output from compare models\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-1024x559.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-300x164.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-768x419.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-610x333.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA-750x409.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1Qu62jca8TpZLkhZgUq1uFA.png 1030w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Output from compare_models<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9de0f2 elementor-widget elementor-widget-text-editor\" data-id=\"a9de0f2\" data-element_type=\"widget\" data-e-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\t\t<pre class=\"wp-block-preformatted\">len(best)<br>&gt;&gt;&gt; 15print(best[:5])<\/pre>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe50567 elementor-widget elementor-widget-image\" data-id=\"fe50567\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"914\" height=\"348\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ.png\" class=\"attachment-large size-large wp-image-19276\" alt=\"Supercharge Your Machine Learning Experiments With PyCaret And Gradio\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ.png 914w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ-300x114.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ-768x292.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ-610x232.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1_H72UEY5AQlYnQswyZ0xhQ-750x286.png 750w\" sizes=\"(max-width: 914px) 100vw, 914px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Output from print(best[:5])<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1f5f450 elementor-widget elementor-widget-heading\" data-id=\"1f5f450\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Gradio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7cb6a4 elementor-widget elementor-widget-text-editor\" data-id=\"a7cb6a4\" data-element_type=\"widget\" data-e-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\t\t<p id=\"c801\">Now that we are done with the modeling process, let\u2019s create a simple UI using Gradio to interact with our models. I will do it in two parts, first I will create a function that will use PyCaret\u2019s&nbsp;<code>predict_model<\/code>&nbsp;functionality to generate and return predictions and the second part will be feeding that function into Gradio and designing a simple input form for interactivity.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1afd5e6 elementor-widget elementor-widget-heading\" data-id=\"1afd5e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Part I \u2014 Creating an internal function<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e113b4d elementor-widget elementor-widget-text-editor\" data-id=\"e113b4d\" data-element_type=\"widget\" data-e-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\t\t<p id=\"30ad\">The first two lines of the code take the input features and convert them into pandas DataFrame. Line 7 is creating a unique list of model names displayed in the&nbsp;<code>compare_models<\/code>&nbsp;output (this will be used as a dropdown in the UI). Line 8 selects the best model based on the index value of the list (which will be passed in through UI) and Line 9 uses the&nbsp;<code>predict_model<\/code>&nbsp;functionality of PyCaret to score the dataset.<\/p>\n<p>https:\/\/gist.github.com\/moezali1\/2a383489a08757df93572676d20635e0#file-gradio_step1-py<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c6a725b elementor-widget elementor-widget-heading\" data-id=\"c6a725b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Part II \u2014 Creating a UI with Gradio<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ce9398 elementor-widget elementor-widget-text-editor\" data-id=\"3ce9398\" data-element_type=\"widget\" data-e-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\t\t<p id=\"95e2\">Line 3 in the code below creates a dropdown for model names, Line 4\u20137 creates a slider for each of the input features and I have set the default value to the mean of each feature. Line 9 initiates a UI (in the notebook as well as on your local host so you can view it in the browser).<\/p>\n<p>https:\/\/gist.github.com\/moezali1\/a1d83fb61e0ce14adcf4dffa784b1643<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32cabf1 elementor-widget elementor-widget-image\" data-id=\"32cabf1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"412\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-1024x412.png\" class=\"attachment-large size-large wp-image-19277\" alt=\"Supercharge Your Machine Learning Experiments With PyCaret And Gradio\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-1024x412.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-300x121.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-768x309.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-610x245.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-750x302.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ-1140x458.png 1140w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/1zVe2L4L8fqDL4zIN75rFwQ.png 1455w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Output from running Gradio interface<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4adf2ec elementor-widget elementor-widget-text-editor\" data-id=\"4adf2ec\" data-element_type=\"widget\" data-e-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\t\t<p id=\"4f68\">You can see this quick video here to see how easy it is to interact with your pipelines and query your models without writing hundreds of lines of code or developing a full-fledged front-end.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fcc74c elementor-widget elementor-widget-video\" data-id=\"0fcc74c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/RpOMTdEXFJc&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bb167b elementor-widget elementor-widget-text-editor\" data-id=\"1bb167b\" data-element_type=\"widget\" data-e-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\t\t<p id=\"a4ee\">I hope that you will appreciate the ease of use and simplicity in PyCaret and Gradio. In less than 25 lines of code and few minutes of experimentation, I have trained and evaluated multiple models using PyCaret and developed a lightweight UI to interact with models in the Notebook.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-605a672 elementor-widget elementor-widget-heading\" data-id=\"605a672\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Coming Soon!<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d1e4b05 elementor-widget elementor-widget-text-editor\" data-id=\"d1e4b05\" data-element_type=\"widget\" data-e-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\t\t<p id=\"e3c0\">Next week I will be writing a tutorial on unsupervised anomaly detection on time-series data using&nbsp;<a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/anomaly.html\" target=\"_blank\" rel=\"noreferrer noopener\">PyCaret Anomaly Detection Module<\/a>. Please follow me on&nbsp;<a href=\"https:\/\/medium.com\/@moez-62905\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Medium<\/a>,&nbsp;<a href=\"https:\/\/www.linkedin.com\/in\/profile-moez\/\" target=\"_blank\" rel=\"noreferrer noopener\">LinkedIn<\/a>, and&nbsp;<a href=\"https:\/\/twitter.com\/moezpycaretorg1\" target=\"_blank\" rel=\"noreferrer noopener\">Twitter<\/a>&nbsp;to get more updates.<\/p>\n<p id=\"5290\">There is no limit to what you can achieve using this lightweight workflow automation library in Python. If you find this useful, please do not forget to give us \u2b50\ufe0f on our GitHub repository.<\/p>\n<p id=\"7300\">To hear more about PyCaret follow us on&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/pycaret\/\" target=\"_blank\" rel=\"noreferrer noopener\">LinkedIn<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/www.youtube.com\/channel\/UCxA1YTYJ9BEeo50lxyI_B3g\" target=\"_blank\" rel=\"noreferrer noopener\">Youtube<\/a>.<\/p>\n<p id=\"dc29\">Join us on our slack channel. Invite link&nbsp;<a href=\"https:\/\/join.slack.com\/t\/pycaret\/shared_invite\/zt-p7aaexnl-EqdTfZ9U~mF0CwNcltffHg\" target=\"_blank\" rel=\"noreferrer noopener\">here<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-913cc25 elementor-widget elementor-widget-heading\" data-id=\"913cc25\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">You may also be interested in:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d29033b elementor-widget elementor-widget-text-editor\" data-id=\"d29033b\" data-element_type=\"widget\" data-e-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\t\t<p id=\"dec1\"><a href=\"https:\/\/towardsdatascience.com\/build-your-own-automl-in-power-bi-using-pycaret-8291b64181d\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Build your own AutoML in Power BI using PyCaret 2.0<\/a><br><a href=\"https:\/\/towardsdatascience.com\/deploy-machine-learning-pipeline-on-cloud-using-docker-container-bec64458dc01\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Deploy Machine Learning Pipeline on Azure using Docker<\/a><br><a href=\"https:\/\/towardsdatascience.com\/deploy-machine-learning-model-on-google-kubernetes-engine-94daac85108b\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Deploy Machine Learning Pipeline on Google Kubernetes Engine<\/a><br><a href=\"https:\/\/towardsdatascience.com\/deploy-machine-learning-pipeline-on-aws-fargate-eb6e1c50507\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Deploy Machine Learning Pipeline on AWS Fargate<\/a><br><a href=\"https:\/\/towardsdatascience.com\/build-and-deploy-your-first-machine-learning-web-app-e020db344a99\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Build and deploy your first machine learning web app<\/a><br><a href=\"https:\/\/towardsdatascience.com\/deploy-pycaret-and-streamlit-app-using-aws-fargate-serverless-infrastructure-8b7d7c0584c2\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Deploy PyCaret and Streamlit app using AWS Fargate serverless<\/a><br><a href=\"https:\/\/towardsdatascience.com\/build-and-deploy-machine-learning-web-app-using-pycaret-and-streamlit-28883a569104\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Build and deploy machine learning web app using PyCaret and Streamlit<\/a><br><a href=\"https:\/\/towardsdatascience.com\/deploy-machine-learning-app-built-using-streamlit-and-pycaret-on-google-kubernetes-engine-fd7e393d99cb\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Deploy Machine Learning App built using Streamlit and PyCaret on GKE<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e4997d elementor-widget elementor-widget-heading\" data-id=\"5e4997d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Important Links<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f01755c elementor-widget elementor-widget-text-editor\" data-id=\"f01755c\" data-element_type=\"widget\" data-e-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\t\t<p id=\"3c7d\"><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/installation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Documentation<\/a><br>Blog<br><a href=\"http:\/\/www.github.com\/pycaret\/pycaret\" target=\"_blank\" rel=\"noreferrer noopener\">GitHub<\/a><br><a href=\"https:\/\/stackoverflow.com\/questions\/tagged\/pycaret\" target=\"_blank\" rel=\"noreferrer noopener\">StackOverflow<\/a><br><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/installation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Install PyCaret<br><\/a><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/tutorials.html\" target=\"_blank\" rel=\"noreferrer noopener\">Notebook Tutorials<br><\/a><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/contribute.html\" target=\"_blank\" rel=\"noreferrer noopener\">Contribute in PyCaret<\/a><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73d1387 elementor-widget elementor-widget-heading\" data-id=\"73d1387\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Want to learn about a specific module?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-90ddbc2 elementor-widget elementor-widget-text-editor\" data-id=\"90ddbc2\" data-element_type=\"widget\" data-e-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\t\t<p id=\"a242\">Click on the links below to see the documentation and working examples.<\/p>\n<p id=\"0ed8\"><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/classification.html\" target=\"_blank\" rel=\"noreferrer noopener\">Classification<br><\/a><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/regression.html\" target=\"_blank\" rel=\"noreferrer noopener\">Regression<\/a><br><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/clustering.html\" target=\"_blank\" rel=\"noreferrer noopener\">Clustering<\/a><br><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/anomaly.html\" target=\"_blank\" rel=\"noreferrer noopener\">Anomaly Detection<\/a><br><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/nlp.html\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Natural Language Processing<br><\/a><a href=\"https:\/\/pycaret.readthedocs.io\/en\/latest\/api\/arules.html\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Association Rule Mining<\/a><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>This tutorial is a step-by-step, beginner-friendly explanation of how you can integrate PyCaret and Gradio, the two powerful open-source libraries in Python, and supercharge your machine learning experimentation within minutes.<\/p>\n","protected":false},"author":1123,"featured_media":19278,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[1543,1544,92,1545],"ppma_author":[3735],"class_list":["post-22776","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-gradio","tag-library","tag-machine-learning","tag-pycaret"],"authors":[{"term_id":3735,"user_id":1123,"is_guest":0,"slug":"moez-ali","display_name":"Moez Ali","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/Moez-Ali-150x150.jpeg","user_url":"https:\/\/pycaret.org\/","last_name":"Ali","first_name":"Moez","job_title":"","description":"Moez Ali, a Data Scientist, is Founder &amp; Author of <a href=\"https:\/\/pycaret.org\/\">PyCaret<\/a>, an open source low-code machine learning library."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22776","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/1123"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=22776"}],"version-history":[{"count":9,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22776\/revisions"}],"predecessor-version":[{"id":31250,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22776\/revisions\/31250"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/19278"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=22776"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=22776"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=22776"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=22776"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}