PyCaret 3.0

An open-source, low-code machine learning library in Python

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive.

Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.

The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.

Quick Links

Learn PyCaret	Documentation	Important Links
⭐Tutorials	📚 API Reference	GitHub
📋 Examples	🛠️ Release Notes	LinkedIn
📙 Blog	📢 Discussions	Meetup
📺 Videos	GitHub Issues	YouTube

Learn PyCaret

Documentation

Important Links

⭐Tutorials

📚 API Reference

GitHub

📋 Examples

🛠️ Release Notes

LinkedIn

📙 Blog

📢 Discussions

Meetup

📺 Videos

GitHub Issues

YouTube

Features

PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the hypothesis to insight cycle time in an ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to perform complex machine learning tasks with only a few lines of code. PyCaret is simple and easy to use.

PyCaret for Citizen Data Scientists

The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are ‘power users’ who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data science challenges in the business setting.

PyCaret deployment capabilities

PyCaret is a deployment ready library in Python which means all the steps performed in an ML experiment can be reproduced using a pipeline that is reproducible and guaranteed for production. A pipeline can be saved in a binary file format that is transferable across environments.

PyCaret is seamlessly integrated with BI

PyCaret and its Machine Learning capabilities are seamlessly integrated with environments supporting Python such as Microsoft Power BI, Tableau, Alteryx, and KNIME to name a few. This gives immense power to users of these BI platforms who can now integrate PyCaret into their existing workflows and add a layer of Machine Learning with ease.

PyCaret is ideal for:

Experienced Data Scientists who want to increase productivity.
Citizen Data Scientists who prefer a low code machine learning solution.
Data Science Professionals who want to build rapid prototypes.
Data Science and Machine Learning students and enthusiasts.

PyCaret at a glance

Classification

Functional API

OOP API

Regression

Functional API

OOP API

Time Series

Functional API

OOP API

Clustering

Functional API

OOP API

Anomaly Detection

Functional API

OOP API

Core Team

Contribute to PyCaret! If you would like to contribute to this project, please see our Contribution Guidelines.

Contributors

Get Help

The fastest way to get help is through our Slack group.
You can also see our Issue log or Discussions for answers to questions asked in the past by other members or raise a new question if it's not asked before.
You can also look out for answers to common questions on Stack Overflow.
We also have a pretty active LinkedIn page.
Check our Frequently Asked Questions (FAQs) page.

Citation

@Manual {PyCaret, 
    title = {PyCaret: An open source, low-code machine learning library in Python}, 
    author = {Moez Ali}, 
    year = {2020}, 
    month = {April}, 
    note = {PyCaret version 1.0}, 
    url = {https://www.pycaret.org
    }