PyCaret Official
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Welcome to PyCaret
An open-source, low-code machine learning library in Python
Help us improve the documentation! If you found a broken link or a typo or would like to contribute to this documentation, please submit a pull request on pycaret-docs repo.
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 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
Checkout our official notebooks!
Example notebooks.
Tutorials and articles by contributors.
The detailed API docs of PyCaret
Our video tutorial from various events.
Engage with community and contributors.
Changes and version history.
Frequently Asked Questions
PyCaret's development plan.
Join our Meetup user group.
Features
PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the hypothesis to insights 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
Regression
Clustering
Anomaly Detection
Time Series Module (beta)
PyCaret new time series module is now available in beta. Staying true to the simplicity of PyCaret, it is consistent with our existing API and fully loaded with functionalities. Statistical testing, model training and selection (30+ algorithms), model analysis, automated hyperparameter tuning, experiment logging, deployment on cloud, and more. All of this with only a few lines of code. If you would like to give it a try, check out our official quick start notebook.
To install this module in beta:
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pip install pycaret-ts-alpha
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PyCaret's time series module is in
beta. We are doing weekly pip releases. When installing pycaret-ts-alpha youmust create a separate python environment to avoid conflicts with the main pycaret library. Core Team
The following people are currently core contributors to PyCaret's development and maintenance:
moezali1 - Overview
GitHub
Yard1 - Overview
GitHub
ngupta23 - Overview
GitHub
tvdboom - Overview
GitHub
wkuopt - Overview
GitHub
mfahadakbar - Overview
GitHub
PabloJMoreno - Overview
GitHub
reza1615 - Overview
GitHub
Contribute to PyCaret! If you would like to contribute to this project, please see our Contribution Guidelines.
Documentation
This documentation is being developed and maintained by:
Contribute! If you would like to join our documentation team and help us build and maintain this awesome documentation for the community, join the #docs-revamp channel on our Slack.
Contributors
Get Help
- 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.
- Newsletter coming soon.
Citation
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@Manual {PyCaret,
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title = {PyCaret: An open source, low-code machine learning library in Python},
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author = {Moez Ali},
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year = {2020},
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month = {April},
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note = {PyCaret version 1.0},
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url = {https://www.pycaret.org
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}
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Support us
⭐Star PyCaret on GitHub
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🔗 Follow us on LinkedIn
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💁 Help others with issues on GitHub
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You can see existing open issues and discussions and help other members of the community by answering their questions.
📺 Subscribe to our YouTube Channel
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🗣 Tweet about PyCaret
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Help us spread the word. We love to hear success stories and use cases.
✍ Blog on PyCaret
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Ⓜ Join our Meetup
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Sponsors
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Last modified 3mo ago