About PyCaret

PyCaret is an open source, low-code machine learning library in Python that aims to reduce cycle time from hypothesis to insights.

PyCaret 2.1 is now available. Click here to see release notes. Documentation on the website is only updated for major releases. To see the latest documentation, Click Here

What is PyCaret?

PyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a 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 few lines of code. PyCaret is simple and easy to use. All the operations performed in PyCaret are automatically stored in a custom Pipeline that is fully orchestrated for deployment. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy and many more. 

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 related challenges in business setting.

PyCaret deployment capabilities

PyCaret is a deployment ready library in Python which means all the steps performed in a ML experiment can be reproduced using a pipeline that is automatically developed and orchestrated in PyCaret as you progress through the experiment. 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.
  • Students of Data Science.
  • Data Scientists and Consultants involved in building Proof of Concept projects.

Citation

If you’re citing PyCaret in a research or scientific paper, please cite this page as the resource. PyCaret’s first stable release 1.0.0 was made publicly available in April 2020. A formatted version of the citation would look like this:

PyCaret.org. PyCaret, April 2020. URL https://pycaret.org/about. PyCaret version 1.0.0.

If you are using Bibtex:

@Manual{PyCaret,
    title = {PyCaret: An open source, low-code machine learning library in Python},
    author = {Moez Ali},
    year = {2020},
    month = {July},
    note = {PyCaret version 2.1},
    url = {https://www.pycaret.org},
}

 

I started this project in the summer of 2019. My motivation for this project comes from the emerging role of citizen data scientists. They provide a complementary role to professional data scientists and bring their own expertise and unique skills in analytics driven tasks. I believe organizations where citizen data scientists co-exist with professional data scientists will outperform companies that only relies on professional data scientists. As much as PyCaret is ideal for citizen data scientists due to its simplicity, ease of use and low-code environment, it can also be used by professional data scientists as part of their machine learning workflows and build rapid prototypes quick and efficiently. It is my hope that PyCaret will help millions of data scientists build their models in the future.

If you have any feedback or would like to reach out, please feel free to send me an email at moez@pycaret.org

Moez Ali

Founder and Author, PyCaret

 

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