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 alternative low-code library that can be used to perform complex machine learning tasks with only few lines of code. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy and many more.

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 is a great library which not only simplifies the machine learning tasks for citizen data scientists but also helps new startups to reduce the cost of investing in a team of data scientists. Therefore, this library has not only helped the citizen data scientists but has also helped individuals who want to start exploring the field of data science, having no prior knowledge in this field.

PyCaret is simple, easy to use and deployment ready. 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.

For more information on PyCaret, please visit our official website https://www.pycaret.org

PyCaret 2.1 is now available. See 2.1 release notes. The easiest way to install pycaret is using pip.

pip install pycaret

• Issue caused in model logging when log_experiment is True. Bug fixed in 2.1.1 patch released on 8/30/2020.
• Issue caused in predict_model functionality for pycaret.regression. Bug fixed in 2.1.2 patch released on 8/31/2020.

Docs: https://pycaret.readthedocs.io/en/latest/

Following libraries are not hard dependencies and are not automatically installed when you install PyCaret. To use all functionalities of PyCaret, these optional dependencies must be installed.

pip install psutil
pip install awscli 
pip install azure-storage-blob
pip install google-cloud-storage
pip install shap

Installation is only supported on 64-bit version of Python.

• Release notes : https://github.com/pycaret/pycaret/releases/tag/2.1
• Docs: https://pycaret.readthedocs.io/en/latest/
• User Guide : https://www.pycaret.org/guide
• Getting Started Tutorials : https://github.com/pycaret/pycaret/tree/master/tutorials
• Example Notebooks : https://github.com/pycaret/pycaret/tree/master/examples
• Other Resources : https://github.com/pycaret/pycaret/tree/master/resources
• Issue Logs: https://github.com/pycaret/pycaret/issues

PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is:

• Experienced Data Scientists who want to increase productivity.
• Citizen Data Scientists who prefer a low code machine learning solution.
• Students of Data Science.
• Data Science Professionals and Consultants involved in building Proof of Concept projects.

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Copyright 2019-2020 Moez Ali [email protected]

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