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 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 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
The first stable release pycaret version 1.0.0 is now publicly available. The easiest way to install pycaret is using pip.
pip install pycaret- User Guide / Documentation: https://www.pycaret.org/guide
- Getting Started Tutorials: https://www.pycaret.org/tutorial
- 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.
Please read requirements.txt for list of requirements. They are automatically installed when pycaret is installed using pip.
- pycaret.anomaly, Moez Ali moez.ali@queensu.ca
- pycaret.classification, Moez Ali moez.ali@queensu.ca
- pycaret.clustering, Moez Ali moez.ali@queensu.ca
- pycaret.datasets, Moez Ali moez.ali@queensu.ca
- pycaret.nlp, Moez Ali moez.ali@queensu.ca
- pycaret.preprocess, Fahad Akbar M.akbar@queensu.ca
- pycaret.regression, Moez Ali moez.ali@queensu.ca
If you would like to contribute to PyCaret, please visit https://www.pycaret.org/contribute
Copyright 2019-2020 Moez Ali moez.ali@queensu.ca
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