scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors. It is currently maintained by a team of volunteers. Website: https://scikit-learn.org Installation Dependencies scikit-learn requires: Python (>= 3.8) NumPy (>= 1.17.3) SciPy (>= 1.3.2) joblib (>= 1.1.1) threadpoolctl (>= 2.0.0) Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer. Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display") require Matplotlib (>= 3.1.3). For running the examples Matplotlib >= 3.1.3 is required. A few examples require scikit-image >= 0.16.2, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0 and plotly >= 5.10.0. User installation If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip: pip install -U scikit-learn or conda: conda install -c conda-forge scikit-learn The documentation includes more detailed installation instructions. Changelog See the changelog for a history of notable changes to scikit-learn. Development We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README. Important links Official source code repo: https://github.com/scikit-learn/scikit-learn Download releases: https://pypi.org/project/scikit-learn/ Issue tracker: https://github.com/scikit-learn/scikit-learn/issues Source code You can check the latest sources with the command: git clone https://github.com/scikit-learn/scikit-learn.git Contributing To learn more about making a contribution to scikit-learn, please see our Contributing guide. Testing After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 5.3.1 installed): pytest sklearn See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage for more information. Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable. Submitting a Pull Request Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: https://scikit-learn.org/stable/developers/index.html Project History The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors. The project is currently maintained by a team of volunteers. Note: scikit-learn was previously referred to as scikits.learn. Help and Support Documentation HTML documentation (stable release): https://scikit-learn.org HTML documentation (development version): https://scikit-learn.org/dev/ FAQ: https://scikit-learn.org/stable/faq.html Communication Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn Gitter: https://gitter.im/scikit-learn/scikit-learn Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos Blog: https://blog.scikit-learn.org Calendar: https://blog.scikit-learn.org/calendar/ Twitter: https://twitter.com/scikit_learn Twitter (commits): https://twitter.com/sklearn_commits Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions Website: https://scikit-learn.org LinkedIn: https://www.linkedin.com/company/scikit-learn YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists Facebook: https://www.facebook.com/scikitlearnofficial/ Instagram: https://www.instagram.com/scikitlearnofficial/ TikTok: https://www.tiktok.com/@scikit.learn Citation If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn