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scikit-learn scikit-learn
scikit-learn: machine learning in Python
Stars
67,152
Forks
27,353
Watchers: 67,152
Language
License: BSD 3-Clause "New" or "Revised" License
Repository Radar Score
66 / 100
Growth
- 7d
- +0
- 30d
- +0
- %
- 0.0%
Not enough metric snapshots yet to chart growth for this repository.
Score breakdown
- popularity 92
- growth 0
- activity 100
- freshness 100
- community 85
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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 community of contributors, with volunteers but also support from several organisations.
Website: https://scikit-learn.org
scikit-learn requires:
- Python (>= 3.11)
- NumPy (>= 1.24.1)
- SciPy (>= 1.10.0)
- Narwhals (>= 2.0.1)
- joblib (>= 1.4.0)
- threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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.
See the changelog for a history of notable changes to scikit-learn.
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.
- 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
You can check the latest sources with the command:
git clone https://github.com/scikit-learn/scikit-learn.git
To learn more about making a contribution to scikit-learn, please see our Contributing guide.
After installation, you can launch the test suite from outside the source
directory (you will need to have pytest >= 7.1.2 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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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
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.
Note: scikit-learn was previously referred to as scikits.learn.
- 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
- Website: https://scikit-learn.org
- Blog: https://blog.scikit-learn.org
- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
- GitHub Discussions: https://github.com/scikit-learn/scikit-learn/discussions
- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
- Discord: https://discord.gg/h9qyrK8Jc8
- 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
- Bluesky: https://bsky.app/profile/scikit-learn.org
- Mastodon: https://mastodon.social/@sklearn@fosstodon.org
- Calendar: https://blog.scikit-learn.org/calendar/
- Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (67,152 stars) signals broad adoption.
- Maintained recently (last push 2 weeks ago).
Who should use it
- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
- Platform and DevOps engineers
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
- Evaluate automation, CI/CD, or infrastructure patterns
Strengths
- Recent repository activity
- README present in our index
- Declared license: BSD 3-Clause "New" or "Revised" License
- Substantial fork count (27,353) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest · Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.