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A set of python modules for machine learning and data mining

Project description

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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 team of volunteers.




scikit-learn requires:

  • Python (>= 3.7)
  • NumPy (>= 1.14.6)
  • SciPy (>= 1.1.0)
  • joblib (>= 0.11)
  • threadpoolctl (>= 2.0.0)

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 0.23 and later require Python 3.6 or newer. scikit-learn 1.0 and later require Python 3.7 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with “Display”) require Matplotlib (>= 2.2.3). For running the examples Matplotlib >= 2.2.3 is required. A few examples require scikit-image >= 0.14.5, a few examples require pandas >= 0.25.0, some examples require seaborn >= 0.9.0.

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.


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.

Source code

You can check the latest sources with the command:

git clone


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 >= 5.0.1 installed):

pytest sklearn

See the web page 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:

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.

Release history Release notifications | RSS feed

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