scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and 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 AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

Scikit-learn requires:

- Python (>= 2.6 or >= 3.3),
- NumPy (>= 1.6.1),
- SciPy (>= 0.9).
scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries for known issues.

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 scikit-learn
The documentation includes more detailed installation instructions.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Contributor's 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: http://sourceforge.net/projects/scikit-learn/files/
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
Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/ ... ter/CONTRIBUTING.md