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.
scikit-learn requires: - Python (>= 2.7 or >= 3.4) - NumPy (>= 1.8.2) - SciPy (>= 0.13.3) For running the examples Matplotlib >= 1.3.1 is required. A few examples require scikit-image >= 0.9.3 and a few examples require pandas >= 0.13.1. 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 <http://scikit-learn.org/stable/modules/computational_performance.html#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 install -U scikit-learn
conda install scikit-learn
The documentation includes more detailed
installation instructions <http://scikit-learn.org/stable/install.html>_.