.. image:: https://travis-ci.org/GoldenCheetah/scikit-sports.svg?branch=master :target: https://travis-ci.org/GoldenCheetah/scikit-sports
.. image:: https://ci.appveyor.com/api/projects/status/tei5gfnma8uxf7u8?svg=true :target: https://ci.appveyor.com/project/glemaitre/scikit-sports
.. image:: https://readthedocs.org/projects/scikit-sports/badge/?version=latest :target: https://scikit-sports.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status
.. image:: https://codecov.io/gh/GoldenCheetah/scikit-sports/branch/master/graph/badge.svg :target: https://codecov.io/gh/GoldenCheetah/scikit-sports
Dependencies
Scikit-sports requires:
* scipy
* numpy
* pandas
* six
* fit-parse
* joblib
* scikit-learn
Installation
scikit-sports
is currently available on the PyPi’s reporitories and you can
install it via pip::
pip install -U scikit-sports
The package is release also in conda-forge::
conda install -c conda-forge scikit-sports
If you prefer, you can clone it and run the setup.py
file. Use the
following commands to get a copy from Github and install all dependencies::
git clone https://github.com/scikit-sports/scikit-sports.git cd scikit-sports pip install .
Or install using pip
and GitHub::
pip install -U git+https://github.com/scikit-sports/scikit-sports.git