sports-betting is a tool that makes it easy to create machine learning based models for sports betting and evaluate their performance. It is compatible with scikit-learn.
Installation documentation, API documentation, and examples can be found on the documentation.
sports-betting is tested to work under Python 3.6+. The dependencies are the following:
- numpy(>=1.1)
- scikit-learn(>=0.21)
- imbalanced-learn(>=0.4.3)
Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).
sports-betting is currently available on the PyPi's repository and you can install it via pip:
pip install -U sports-betting
The package is released also in Anaconda Cloud platform:
conda install -c algowit sports-betting
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/AlgoWit/sports-betting.git cd sports-betting pip install .
Or install using pip and GitHub:
pip install -U git+https://github.com/AlgoWit/sports-betting.git
After installation, you can use pytest to run the test suite:
make test
Download data:
download
Apply backtesting:
backtest
Make new predictions:
predict