Basketball Data Mining and Analytics

This project aims to harvest basketball data from reputable sites, namely Basketball Reference in an attempt to mine interesting patterns in order to visualize and assess basketball game winning formula for the current era.

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Installation

Install Python 3.8 with pip

NBA_api_test requires pip install nba_api

standard_api_test requires pip install request

libraries/packages required to run the calculations: pip install sklearn pip install matplotlib pip install mpl_toolkits pip install numpy pip install csv

Run Calculations.py to generate data that goes into CalculationResults

Usage

grandTeamLogs

Contains all the raw NBA result data from Basketball Reference

Training data

Contains processed NBA stat as csv to train our linear regression model

Run the calculations

To get the predicted results by all of our methods. Run the calculations.py file using this command line in terminal:

python3 calculation.py

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT