NBA Prediction
For a more detailed analysis, check out the full Github repo here: NBA-Prediction-Analysis You can even copy the notebook and run the code yourself.
Project Goal
The purpose of this project is to create a machine learning model that can accurately predict the outcome of NBA games using boxscore statistics from the past 10 seasons. In order to accomplish this, I:
- built a web-scraper from scratch to collect data on over 12,000 NBA games
- aggregated and processed multiple datasets to prepare them for modeling
- evaluated and iterated upon the models to improve accuracy
Project Summary
All individual models fell short of the target accuracy of 68%. The best machine learning model was a Gaussian Naive-Bayes model that was trained on averaged team statistics from the past 20 games that underwent Principal Component Anaylsis to reduce dimensionality. The model had an accuracy of 63.5%. I then tested an algorithmic approach by creating an Elo rating system, which was the best performing model with an accuracy of 65.3%. With access to additional data and improved data aggregation techniques, I believe it is possible to create an ensemble model that can beat the 68% threshold.