An active machine learning system for forecasting Golden State Warriors game outcomes.
Project overview
This project aims to:
- Analyze historical Warriors game data using feature engineering.
- Build ML models to predict game outcomes.
- Implement an incremental learning system that updates with each new game.
- Make predictions for upcoming games using current season data.
Current implementation
- Data pipeline over historical and current season game data.
- Feature engineering for rolling statistics, streaks, rest days, and matchup context.
- Multiple model tracks for classification (win/loss) and regression (point differential).
- Prediction output generation for upcoming games.
Next step
Continue improving feature variation and incremental update stability to tighten calibration and weekly prediction consistency.