AI-driven golf performance analysis (PGA Tour 2015–2022) — Colab notebook, models, and SHAP interpretability
Author: Yuanzhen (Jane) Wei
This project uses PGA Tour data (2015–2022) to predict round-level golf performance using machine learning (Linear Regression, Random Forest, XGBoost) and interpret results with SHAP.
Main file: ai_golf_performance_analysis.ipynb
Goal: Identify which metrics (e.g., strokes-gained, putting, driving) most affect total strokes per round.
How to view:
- Open the notebook directly on GitHub, or
- Download it and open in Google Colab.
Next steps:
- Extend analysis with additional seasons or player-level features
- Compare results with amateur/junior datasets
- Publish as a high-school research project