College Football Playoff Predictor
A machine learning model estimating College Football Playoff selection from historical season data
Overview
- Built in a Kaggle notebook using historical college football data to model team performance and estimate each team's probability of making the playoff
- Engineers features from historical season data to represent team strength
- Trains and compares classifiers (Random Forest vs. XGBoost) for CFP selection
- Outputs playoff probabilities and comparative insights across contending teams
Tech Stack
| Layer | Tools |
|---|---|
| Data Prep | Pandas, NumPy |
| Modeling | Scikit-learn (Random Forest), XGBoost |
| Environment | Kaggle Notebooks |
Skills Developed
- Data preprocessing and feature engineering
- Supervised classification and model evaluation
- Communicating model output as probabilities