NBA Player Stat Multi-Output Prediction Model (Random Forest)
A multi-output regression model trained using scikit-learn's MultiOutputRegressor wrapping a RandomForestRegressor. It predicts 6 key end-of-season player statistics (pts, reb, stl, tov, fg3a, fga) based on historical aggregated box-score data from NBA regular seasons and playoffs (2012-13 to 2023-24). Input features undergo preprocessing including dropping identifiers and one-hot encoding.
For training purposes, the seasons 2012-2013 through 2021-2022 were selected.
For testing purposes, the 2022-2023 season was selected.
For training purposes, the seasons 2012-2013 through 2022-2023 were selected.
For testing purposes, the 2023-2024 season was selected.
Three-point field goal attempts in the previous season.
Field goal attempts in the previous season.
Predicted 3-point field goal attempts per season.
Predicted field goal attempts per season.
Evaluation plots for the Playoffs - LAL Subset scenario.
Evaluation plots for the Playoffs - Whole Dataset scenario.
Evaluation plots for the Regular Season - LAL Subset scenario.
Evaluation plots for the Regular Season - Whole Dataset scenario.
Trained on regular-season data (2012–2023) with a single train/test split. Fitting used MAE minimization without additional cross-validation.
R² Score (Regular Season - Whole Dataset - Points)
R² score evaluated on the regular season test set for the 'pts' target.
MAE (Regular Season - Whole Dataset - Points)
Mean Absolute Error evaluated on the regular season test set for the 'pts' target.
R² Score (Playoffs - LAL Subset - Points)
R² score evaluated on the LAL playoff test subset for the 'pts' target.
Project analyzing historical NBA data to predict player and team performance, following FAIR principles.