DMP NBA Player Dataset & Prediction Model

This project analyzes historical NBA data (2012-2024, sourced from Kaggle) using a multi-output Random Forest model (scikit-learn) to predict key player statistics (points, rebounds, etc.). The experiment emphasizes reproducibility and FAIR data practices, producing the trained model, evaluation metrics, visualizations, FAIR4ML metadata, and this DMP as outputs. This work is part of the TU Wien Data Stewardship lecture. Github: https://github.com/bubaltali/nba-prediction-analysis/ DBRepo: https://test.dbrepo.tuwien.ac.at/database/2e167490-c803-4a9a-a317-6e274c6b3a37/info TUWRD. https://handle.test.datacite.org/10.70124/ymgzs-z3s43 The data was taken from: https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years/data