Developed a polynomial regression model to predict the points scored by NBA players in matches based on comprehensive training data from each team. The model leverages various features, including player stats, training intensity, and game conditions, to identify non-linear relationships and improve accuracy. By analyzing historical performance and training patterns, this predictive model aims to provide valuable insights for coaches and analysts, facilitating better game strategies and player development decisions.