Analysis of NBA Players and Shot Prediction Using Random Forest and XGBoost Models

This paper proposes a comparative study using machine learning algorithms to predict the shooting success by basketball players in the National Basketball Association (NBA). This work is focusing on analyzing NBA's regular session dataset, which will help NBA teams to prepare their play plan for future games based on the other team players' performance. For instance, how good is each player usually in shooting from different distance and what defense strategies they often use. In this work, Random Forest and XGBoost models are used for shooting prediction.