Analyzing the impact of sports participation on academic growth using machine learning techniques

In recent years, physical activity has become one of the most prominent areas of study, emphasizing how it contributes to well-rounded student development. By analyzing and predicting performance outcomes, this study analyzes and examines the impact of sports participation on academic growth. In order to develop and evaluate machine learning models, we used a dataset consisting of academic records, frequency and type of sports involvement, as well as other demographic attributes related to the students. Academic improvement is positively correlated with regular engagement in sports activities, and key predictors include physical fitness indicators, duration of participation, and type of sport. The findings indicate the potential of data-driven approaches for uncovering hidden patterns and supporting policy development regarding the integration of physical education into academics. As sports play an increasingly important role in academic environments, this study highlights the role of machine learning in educational analytics.