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Predictive Modeling for Athlete Injury Recovery Time and Setback Risk using Random Forest and XGBoost
Effective management of athlete injuries is crucial for optimal performance and career longevity. This thesis introduces a machine learning-based Athlete Injury Recovery Prediction system. Leveraging historical injury data and athlete profiles, the system employs algorithms such as Random Forest and XGBoost to predict recovery times and potential setbacks. Developed using Python and Scikit-learn, the model’s accuracy is enhanced through feature engineering and hyperparameter tuning. A user-friendly interface allows coaches and medical professionals to input data and receive actionable insights. This predictive model aims to revolutionize sports medicine by enabling tailored recovery plans and minimizing downtime for athletes
