AdaForest: Advancing Olympic Medal Prediction with Machine Learning

Accurate prediction of Olympic medal counts is crucial for countries striving to optimize their athletic performance. Traditional prediction methods, primarily reliant on linear regression and sociocconomic variables, often fall short in addressing the complex, non-linear relationships inherent in Olympic performance data. This study introduces AdaForest, a novel ensemble model that combines Bayesian-Optimized Random Forest (BORF) for medal-winning classification and AdaBoost for medal count regression. Through feature engineering, we identify key predictors such as participant counts, historical medal rates, and participation trends. These are validated via Spearman correlation analysis, confirming their strong link to medal outcomes. Trained on historical Olympic data, AdaForest achieves over 98% accuracy in classification and an R-squared value exceeding 80 % in regression for each medal type. Our findings underscore the effectiveness of machine learning techniques in surpassing conventional prediction methods, offering a robust analytical framework for future Olympic success.