Implementation of Random Forest Algorithm Based Medal Count Prediction

Abstract

This paper proposes a data trend prediction model based on random forest regression, focusing on the comprehensive use of machine learning and association rule mining in complex data prediction. Firstly, considering various factors, we constructed a medal prediction model based on random forest regression, assessed the accuracy of data, predicted the medal list of the 2028 Olympic Games and the first medal prediction results of the non-winning countries, and at the same time, we used the Apriori algorithm to analyze the relationship between the setting of the competition events and the distribution of the medals. Then, a regression model was constructed with “coaching effect” as the main variable, and the coefficients of “coaching effect” were judged according to the contribution degree of the coefficients. By applying the MK mutation test, we select and evaluate the changes in the number of medals of the three countries after investing in “great coaches”. The model analyzes and validates the impact of great coaches on sports performance through a predictive model with excellent accuracy assessment obtained from Random Forest Regression.