Medal Prediction Analysis Based on Random Forest Regression

The aim of this study is to propose a multi-model approach based on random forest regression for Olympic medal result prediction. First, the gold medal and total medal prediction models are constructed using random forest regression, and the multi-source data features are integrated through data mining. Feature engineering is applied to screen key features to improve model performance and interpretability. Second, the impact of athletes and coaches on the number of medals is evaluated based on machine learning algorithms, and scores are calculated by integrating multiple factors to provide data-driven recommendations for investing in sports programs. Finally, an innovative model is designed to predict the impact of new sports events on national medal allocation, providing strategic support for decision-making. This study integrates multiple models and fully utilizes computer technology to process multi-source data, significantly improving the accuracy and applicability of the predictions.