A random forest regression model predicting the winners of summer Olympic events - Jia, M.; Zhao, Y.; Chang, F.; Zhang, B.; Yoshigoe, K. (Proceedings of the 2020 2nd International Conference on Big Data Engineering, 2020)
From the past Olympic medal lists, we can find that the number of medals of China has been increasing steadily in recent years while we also observe that some countries always occupy the top positions of the Olympic medal list, such as the United States, Britain and Germany. In this work we take the data of the medal lists from the 18th to 31st Summer Olympic Games as a sample and selects GDP, the population, the size of national team and the home advantage as the characteristic parameters to build a random forest regression model to predict the number of medals. The FP-growth algorithm is used to analyze the association rules of the data. And the winners of some events in the 2020 Tokyo Olympic Games are predicted.