Research on Olympic medal prediction using random forest regression model

Under the background of globalization, sports competition among countries is becoming increasingly fierce. How to scientifically predict the number of Olympic medals has become an important issue for enhancing a country's sports strength. Based on the data of the Summer Olympics from 1896 to 2024, this study constructed an Olympic medal prediction model based on machine learning algorithms to accurately predict and analyze the number of medals for each country. First, we analyze the original data set and preprocess outliers and missing values. Then, feature variables are created from aspects such as previous Olympic champions, the advantages of the host country, the scale of the event and the strength of the athletes. Finally, the random forest algorithm was used to predict the Olympic medals of various countries in 2028. The research results show that the United States will lead the medal table with 41 gold medals and 122 medals, followed closely by China. Traditional sports powerhouses such as Japan, the United Kingdom, Australia and France are expected to achieve excellent results. Emerging competitors such as Germany and the Netherlands are also expected to achieve excellent results. The results of this study can help sports administrative institutions better formulate strategies and allocate resources.