Olympic Medal Prediction Based on Machine Learning Models

The Olympic Games are the highest stage for global sports competition, and the number of medals is an important indicator of a country's athletic strength. This study uses machine learning methods such as Gradient Boosting and Random Forest models to predict the number of gold medals and total medals for each country in the 2028 Los Angeles Olympic Games, while also exploring the medal potential of emerging nations. For countries with historical medal data, a Random Forest model is constructed with features including host country status, historical medal count, athlete ratings, and the number of events participated in. Model predictions show that traditional sports powerhouses such as the United States, China, and Russia will continue to dominate the medal table, with the host country, the United States, expected to win more medals. For emerging countries, the Gradient Boosting model predicts that eight countries have a greater than 50% chance of winning medals for the first time in 2028. Furthermore, K-means clustering analysis reveals the strengths of different countries in specific events and the impact of the host country's status on medal distribution. This study provides a comprehensive methodological framework for Olympic medal prediction and offers important references for the formulation of national sports policies.