Olympic medal prediction model based on XGBoost

In the summer Olympic Games medal list prediction problem, for the problems of insufficient prediction accuracy of traditional statistical models and limited machine learning ability under a single index system, this paper proposes an XGBoost prediction method based on multidimensional data fusion.Combining historical medal data with multi-dimensional features such as comprehensive national power of sports, national development index, Olympic sub-input intensity, national health index, etc., constructing composite feature vectors as model inputs, and through feature engineering optimization and parameter tuning, an XGBoost integrated learning model applicable to Olympic medal prediction is established.The experimental results show that the model prediction results have a high degree of consistency with the actual medal distribution, and compared with the traditional statistical model and the prediction method under the single index system, the multidimensional data fusion method proposed in this paper has higher prediction accuracy, especially in the long-period prediction task, which shows superior performance.The results of the research are important for promoting cross-fertilization in the fields of sports science, statistics and forecasting.