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- W4313269944 abstract "Previous studies had shown that attendance of sports games possesses high theoretical and practical value, but there is little related prediction research. Based on the related game demand analysis, this paper conducts a more complete framework for forecasting the demand of sports games with several machine learning algorithms. Based on the extreme gradient boosting(XGBoost) model, we further analyzed the importance of factors that affect spectator demand. In the prediction, we mainly considered machine learning models, such as multivariate adaptive regression splines(Mars), classification and regression(CART), backpropagation neural network(BPNN), support vector regression(SVR), random forest(RF), and XGBoost. The results indicate that the XGBoost model gets the best performance. By comparing features' importance, we found the size of the stadium, the economic and attractive factors are crucial for sports game attendance prediction, which are consistent with previous economics studies." @default.
- W4313269944 created "2023-01-06" @default.
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- W4313269944 date "2022-10-28" @default.
- W4313269944 modified "2023-10-18" @default.
- W4313269944 title "Sports Games Attendance Forecast Using Machine Learning" @default.
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- W4313269944 doi "https://doi.org/10.1109/icdsca56264.2022.9987748" @default.
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