Exploratory Research on Machine Learning Algorithms for Estimating the Effects of Mega-Scale Sporting Events

Abstract

This study focused on identifying the most effective and precise machine learning models for evaluating the effects of major sporting events. This study incorporates advanced selection techniques, such as Least Squares, Stepwise Regression, Support Vector Machines, Boosted Neural Networks, and KNN, to broaden the scope of machine learning applications. This study emphasizes the significance of key factors such as long-term investments, visitor numbers, and GDPs in assessing the impact of these events. This study assessed the contribution of the correlation between the variables and the principal components and each machine learning model performance during the training and validation phases. The results indicate that the Least Squares and Stepwise Regression models performed well during the training phase, whereas the neural-boosted model showed limitations in generalization.