Abstract
As a matter of fact, the Word Cup predictions attracts lots of scholars on account of its complex features. In recent years, various machine learning models are proposed to realize the accurate predictions. With this in mind, in this study, the author will compare the advantages and disadvantages of five models, i.e., logistic regression, decision tree, random forest, neural network, and support vector machine, in predicting World Cup matches. The author used the results of past World Cup matches as the original dataset. After research, the author concluded that the logistic regression model was the most effective. While Neural Networks and Support Vector Machines followed closely in predictive accuracy, they also showed promising potential. Decision tree and the random forest models suffer from severe overfitting, and the author believe that these two models are difficult to apply in this field. Overall, these results shed light on guiding further exploration or World Cup prediction.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
