Abstract
This research work investigates the use of machine learning algorithms (Linear Regression and K-Nearest Neighbour) for NFL games result prediction. Data mining techniques were employed on carefully created features with datasets from NFL games statistics using RapidMiner and Java programming language in the backend. High attribute weights of features were obtained from the Linear Regression Model (LR) which provides a basis for the K-Nearest Neighbour Model (KNN). The result is a hybridized model which shows that using relevant features will provide good prediction accuracy. Unique features used are: Bookmakers betting spread and players’ performance metrics. The prediction accuracy of 80.65% obtained shows that the experiment is substantially better than many existing systems with accuracies of 59.4%, 60.7%, 65.05% and 67.08%. This can therefore be a reference point for future research in this area especially on employing machine learning in predictions.
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 callMore From: International Journal of Computer Applications Technology and Research
Disclaimer: 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.
