E-Sport Engagement Prediction Using Machine Learning Classification Algorithms - Masrur Mohd Khir; Nur Atiqah Rochin Demong; Siti Noorsuriani Maon (International journal of interactive mobile technologies, 2024)
In recent years, e-sports has experienced a rapid surge in popularity, attracting a vast and diverse audience. As this industry continues to evolve, understanding and predicting e-sport engagement becomes increasingly vital for stakeholders, including game developers, tournament organizers, sponsors, and marketers. Machine learning classification algorithms offer a powerful approach to analyse and forecast user engagement in e-sports, thereby enabling the industry to tailor experiences to individual preferences and behaviours. Thus, this study investigates the level of engagement classification technique of data mining using predictive modelling operations with four different classes, namely strongly agree, either agree or disagree, disagree, and strongly disagree. Machine learning algorithms, particularly classification models, have proven to be effective in analysing large and complex datasets related to e-sport engagement. This study applies statistical techniques to categorize users based on 59 attributes of 106 instances to predict the engagement levels. By training on historical user data, six classification algorithms from two groups, namely bayes and rules, have been used to identify patterns and trends that are indicative of different engagement levels, with the accuracy ranges from 76% to 92%. For feature selection, the result shows that participating in activities, enjoying exchanging ideas, and playing with like-minded gamers were the top three ranking dimensions contributing to the level of engagement. Machine learning classification algorithms have the potential to revolutionize how e-sport engagement is understood and optimized. By analysing diverse data points and leveraging advanced predictive techniques, machine learning algorithms enable stakeholders to tailor e-sport experiences to individual preferences and behaviours, ultimately enhancing user engagement and satisfaction.