Prediction of the Digital Game Rating Systems based on the ESRB

  • Khaled Mohammad Alomari, Ahmad Qasim AlHamad Faculty of Computer Sciences, Abu Dhabi University, Abu Dhabi, UAE
  • Hisham o. Mbaidin Faculty of Management Information Systems, Mutah University, Karak, Jordan
  • Said Salloum Fujairah University, Fujairah, UAE
Palabras clave: Video game, Digital Game Rating Systems, Machine Learning.


This study tries to find out the best model for prediction video game rate categories. A representation from four rating categories (everyone, everyone 10+, teen, mature) was used for the analysis. The paper follows CRISP-DM approach under Rapid Miner software to business and data understanding, Data preparation, model building and evaluation. The researchers compared prediction among six model and the results showed that the Generalized Linear Models (GLMs) achieved a best accuracy (0.9027), also results highlighted eight important content descriptions to have the highest influence on prediction.
Cómo citar
Ahmad Qasim AlHamad, K. M. A., Mbaidin, H. o., & Salloum, S. (2019). Prediction of the Digital Game Rating Systems based on the ESRB. Opción, 35, 1368-1393. Recuperado a partir de