Machine learning driven responsible gaming framework with apache spark

Abstract

This paper tackles an important and challenging problem of protecting players from irresponsible gambling behavior. Such prevention is a crucial and mandatory obligation for major gambling providers. The paper presents a novel machine learning driven solution for implementing the responsible gaming facility. The engine leverages two powerful machine learning algorithms: random forest and gradient boosting. The tests were actualized by reusing a publicly available dataset provided by Transparency Project. The final results confirm that the proposed implementation of the framework passes the criteria as a proof of concept solution.