Designing unsupervised machine learning algorithms to explore user behaviour patterns for online games

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The massive amount of time-varying and high dimensional data generated from online games provides new opportunity to both the gaming industry and researchers to derive actionable insights. These data potentially allow businesses to predict their revenues, improve technical performances to enhance users' online gaming experience, develop new games based on users feedback or interests, and, most importantly, predict the behaviour of players with implications for future of a given game. Understanding players' behaviour is crucial as it allows the identification of both valuable players and also those players that are ready to churn. The objective of this project is to investigate how data generated by the game players can be analyzed and whether behavioural patterns can be identified.