Using machine learning to predict potential online gambling addicts.
Loading...
URL
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
2018-10-08
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
45
Series
Abstract
Betting addicts on the gambling websites are difficult to identify because online gambling is by nature different from real gambling. This thesis attempts to identify potential gambling addicts in an online gambling website X using machine learning models. The models are based on user’s usage history on the website. The usage data is collected for each user from the site using JavaScript. The data is then analyzed and stored in a database. Machine learning models are then trained using Support Vector Machines with the data of users who are by definition problem gamblers. The system then makes a prediction for all active users based on their recent usage history. The final results include an automated system for daily learning and prediction of potential problem gamblers who show early signs of gambling addiction.Description
Supervisor
Gionis, AristidesThesis advisor
Mantere, MarkkuKeywords
betting addiction, machine learning, classification, predictive analysis