In modern casinos, personnel exist to advise, or in some cases, order

individuals to stop gambling if they are found to be gambling in a destructive

way, but what about online gamblers? This thesis evaluated

the possibility of using machine learning as a supplement for personnel

in real casinos when gambling online. This was done through supervised

learning or more specifically, a decision tree algorithm called

CART. Studies showed that the majority of problem gamblers would

find it helpful to have their behavioral patterns collected to be able to

identify their risk of becoming a problem gambler before their problem

started. The collected behavioral features were time spent gambling, the

rate of won and lost money and the number of deposits made, all these

during a specific period of time. An API was implemented for casino

platforms to connect to and give collected data about their users, and

to receive responses to notify users about their situation. Unfortunately,

there were no platforms available to test this on players gambling live.

Therefore a web based survey was implemented to test if the API would

work as expected. More studies could be conducted in this area, finding

more features to convert for computers to understand and implement

into the learning algorithm.