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.