This thesis explores development of predictive models on real life datasets. The goal of approaches, described in this thesis, is modeling and prediction of players' behavior in casino industry. The basis for creation of predictive model is preparation of real life datasets for machine learning algorithms. Sensible curation of real life datasets that include missing values, inaccuracies and other noise determines the possibility for development of accurate predictive models. First goal of predictive behavioral modeling is creation of automated prediction model that predicts if the player will make a second deposit. Accuracy of developed model is sufficient for implementation in real life casino operation. Second goal is to develop broader predictive model that describes and predicts development of player’s behavior. Sensibility of proposed approach and its compliance with domain demands is presented. Real predictive strength of proposed model is out of scope of this work as it requires a lot of additional domain knowledge.