The goal of this project was to implement a probabilistic classifier based on Bayesian theorem. Bayesian method is a probabilistic learning method which requires both prior probabilities, kind of assumptions, and posterior probabilities, that is determined after observing different data about the hypothesis. Therefore Bayesian classifiers combines predictions of the alternative hypotheses and their weighted posterior probabilities to calculate the most probable classification of the new instance.In this project two different implementations were used for the task. The first implementation uses the regular formula. The second implementation is created for the big documents, which includes more than 30 words, because when the classifier is tested on 20 news data set, the limitations of the floating points are realised.