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The Naive Bayes classifier is a probability-based classifier. It is an algorithm that tries to find the final probabilities P (Cj | A) of the test data with the help of the preliminary probabilities P (A | Cj) learned from the training data. The algorithm is based on the Bayes’ theorem [30]. According to Bayes’ theorem, events are interrelated and there is a relationship between probabilities P (A | C) and P (C | A), P (A), P (C). Therefore, while calculating the value of P (A | C) with the help of Bayes, we use the equation P (C | A) = (P (A | C) P (C)) / (P (A)). The Naive Bayes approach is used to solve the zero probability problem of Bayesian approach. Thanks to the naive approach, it is assumed that there is no relationship between the events and the process is shortened. Thus, it is possible to get rid of sparsity in the data relatively.