Naive Bayes classifiers are built on Bayesian classification methods. These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities. Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems. By using Titanic passenger personal information and whether or not they survived the shipwreck, I demonstrated how to make a prediction of survival using passenger ticket fare information based on Naive Bayes classification.