This chapter implements the Complete Bayes algorithm and covers the Naive Bayes classifier. The approach known as Naive Bayes roughly makes the “naive” assumption that the input variables are independent of each other. Data Science is a practical exercise, and if we cannot use an algorithm in the real world, we must push on to find something else that will work. The chapter also covers probability and in particular both Bayes Theorem and conditional independence. Naive Bayes is clearly based on the concepts of probability. There are other algorithms that are also based on probability. the concept of an event is part of probability theory and may have nothing to do with other algorithms. In fact, when we study neural networks, there is no necessity of discussing events.