In classification or class prediction, we try to use the information from the predictors or independent variables to sort the data samples into two or more distinct classes or buckets. Classification is the most widely used data mining task in business. There are several ways to build classification models. In this chapter, we will discuss and show the implementation of six of the most commonly used classification algorithms: decision trees, rule induction, k-nearest neighbors, naïve Bayesian, artificial neural networks, and support vector machines. We conclude this chapter with building ensemble classification models and a discussion on bagging, boosting, and random forests.