Machine learning techniques such as regression, Naïve Bayes, decision tree, and SVM discussed in the previous chapters are mainly used for prediction purposes. However, there are other analytical techniques that play an important role in data analysis such as clustering, association rule mining, random forest, principal component analysis, and logistic regression. Clustering is an analytical technique that groups similar objects into two or more groups using the different data attributes and features. Association rule mining is widely used in e-markets for enhancing the customer purchases by providing the suitable offers for the customers. Random forest is a classification technique that builds a number of decision trees based on the decision points in the classification. In this chapter, these analytical techniques are discussed with examples.