The focus of this project is in predicting customer churn. It is essential to consider and handle the imbalance problem, that is why this project explains the imbalance problem, states its importance and presents some methods to handle it. It continues to describe the algorithms of three classification algorithms; logistic regression, classification trees and random forest. Finally, it states the importance of using appropriate assessment metrics and uses suitable ones to evaluate the performance of the imbalance techniques and models. The results were contradicting to the conclusions found in scientific literature due to the underlying nature of the data. Further analysis should be done to understand some of the results obtained.