With the advancement of technology, companies are able to foresee the customers who are going to leave their organization much before. This problem of customer churn prediction is handled in the current work. In the real-world, data is not balanced, having more observations with respect to few classes and less observations in case of other classes. But, giving equal importance to each class is really significant to build an efficient prediction model. Moreover, real-world data contains many attributes meaning that the dimensionality is high. In the current paper, we discussed three data balancing techniques and two methods of dimensionality reduction i.e. feature selection and feature extraction. Further, selecting the best machine learning model for churn prediction is an important issue. This has been dealt in the current paper. Also, we aim to improve the efficiency of customer churn prediction by evaluating various class balancing and dimensionality reduction techniques. Moreover, we evaluated the performance of the models using AUC curves and K-fold cross-validation techniques.