Nowadays, businesses are highly competitive as most markets are extremely saturated. As a result, customer management is of critical importance to avoid dissatisfaction that leads to customer loss. Thus, predicting customer loss is crucial to efficiently target potential churners and attempt to retain them. By classifying customers as churners and non-churners, customer loss is equated to a binary classification problem. In this paper, a new real-world dataset is used, originating from a popular web-based drug information platform, in order to predict subscriber churn. A number of methods that belong to different machine learning categories (linear, nonlinear, ensemble, neural networks) are constructed, optimized and trained on the subscription data and the results are presented and compared. This study provides a guide for solving churn prediction problems as well as a comparison of various models within the churn prediction context. The findings co-align with the notion that ensemble methods are, in principle, superior whilst every model maintains satisfying results.