Customer Churn Prediction employing Ensemble Learning

In recent years, there has been an enormous increase in the number of companies and of customers for almost every industry. The increment in the number of companies has also provided the choices to the customer but in turn it has also created new challenges. Thus, the companies must work not only to improve their products or services but to sustain customers in the competitive world. Churn prediction is the prediction of customers who are at a potential risk of discontinuing the product or service of the company. Thus, in today’s competitive world, churn prediction is more relevant. In the present work, we have employed various machine learning models for an early prediction of churns, to mitigate the potential risk of losing the customers. The authors have chosen ensemble models for this task. Finally, the models are trained on the dataset. The results for various models are compared using accuracy, precision, recall, and F1 score. Moreover, it is also observed that for our dataset XGBoost outperformed over other models.