Enhanced Framework to Predict Customer Churn Using Machine Learning

The leading challenge for most businesses is customer churn; it is very hard for every business organization to maintain their customers. If customers are not satisfied with the service provided by an organization, they will switch to another organization that better meets their needs. In terms of churn prediction (CP), many research work was proposed by researchers, who have used machine learning (ML) algorithms for prediction. In Telecommunication sector, due to switchover of the customer to other organization, the service provider will face a huge loss. Churn prediction in any type of business to preserve their customers by meeting all the expectations of the customer. A framework has been designed for estimating the customer exit within the banking industry by applying Machine Learning Algorithms. H2O AutoML is used to predict the churn from the banking data. By leveraging H2O AutoML, we employed different kinds of machine learning approaches, Like Stacked Ensemble, Gradient Boosting Machines (GBM), along with Deep Learning approaches. Results demonstrate that the suggested framework attained higher predictive accuracy, surpassing the performance of individual models in metrics like RMSE, MAE, and overall accuracy rates. It will help the banking sector to retain their customers.