A grid search optimized extreme learning machine approach for customer churn prediction
Customers' behaviors such as tendencies, loyalty status, satisfaction criteria show an alteration day by day due to the changing world. So, these behavior changes should be analyzed very well in every step of the decision-making process. Customer churn analysis is the determination of customers who tend to leave by analyzing the customer data with various methods before this situation occurs. Customer churn analysis is very important to take the proper steps to minimize customer losses. In this study, a new approach based Extreme Learning Machine (ELM) has been used to solve customer churn prediction problem. It is aimed to investigate the parameters of the algorithm that produce the best solution with grid search optimization. Also, a modified accuracy calculation approach has been presented. The churn data set obtained from the UCI Machine Learning Repository has been used to determine the effectiveness of the ELM. Naive Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) methods are selected for performance comparison of the model. With a value of 93.1%, the best accuracy measure has been obtained with ELM. Due to the low number of parameters to be determined and performance evaluation measures that compete with other models’ results, it can be said that the ELM is highly effective and interesting in the solution of the problem.