An Optimized Kernel MSVM Machine Learning-based Model for Churn Analysis

Abstract

Customer churn is considered as a significant issue in any industry due to various services, clients, and commodities. A massive amount of data is being created from e-commerce services and tools. Analytical data and machine learning-based approaches have been implemented and utilized for CA (churn analysis) to design a plan, i.e., required to comprehend the rationale for the CC (Customer Churn) and to generate a profitable and actual customer holding program. The analytics and machine learning approaches mainly focus on customer profiling, CC classification, and detection of features that affect churn. However, there are no specific techniques which can be used to determine how often a prospective customer is inclined to cover all the expenses whether they are churned or not. In this paper, an Optimized Kernel MSVM classification model is proposed to predict and classify churn. In the proposed work, MSVM algorithm has been used for classification. The kernel PCA and ALO optimizer method has been used for Feature extraction and selection. The proposed model Optimized Kernel MSVM has been implemented on Tele-communication sector customer churn database to demonstrate the proposed model's generalization ability. The Optimized Kernel MSVM model has achieved an accuracy of 91.05%, AUC 85% being maximum and reduced the RMSE score to 2.838. The implementation shows that both churn detection and classification may be examined at the same time while maintaining the highest overall accuracy and AUC.