Welcome to the InfoSci Platform
Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector

Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector

Babita Majhi, Sachin Singh Rajput, Ritanjali Majhi
Copyright: © 2021 |Pages: 13
ISBN13: 9781799866596|ISBN10: 1799866599|ISBN13 Softcover: 9781799866602|EISBN13: 9781799866619
DOI: 10.4018/978-1-7998-6659-6.ch015
Cite Chapter Cite Chapter

MLA

Majhi, Babita, et al. "Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector." Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, IGI Global, 2021, pp. 262-274. https://doi.org/10.4018/978-1-7998-6659-6.ch015

APA

Majhi, B., Rajput, S. S., & Majhi, R. (2021). Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector. In M. Panda & H. Misra (Eds.), Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (pp. 262-274). IGI Global. https://doi.org/10.4018/978-1-7998-6659-6.ch015

Chicago

Majhi, Babita, Sachin Singh Rajput, and Ritanjali Majhi. "Performance Evaluation of Machine Learning Techniques for Customer Churn Prediction in Telecommunication Sector." In Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, edited by Mrutyunjaya Panda and Harekrishna Misra, 262-274. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6659-6.ch015

Export Reference

Mendeley
Favorite

Abstract

The principle objective of this chapter is to build up a churn prediction model which helps telecom administrators to foresee clients who are no doubt liable to agitate. Many studies affirmed that AI innovation is profoundly effective to anticipate this circumstance as it is applied through training from past information. The prediction procedure is involved three primary stages: normalization of the data, then feature selection based on information gain, and finally, classification utilizing different AI methods, for example, back propagation neural network (BPNNM), naïve Bayesian, k-nearest neighborhood (KNN), support vector machine (SVM), discriminant analysis (DA), decision tree (DT), and extreme learning machine (ELM). It is shown from simulation study that out of these seven methods SVM with polynomial based kernel is coming about 91.33% of precision where ELM is at the primary situation with 92.10% of exactness and MLANN-based CCP model is at third rank with 90.4% of accuracy. Similar observation is noted for 10-fold cross validation also.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.