Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector
J. Pamina1, T. Dhiliphan Rajkumar2, S. Kiruthika3, T. Suganya4, Femila.F5
1J.Pamina, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, (Tamil Nadu), India.
2T. Dhiliphan Rajkumar, Department of Computer Science and Engineering, , Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Srivilliputur Post-626126, Virudhunagar District, (Tamil Nadu), India.
3S.Kiruthika, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, (Tamil Nadu), India.
4T.Suganya, Department of Computer Science and Engineering, Sri krishna college of Technology, Coimbatore, (Tamil Nadu), India.
5Femila F, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, (Tamil Nadu), India.
Manuscript received on 03 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 299-308 | Volume-8 Issue-2, July 2019 | Retrieval Number: A9170058119/19©BEIESP | DOI: 10.35940/ijrte.A9170.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Most prominent challenges in all business is to retain and satisfy their valuable customers for sustain successfully in the market. Numerous Machine learning approaches are emerging to develop various customer retention models to solve this issue in many applications. This swing is more realized in telecom industry due its enormous significance. This article presents an elaborated survey on machine learning based churn prediction in telecom sector from the year 2000 to 2018. We also extracted the problems and challenges in Telecom Churn Prediction and reported suggestion and solutions. We believe this article helps the researches or data analysts in the telecom field to select optimal and appropriate methods and for designing improved novel model for churn prediction in future.
Index Terms: Churn Prediction, Machine Learning, Survey, Telecom.
Scope of the Article: Machine Learning