Open AccessExploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector
Author(s) -
J. Pamina,
T. Dhiliphan Rajkumar,
S. Kiruthika,
T. Suganya,
F Femila
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a9170.078219
Subject(s) - telecommunications , computer science , field (mathematics) , swing , data science , artificial intelligence , engineering , mechanical engineering , mathematics , pure mathematics
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