An Optical Ensemle Classification for Predicting Churn in Telecommunication

S. Chenthur Pandian

9 citations0/0 files storedJournal of Engineering Science and Technology Review

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Churn Prophecy is the most salient sphere in the telecom industry for planning, decision making and to know the future events.Due to the tremendous amount of data, prediction has been always a tangled work.The prominent objective of churn Prophecy model is to strongly classify the customers into clusters and predict the prospective churners.Efficient feature selection is also required for predicting the churners.In this paper, an overview of recent machine learning works for customer churn prediction has provided.This paper provides XG boost classifier which less focused in the previous works.XG boost classifier is applied on publicly available telecom dataset and experiential results are compared with KNN and Random Forest Classifiers.XG boost classifier performs superior out of three classifiers mentioned.The evaluated metrics such as Precision, Recall, F1-score, Support are calculated.Finally, it is observed that among all the features, Fiber Optic customers with greater monthly chargers has higher cognition for churn using XG boost classifiers.

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2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2005Applying data mining to telecom churn management 2015A comparison of machine learning techniques for customer churn prediction 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2009Customer churn prediction by hybrid neural networks 2018Customer churn prediction in telecommunication industry using data certainty 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2014Improved churn prediction in telecommunication industry using data mining techniques

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