Work2023

Customer Churn Prediction Using Machine Learning Approaches

R. Srinivasan · D Rajeswari · G. Elangovan

32 citations0/0 files storedInternational Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)

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

Abstract

Customer Churn (CC) is a major issue and important concerns for large organizations and businesses alike. Telecom industries are attempting to improve methods to predict possible customer churn due to the immediate impact on revenue, particularly in the telecom sector. This paper discusses the various ML algorithms used to construct the churn model that helps telecom operators to predict customers who are likely to churn. The experimental results are compared to predict the best model among various techniques. As a result, the use of the Random Forest combined with SMOTE-ENN outperforms best result than other in terms of Fl-score. According to our analysis, the maximum prediction is 95 percent based on Fl-score.

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2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2009Customer churn prediction by hybrid neural networks 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2014Churn prediction using comprehensible support vector machine: An analytical CRM application 2008Predicting credit card customer churn in banks using data mining 2011An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction 2012Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models 2021Effective ML Techniques to Predict Customer Churn

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