Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction

Shixuan Wei

doi:10.54254/2753-8818/2025.AD26483

Theoretical and Natural ScienceOpen access

Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction

Research Article

Open Access

Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction

Shixuan Wei ^1*

¹ James Cook University

^*Corresponding author: Shixuan.wei@my.jcu.edu.au

Published on 3 September 2025

TNS Vol.134

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-343-7

ISBN (Online): 978-1-80590-344-4

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Abstract

Customer churn has consistently been a significant issue within the telecommunications sector. The timely and accurate identification of high-risk churn customers is essential for improving customer satisfaction and operational profitability. With the rise of data-driven decision-making, predictive modelling has become a critical tool for telecom operators to mitigate churn risks. This study leverages the publicly available Telco Customer Churn dataset from Kaggle and employs R to construct and evaluate three classical machine learning models: Logistic Regression, Decision Tree, and K-Nearest Neighbours (KNN). Then, after preprocessing, feature selection, and hyper-parameter tuning, the models are evaluated with score metric and multi-metric evaluation to predict customer churn. The results of the research showed that it is 81.1% accurate, 72.5% precise in accuracy and interpretability compared to other models since it is a very interpretable model, as you can see from the Receiver Operating Characteristic (ROC) curve also. All models have specific weaknesses in predicting churned users; however, as a whole, they manage to achieve good accurate results. This study can offer an empirical and technical foundation for southern industrial companies to analyze churn and do data management, which is vital in the telecom industry.

Keywords:

Customer Churn, Machine Learning, Logistic Regression, Decision Tree, K-Nearest Neighbours

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References

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[9]. Najar, S., Chahbar, M., & Mouhoub, M. N. (2021). Predicting customer churn in the telecommunication industry using machine learning techniques. Procedia Computer Science, 194, 229-236.

[10]. Ullah, I., Aslam, M., Latif, S., & Qureshi, K. N. (2019). Customer churn prediction in telecommunication industry using ensemble learning. TELKOMNIKA, 17(3), 1305–1311.

[11]. Zhu, X., et al. (2022). A review of machine learning for customer churn prediction. Artificial Intelligence Review, 55(4), 3213–3236.

[12]. Kaggle. (2023). Telco Customer Churn Dataset.

References

[2]. Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Example-dependent cost-sensitive decision trees. Expert Systems with Applications, 42(19), 6609–6619.

[5]. Amin, A., Anwar, S., Adnan, A., et al. (2019). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.

[6]. Huang, B., Kechadi, T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), 1414–1425.

[8]. Keramati, A., Jafari-Marandi, R., Aliannejadi, M., et al. (2014). Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994–1012.

[9]. Najar, S., Chahbar, M., & Mouhoub, M. N. (2021). Predicting customer churn in the telecommunication industry using machine learning techniques. Procedia Computer Science, 194, 229-236.

[10]. Ullah, I., Aslam, M., Latif, S., & Qureshi, K. N. (2019). Customer churn prediction in telecommunication industry using ensemble learning. TELKOMNIKA, 17(3), 1305–1311.

[11]. Zhu, X., et al. (2022). A review of machine learning for customer churn prediction. Artificial Intelligence Review, 55(4), 3213–3236.

[12]. Kaggle. (2023). Telco Customer Churn Dataset.

Cite this article

Wei,S. (2025). Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction. Theoretical and Natural Science,134,24-30.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Controlling Robotic Manipulator Using PWM Signals with Microcontrollers

ISBN: 978-1-80590-343-7(Print) / 978-1-80590-344-4(Online)

Editor: Marwan Omar, Mustafa Istanbullu

Conference website: https://2025.confapmm.org/adana.html

Conference date: 19 September 2025

Series: Theoretical and Natural Science

Volume number: Vol.134

ISSN: 2753-8818(Print) / 2753-8826(Online)

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