TEJAS Journal of Technologies and Humanitarian Science

Ravindra Chaurasia
Scholar, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

Vaishnavi Srivastava
Scholar, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

Sumit Chaurasiya
Scholar, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

Shubham Singh
Scholar, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

Anurag Singh
Assistant Professor, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

📜 License:

This work is licensed under a Creative Commons Attribution 4.0 International License

• Share — Copy and Redistribute the material
• Adapt — Remix, Transform, and build upon the material
• The licensor cannot revoke these freedoms as long as you follow the license terms.

Abstract:

Customer churn—the loss of existing clients—poses a major challenge for business growth. This study predicts churn using structured datasets containing demographics, transaction history, and engagement metrics. Multiple machine learning models, including XGBoost, Random Forest, and LightGBM, were trained and evaluated using accuracy, precision, recall, and F1-score. XGBoost achieved the highest predictive performance, effectively identifying at-risk customers while minimizing false positives. The research provides a practical framework for integrating machine learning into customer relationship management systems, enabling timely interventions and data-driven strategies to improve retention, reduce churn, and enhance long-term revenue stability

References

[1]. Idris, A., Khan, A., & Lee, Y. (2021). Customer Churn Prediction in Telecom Industry using Machine Learning Techniques. Journal of Big Data, 8(1), 34. https://doi.org/10.1186/s40537-021-00455-5
[2]. Amin, A., & Zafar, M. (2020). Predicting Customer Churn using XGBoost and Random Forest Classifiers. International Journal of Advanced Computer Science and Applications, 11(7), 120– 127. https://doi.org/10.14569/IJACSA.2020.0110716
advanced [3]. Verbeke, W., Martens, D., & Baesens, B. (2014). Building comprehensible customer churn prediction models with analytics. Expert Systems with Applications, 41(4), 2014–2028. https://doi.org/10.1016/j.eswa.2013.08.041
[4]. Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626–4636. https://doi.org/10.1016/j.eswa.2008.06.056
[5]. Huang, B., Kechadi, T., & Buckley, B. (2012). Customer Churn Prediction in Telecommunications. Expert Systems with Applications, 39(1), 1414–1425. https://doi.org/10.1016/j.eswa.2011.08.024
[6]. Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review. Expert Systems with Applications, 36(2), 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021
[7]. Suryanarayana, G., & Rao, P. (2021). A Comparative Study of Machine Learning Models for Customer Churn Prediction. Procedia Computer Science, 184, 543–552. https://doi.org/10.1016/j.procs.2021.03.067
[8]. IBM Knowledge Center. (2020). Customer Churn Management Retrieved from https://www.ibm.com/docs/en/spss-modeler/18.2.0
[9]. Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473–2480. https://doi.org/10.1016/j.eswa.2007.12.020 (Used widely in churn-related predictive modelling studies)
[10]. Gao, C., Xu, Y., & Duan, Y. (2020). An Improved Random Forest Algorithm for Telecom Customer Churn Prediction. IEEE Access, 8, 150087–150095. https://doi.org/10.1109/ACCESS.2020.3015880
[11]. Gupta, S., & Rani, R. (2021). Customer Churn Prediction in Telecom Sector using Machine Learning Algorithms. Materials Today: Proceedings, 46, 9926–9931. https://doi.org/10.1016/j.matpr.2021.03.471