References
- Agarwal, P., Data Science Approaches for Churn Prediction, 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-7, doi: 10.1109/ICCCNT61001.2024.10723983.
- Ahmed, J., Younis, I., Sarwar, U., Ghaffar, R., & Ahmed, T. (2024). Leveraging Machine Learning Models for Customer Churn Prediction in Telecommunications: Insights and Implications. VAWKUM Transaction on Computer Sciences, 12(2), 16–27. doi: 10.21015/vtcs.v12i2.1904
- Ako, R. E., Malasowe, B. O., Okpor, M. D., Akazue, M. I., Yoro, R. E., Ojugo, A. A., Setiadi, D. R. I. M., Odiakaose, C. C., Abere, R. A., Emordi, F. U., Geteloma, V., & Ejeh, P. O. (2024). Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost. Journal of Computing Theories and Applications, 2(1), 86–101. https://doi.org/10.62411/jcta.10562
- Alhakim, M. F., Petchhan, J., & Su, S. (2024). Leveraging TabNet for Enhanced Customer Churn Prediction in the Telecommunication Industry. 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), Taichung, Taiwan, 2024, pp 717–718. doi:10.1109/icce-taiwan62264.2024.10674508
- Atay, M. T., & Turanlı, M. (2024). Analysis of customer churn prediction using logistic regression, -nearest neighbors, decision tree and random forest algorithms. Advances and Applications in Statistics, 92(2), pp 147–169. doi:/10.17654/0972361725008
- Awodele, O. et al., (2020). Enhanced Churn Prediction in the Telecommunication Industry. International Journal of Innovative Research in Computer Science & Technology (IJIRCST), 8(2).
- Basha, H. S., Mittal, M., Chaithra, M. H., Kala, K., Patil, H., & Maranan, R. (2024). Predicting Customer Churn Rates in Service Providing Organizations Using Riemann Residual Neural Network with Snow Geese Algorithm, 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India. pp 1463–1469, doi: 10.1109/icoici62503.2024.10696036
- Bhattacharjee, S., Thukral, U., & Patil, N. (2023). Early Churn Prediction from Large Scale User-Product Interaction Time Series. arXiv.Org, abs/2309.14390. doi: 10.48550/arxiv.2309.14390
- Burez, J. and Van den Poel, D. (2009). Handling class imbalance in customer churn prediction, Expert Systems with Applications, 36(3), pp. 4626–4636.
- Demirbaga, Ü., Aujla, G.S., Jindal, A., Kalyon, O. (2024). Machine Learning for Big Data Analytics. In: Big Data Analytics. Springer, Cham. doi: 10.1007/978-3-031-55639-5_9
- Denayer, J. (2024). Customer Churn Analysis in Telecom Industry using Kaplan–Meier and Cox Proportional Hazards Model. Indian Scientific Journal Of Research In Engineering And Management, 08(06), pp 1–5. doi:/10.55041/ijsrem36121
- De Rainville, F.M. et al. (2012). DEAP: A Python framework for Evolutionary Algorithms. GECCO’12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion, pp. 85–92.
- Frazier, P. I. (2018). A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811, Available at: https://arxiv.org/abs/1807.02811v1 (Accessed: 4 January 2024).
- Gour, S. et al. (2024). Optimizing Hyperparameters in Neural Networks Using Genetic Algorithms. Recent Advances in Computational Intelligence and Cyber Security, pp. 106–116. Available at: https://doi.org/10.1201/9781003518587-9.
- Haddadi, S., Farshidvard, A., Silva, F., JReis, J. & Reis, M., (2024). Customer churn prediction in imbalanced datasets with resampling methods: A comparative study. Expert Systems with Applications, Volume 246.
- Hambali, M. A., & Andrew, I. (2024). Bank Customer Churn Prediction Using SMOTE: A Comparative Analysis. Qeios. https://doi.org/10.32388/h82xtw
- Harshini, A., Ramya, N., Teja, B. S., Sandeep, C. S. S., & Shareefunnisa, S. (2024). Improving customer churn prediction accuracy: a SMOTE-based approach. 2024 8th International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2024, pp 215–222. https://doi.org/10.1109/icisc62624.2024.00044
- Imani, M., Ghaderpour, Z., Joudaki, M., & Beikmohammadi, A. (2024). The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction. 2024 10th International Conference on Web Research (ICWR), Tehran, Iran, Islamic Republic of, 2024, pp 202–209. https://doi.org/10.1109/icwr61162.2024.10533320
- Kun, L., Alli, H. & Rahman, K. (2024). GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators. Social Sciences & Humanities Open, Volume 10.
- Lalwani, P., Mishra, M. & Chadha, J. (2021). Customer churn prediction system: a machine learning approach. Computing, 271–294.
- Ljubičić, К., Merćep, А. & Kostanjčar, Z.. (2023). Churn prediction methods based on mutual customer interdependence. Journal of Computational Science, 67(101940).
- Loukili, М., Messaoudi, F. & El Ghazi, M. (2022). Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization. International Journal of Advances in Soft Computing and its Applications, 14(3), 49-63.
- Matthews-El, T. & K., B., 2024. 14 Customer Retention Strategies That Work. [Online] Available at: (https://www.forbes.com/advisor/business/customer-retention-strategies/ [Accessed 23 02 2025].
- Poudel, S., Pokharel, S. & Timilsina, M.. (2024). Explaining customer churn prediction in telecom industry using tabular machine learning models. Machine Learning with Applications, 17(100567).
- Prabadevi, B., Shalini, B. & Kavitha, B. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, Volume 4, 145-154.
- Pratheebha, T., Santhana Megala, S., & Indumathi, V. (2024). An Improved Churn Prediction Model using Relevance Feature Identification and Personalized Modeling Technique with Optimized Retention Strategies. 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2024, pp. 1489-1496, doi: 10.1109/ICECA63461.2024.10800772.
- Ren, H. (2025). Machine Learning-Based Prediction of Customer Churn Risk in E-commerce. Advances in Economics, Management and Political Sciences, 153(1), 47–52. https://doi.org/10.54254/2754-1169/2024.19473
- Sánchez-Gutiérrez, M. E., & González-Pérez, P. P. (2023). Addressing the class imbalance in tabular datasets from a generative adversarial network approach in supervised machine learning. https://doi.org/10.1177/17483026231215186
- Sarkar, T. D., Rahman, Md. A., & Karthikeyan, S. (2024). Analysis of Customer Churn for Telecom Company with SMOTE-ENN and Hyperparameter Tuning Randomized-SearchCV Technique in Advanced Machine Learning Technology. 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI), KANNUR, India, pp 1–6. doi:10.1109/apci61480.2024.10617375
- Sathishkumar, V., Anilkumar, Ch., & Moorthy, U. (2023). Big Data Analytics and Implementation Challenges of Machine Learning in Big data. Applied and Computational Engineering, 2(1), 533–538. doi: 10.54254/2755-2721/2/20220584
- Shumaly, S., Neysaryan, P. & Guo, Y., (2020). Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees. Mashhad, IEEE, 082-087
- Sharma, R. (2024). Customer Churn Analysis in Telecom Industry using Logistics Regression in Machine Learning with Kaplan–Meier and Cox Proportional Hazards Model. Indian Scientific Journal of Research In Engineering And Management. doi: 10.55041/ijsrem30745
- Sun, Y., Liu, H. & Gao, Y. (2023). Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon, 9(2).
- Thamizhselvi, D., Punitha, A., Kumar, R. S., Godson, V. R., Godson, D. R and Bharath, S., Churn Analysis For Restaurant, 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, doi: 10.1109/ICPECTS62210.2024.10780408.
- Wagh, S., Andhale, A., Wagh, K.,Pansare, J., Ambadekar, S.& Gawande, S. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, Volume 14.
- Wang, Ch., Rao, C., Hu, F., Xiao, X.& Goh, M. (2024). Risk assessment of customer churn in telco using FCLCNN-LSTM model. Expert Systems with Applications, 248(123352).
- Yokoyama, A.M., Ferro, M. and Schulze, B. (2023). Multi-objective hyperparameter optimization approach with genetic algorithms towards efficient and environmentally friendly machine learning. AI Communications, 37(3), pp. 429–442.