No. 12. Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner Manuscript Received: 22 June 2023, Accepted: 1 August 2023, Published: 15 September 2023

Main Article Content

Yan Lin Tan
Ying Han Pang
Shih Yin Ooi
Wee How Khoh
Fu San Hiew


In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance.  In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4 % f1-score and 60.62 % recall.

Article Details



“How Many People Have Smartphones Worldwide (Jun 2023),” (accessed Jun. 20, 2023).

K. W. De Bock and D. Van Den Poel, “An Empirical Evaluation of Rotation-based Ensemble Classifiers for Customer Churn Prediction,” Expert Sys. Appl., vol. 38, no. 10, pp. 12293–12301, 2011.

S. Wu, W. C. Yau, T. S. Ong and S. C. Chong, “Integrated Churn Prediction and Customer Segmentation Framework for Telco Business,” IEEE Access, vol. 9, pp. 62118–62136, 2021.

N. Y. Nhu, T. Van Ly and D. V. Truong Son, “Churn Prediction in Telecommunication Industry Using Kernel Support Vector Machines,” PLoS One, vol. 17, no. 5, pp. e0267935, 2022.

A. Sharma, P. Shukla, M. K. Gourisaria, B. Sharma and I. Ben Dhaou, “Telecom Churn Analysis Using Machine Learning in Smart Cities,” in 1st Int. Conf. Advanced Innov. Smart Cities, pp. 1-5, 2023.

N. N. Nguyen and A. T. Duong, “Comparison of Two Main Approaches for Handling Imbalanced Data in Churn Prediction Problem,” J. Advances Inform. Technol., vol. 12, no. 1, pp. 29–35, 2021.

P. Lalwani, M. K. Mishra, J. S. Chadha and P. Sethi, “Customer Churn Prediction System: A Machine Learning Approach,” Computing, vol. 104, no. 2, pp. 271–294, 2022.

J. J. R. Angelina, S. J. Subhashini, S. H. Baba, P. D. K. Reddy, P. V. S. K. Reddy, and K. S. Khan, “A Machine Learning Model for Customer Churn Prediction Using CatBoost t/Classifier,” in 2023 7th Int. Conf. Intellig. Comput. and Contr. Sys., pp. 166–172, 2023.

S. F. Bilal, A. A. Almazroi, S. Bashir, F. H. Khan and A. A. Almazroi, “An Ensemble Based Approach Using A Combination of Clustering and Classification Algorithms to Enhance Customer Churn Prediction in Telecom Industry,” PeerJ Comput. Sci., vol. 8, pp. e854, 2022.

“Why Use Ensemble Learning?,” (accessed Jun. 19, 2023).

X. Wang, K. Nguyen and B. P. Nguyen, “Churn Prediction Using Ensemble Learning,” in Proc. 4th Int. Conf. Machine Learning and Soft Comput., pp. 56–60, 2020.

A. Hammoudeh, M. Fraihat and M. Almomani, “Selective Ensemble Model for Telecom Churn Prediction,” in 2019 IEEE Jordan Int. Joint Conf. Electr. Eng. and Inform. Technol., pp. 485–487, 2019.

A. Idris, M. Rizwan and A. Khan, “Churn Prediction in Telecom Using Random Forest and PSO Based Data Balancing in Combination with Various Feature Selection Strategies,” Comp. & Electr. Eng., vol. 38, no. 6, pp. 1808–1819, 2012.

U. Ahmed, A. Khan, S. H. Khan, A. Basit, I. U. Haq and Y. S. Lee, “Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry,” Jan. 2019, Accessed: May 28, 2023. [Online]. Available:

T. Albrecht and D. Baier, “Churn Analysis Using Deep Learning: Customer Classification from a Practical Point of View,” Data Science-Series A, vol. 6, no. 2, pp. P04, 2020.

S. W. Fujo, S. Subramanian and M. A. Khder, “Customer Churn Prediction in Telecommunication Industry Using Deep Learning,” Inform. Sci. Lett., vol. 11, no. 1, pp. 185-198, 2022.

“telecom churn (cell2cell) | Kaggle,” (accessed May 29, 2023).

N. I. Mohammad, S. A. Ismail, M. N. Kama, O. M. Yusop and A. Azmi, “Customer Churn Prediction in Telecommunication Industry Using Machine Learning Classifiers,” in Proc. 3rd Int. Conf. Vision, Image and Signal Processing, no. 34, pp. 1–7, 2019.

D. A. Kumar and V. Ravi, ‘‘Predicting Credit Card Customer Churn in Banks Using Data Mining,’’ Int. J. Data Anal. Technol. Strategies, vol. 1, no. 1, pp. 4–28, 2008.

I. Kaur and J. Kaur, “Customer Churn Analysis and Prediction in Banking Industry Using Machine Learning,” in 2020 Sixth Int. Conf. Parallel, Distributed and Grid Comput., pp. 434-437, 2020.

Q. Yanfang and L. Chen, “Research on E-commerce User Churn Prediction Based on Logistic Regression,” in Proc. 2017 IEEE 2nd Inform. Technol., Netw., Electron. and Auto. Contr. Conf., vol. 2018, pp. 87–91, 2018.

R. S. Shankar, J. Rajanikanth, V. V. Sivaramaraju and K. V. S. S. R. Murthy, “Prediction of Employee Attrition Using Datamining,” in 2018 IEEE Int. Conf. Sys., Comput., Auto. and Netw., India, pp. 1-8, 2018.

S. B. Jabeur, C. Gharib, S. M. Wali, and W. B. Arfi, “CatBoost Model and Artificial Intelligence Techniques for Corporate Failure Prediction,” Technol. Forecast Soc. Change, vol. 166, pp. 120658, 2021.

N. Alboukaey, A. Joukhadar and N. Ghneim, “Dynamic Behavior Based Churn Prediction in Mobile Telecom,” Expert Sys. with Appl., vol. 162, pp. 113779, 2020.

Y. Deng, D. Li, L. Yang, J. Tang and J. Zhao, “Analysis and Prediction of Bank User Churn Based on Ensemble Learning Algorithm,” in Proc. 2021 IEEE Int. Conf. Power Electron., Comp. Appl., pp. 288–291, 2021.