Identifying customer churn in Telecom sector: A Machine Learning Approach

Moshood Abiola  Hambali; Emmanuel Lawrence; Yinusa Olasupo; Ishaku  Andrew

doi:10.53704/fujnas.v13i2.469

Authors

Moshood Abiola Hambali Computer Science Departments, Federal University Wukari, Taraba-Nigeria
Emmanuel Lawrence Computer Science Departments, Federal University Wukari, Taraba-Nigeria
Yinusa Olasupo Computer Science Departments, Federal University Wukari, Taraba-Nigeria
Andrew Ishaku Wreford Computer Science Departments, Federal University Wukari, Taraba-Nigeria

DOI:

https://doi.org/10.53704/fujnas.v13i2.469

Keywords:

Customer Churn, Telcomminication, Machine Learning, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF)

Abstract

Nowadays, there is no shortage of options for customers when choosing where to put their money. As a result, customer churn and engagement have become one of the top issues. With the increase in the number of service providers for the same targeted population, there is a need for service providers to try to find the changing customer behaviour and their rising expectations to retain them. Various studies have proposed customer churn. Data mining was routinely used to predict telecom customer attrition. Most researchers have compared and proposed different approaches for the prediction of customer churn, though some of the Machine learning (ML) algorithms used were unable to provide the performance needed to identify customer churn. Therefore, this paper presents a comparative analysis of Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) in the Telecommunications Dataset. To prepare the dataset for machine learning algorithms, chi-square was used for feature selection to select the most informative features from the original dataset. We validate our model using a ten-fold cross-validation approach to test the performance of our models. RF model performed better than other models in terms of accuracy (94%), precision (94%) and F-measure (94%), respectively. Additionally, we compared our results with existing models that used the same dataset; the proposed strategy outperformed them.

Keywords: Customer Churn, Telecommunication, Machine Learning, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF)

Author Biography

Moshood Abiola Hambali, Computer Science Departments, Federal University Wukari, Taraba-Nigeria

References

Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data. https://doi.org/10.1186/s40537-019-0191-6

Alboukaey, N., Joukhadar, A., & Ghneim, N. (2020). Dynamic Behavior based Churn Prediction in Mobile Telecom. Expert Systems With Applications, 113779. https://doi.org/10.1016/j.eswa.2020.113779

Domingos, E., Ojeme, B., & Daramola, O. (2021). Experimental analysis of hyperparameters for deep learning-based churn prediction in the banking sector. Computation, 9(3), 34.

Fujo, S. W., Subramanian, S., & Khder, M. A. (2022a). Customer churn prediction in telecommunication industry using deep learning. Information Sciences Letters, 11(1), 24.

Fujo, S. W., Subramanian, S., & Khder, M. A. (2022b). Deep Learning Customer Churn Prediction in Telecommunication Industry Using Deep Learning. Information Sciences Letters Volume, 11(1), 185–198.

Hambali, M. A., Oladele, T. O., Adewole, K. S., Sangaiah, A. K., & Gao, W. (2022). Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm. Multimedia Tools and Applications, 1213, 1–45. https://doi.org/10.1007/s11042-022-13532-5

Jain, H., Khunteta, A., & Srivastava, S. (2020). Churn prediction in telecommunication using logistic regression and logit boost. Procedia Computer Science, 167, 101–112.

Jain, H., Khunteta, A., Srivastava, S., Jain, H., Khunteta, A., & Srivastava, S. (2020). ScienceDirect ScienceDirect Churn Prediction in Telecommunication using Logistic Regression Churn Prediction in Telecommunication using Logistic Regression and Logit Boost and Logit Boost. Procedia Computer Science, 167(2019), 101–112. https://doi.org/10.1016/j.procs.2020.03.187

Leung, H. C., & Chung, W. (2020). A Dynamic Classification Approach to Churn Prediction in Banking Industry. Americas Conference on Information Systems, 1–5.

Mirabdolbaghi, S. M. S., & Amiri, B. (2022). Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions. Discrete Dynamics in Nature and Society, 2022.

Mittal, K., Khanduja, D., & Tewari, P. C. (2017). An insight into ‘decision tree analysis.’ World Wide Journal of Multidisciplinary Research and Development, 3(12), 111–115.

Priyanka, & Kumar, D. (2020). Decision tree classifier: a detailed survey. International Journal of Information and Decision Sciences, 12(3), 246–269.

Rahman, M., & Kumar, V. (2020). Machine learning based customer churn prediction in banking. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 1196–1201.

Saheed, Y. K., & Hambali, M. A. (2021). Customer churn prediction in telecom sector with machine learning and information gain filter feature selection algorithms. 2021 International Conference on Data Analytics for Business and Industry (ICDABI), 208–213.

Sana, J. K., Abedin, M. Z., Rahman, M. S., & Id, M. S. R. (2022). A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection. PLoS ONE, 17(12), e0278095 1-21. https://doi.org/10.1371/journal.pone.0278095

Shaaban, E., Helmy, Y., Khedr, A., & Nasr, M. (2012). A proposed churn prediction model. International Journal of Engineering Research and Applications, 2(4), 693–697.

Sharma, A., & Panigrah, P. K. (2011). A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications, 27(11), 26–31.

Sudharsan, R., & Ganesh, E. N. (2022). A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy. Connection Science, 34(1), 1855–1876. https://doi.org/10.1080/09540091.2022.2083584

Suresh, A., Udendhran, R., & Balamurgan, M. (2020). Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11), 7947–7953.

Tékouabou, S. C. K., Gherghina, ?tefan C., Toulni, H., Mata, P. N., & Martins, J. M. (2022). Towards explainable machine learning for bank churn prediction using data balancing and ensemble-based methods. Mathematics, 10(14), 2379.

Yang, F.-J. (2019). An extended idea about decision trees. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 349–354.

Zhang, T., Moro, S., & Ramos, R. F. (2022). A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation. Future Internet, 14(94), 1–19.