INTEGRATING RANDOM FOREST AND LOGISTIC REGRESSION TO FORECAST CUSTOMER ATTRITION IN THE TELECOM SECTOR

Authors

  • Ayoade A. Owoade
    Tai Solarin University of Education, Ijebu Ode
  • Olugbenga I. Bakare
    Tai Solarin University of Education, Ijebu Ode
  • Gbenga O. Ogunsanwo
    Tai Solarin University of Education, Ijebu Ode

Keywords:

Random Forest, Machine Learning, Logistic Regression, Customer Attrition

Abstract

Since customers are the foundation of any successful business, companies must prioritize making sure they are satisfied. However, due to increased corporate competition, the importance of customers’ and marketing tactics more informed conduct in the past few years, client attrition is a significant problem and is acknowledged as one of the top concerns among businesses. Organizations must take a number of measures to address the problems with churn brought on by the services they offer. Customer attrition strategies are essential in the fiercely competitive and rapidly changing telecom industry. Utilizing machine learning methods, assess the possibility that a client will leave a firm. This research uses logistic regression, random forest, and big data to predict customer attrition in the telecom sector. A large-scale logistic regression analysis has been used to assess the probability of churn as a function of a variable set or customer attribute. Similarly, based on how close a feature is to customers in each class, random forest is employed to ascertain if or not a customer churns. This research makes use of information from the Kaggle website to forecast and examine churn. According to the results of the study show that 0.84 percent is the area under the curve., and the forecast precision rates for consumer churn using linear regression and random forest are 0.80 and 0.79 percent, respectively.

Dimensions

Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer churning analysis using machine learning algorithms. International Journal of Intelligent Networks, 4.

Zhao, H., Yao, X., Liu, Z., & Yang, Q. (2021). Impact of pricing and product information on consumer buying behavior with customer satisfaction in a mediating role. Frontiers in Psychology, 12(1). frontiersin.

Srinivasan, R., Rajeswari, D., & Elangovan, G. (2023, January 1). Customer Churn Prediction Using Machine Learning Approaches. IEEE Xplore.

Karamollaolu, H., Yceda, ., & Doru, . A. (2021, September 1). Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis. IEEE Xplore.

Shields, K. (2021). Chapter 3: Managing a Customer Service Team. Ecampusontario.pressbooks.pub, 3(5).

Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2023). Customer Churn Prediction in Telecom Sector using Machine Learning Techniques. Results in Control and Optimization, 14, 100342.

Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1).

Sana, J. K., Abedin, M. Z., Rahman, M. S., & Rahman, M. S. (2022). A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection. PLOS ONE, 17(12)

Ribeiro, H., Barbosa, B., Moreira, A. C., & Rodrigues, R. G. (2023). Determinants of churn in telecommunication services: a systematic literature review. Management Review Quarterly.

Petropoulos, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3). sciencedirect.

Mandal, P. C. (2023). Engaging Customers and Managing Customer Relationships. Journal of Business Ecosystems, 4(1), 114.

Shobana, J., Gangadhar, Ch., Arora, R. K., Renjith, P. N., Bamini, J., & Chincholkar, Y. devidas. (2023). E-commerce customer churn prevention using machine learning-based business intelligence strategy. Measurement: Sensors, 27

Aghaabbasi, M., & Chalermpong, S. (2023). Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review. Travel Behaviour and Society, 33.

Saghir, M., Bibi, Z., Bashir, S., & Khan, F. H. (2019). Churn Prediction using Neural Network based Individual and Ensemble Models. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 121. Springer. https://doi.org/10.1007/s42979-021-00592-x

Lu, Y. L., Zhelavskaya, I. S., & Wang, C. (2022). Neural Decision Tree: A New Tool for Building Forecast Models for Plasmasphere Dynamics. Earth and Space Science, 9(7).

Zhao, M., Zeng, Q., Chang, M., Tong, Q., & Su, J. (2021). A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China. Discrete Dynamics in Nature and Society, 2021, 112. hindawi. https://doi.org/10.1155/2021/7160527

Nguyen, N. Y., Tran, L. V., & Dao, S. V. T. (2022). Churn prediction in telecommunication industry using kernel Support Vector Machines. PLOS ONE, 17(5).

Wibawa, A. P., Kurniawan, A. C., Murti, D. M. P., Adiperkasa, R. P., Putra, S. M., Kurniawan, S. A., & Nugraha, Y. R. (2019). Nave Bayes Classifier for Journal Quartile Classification. International Journal of Recent Contributions from Engineering, Science & IT (IJES), 7(2), 91.

Dar, S. A. (2022). The Relevance of Taylors Scientific Management in the Modern Era. Journal of Psychology and Political Science (JPPS) ISSN 2799-1024, 2(06), 16.

Michele, P., Fallucchi, F., & De Luca, E. W. (2019). Create Dashboards and Data Story with the Data & Analytics Frameworks. Metadata and Semantic Research, 272283.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 125.

Ku-Mahamud, K. R. & Basim Alwan, H. (2020). Big data: definition, characteristics, life cycle, applications, and challenges. IOP Conference Series: Materials Science and Engineering, 769, 012007. https://doi.org/10.1088/1757-899x/769/1/012007

Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers understanding of the phenomenon of the decade. PLOS ONE, 15(2),

Islam, Md. A. (2024). Impact of Big Data Analytics on Digital Marketing: Academic Review. Journal of Electrical Systems, 20(5s), 786820. https://doi.org/10.52783/jes.2327

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 125. springer.

Batko, K., & lzak, A. (2022). The Use of Big Data Analytics in Healthcare. Journal of Big Data, 9(1). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733917/

Roberts, S. (2023). What are the 3Vs of Big Data? Www.theknowledgeacademy.com. https://www.theknowledgeacademy.com/blog/big-data-3v/

Jabeen, H. (2020). Chapter 3 Big Data Outlook, Tools, and Architectures. Lecture Notes in Computer Science, 3555. https://doi.org/10.1007/978-3-030-53199-7_3

Berisha, B., Meziu, E., & Shabani, I. (2022). Big data analytics in Cloud computing: an overview. Journal of Cloud Computing, 11(1).

Cavlak, N., & Cop, R. (2021). The Role of Big Data in Digital Marketing. Advances in Marketing, Customer Relationship Management, and E-Services, 1633. https://doi.org/10.4018/978-1-7998-8003-5.ch002

Udeh, C. A., Orieno, O. H., Daraojimba, O. D., Ndubuisi, N. L., & Oriekhoe, O. I. (2024). Big Data Analytics: A Review of Its Transformative Role in Modern Business Intelligence. Computer Science & IT Research Journal, 5(1), 219236.

Probst, P., Wright, M. N., & Boulesteix, A. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3).

Qin, Z., Liu, Y., & Zhang, T. (2022). Research on Early Warning of Customer Churn Based on Random Forest. Journal on Artificial Intelligence, 4(3), 143154.

Sultan, A., Saabun, W., Faizi, S., & Ismail, M. (2021). Hesitant Fuzzy Linear Regression Model for Decision Making. Symmetry, 13(10), 1846.

Jain, H., Khunteta, A., & Srivastava, S. (2020). Churn Prediction in Telecommunication using Logistic Regression and Logit Boost. Procedia Computer Science, 167, 101112.

Mooney, M. A., Neighbor, C., Karalunas, S., Dieckmann, N. F., Nikolas, M., Nousen, E., Tipsord, J., Song, X., & Nigg, J. T. (2022). Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation. Clinical Psychological Science, 216770262211202. https://doi.org/10.1177/21677026221120236

Martnez, A., Schmuck, C., Pereverzyev, S., Pirker, C., & Haltmeier, M. (2020). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588596.

Sleiman, R., Mazyad, A., Hamad, M., Tran, K.-P., & Thomassey, S. (2022). Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic. International Journal of Computational Intelligence Systems, 15(1), 99.

Idriss, I. A., Cheng, W., & Hailu, Y. (2023). Weighted Maximum Likelihood Technique for Logistic Regression. Open Journal of Statistics, 13(06), 803821.

Published

30-06-2025

How to Cite

INTEGRATING RANDOM FOREST AND LOGISTIC REGRESSION TO FORECAST CUSTOMER ATTRITION IN THE TELECOM SECTOR. (2025). FUDMA JOURNAL OF SCIENCES, 9(6), 80-89. https://doi.org/10.33003/fjs-2025-0906-3556

Issue

Vol. 9 No. 6 (2025): FUDMA Journal of Sciences - Vol. 9 No. 6

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

INTEGRATING RANDOM FOREST AND LOGISTIC REGRESSION TO FORECAST CUSTOMER ATTRITION IN THE TELECOM SECTOR. (2025). FUDMA JOURNAL OF SCIENCES, 9(6), 80-89. https://doi.org/10.33003/fjs-2025-0906-3556

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