A Novel Stochastic Gradient Descent Based Logistic Regression (SGD-LR) Framework for Customer Churn Prediction

Authors

  • Suja Sundram Assistant Professor, Department of Business Administration, Jubail Industrial College, Kingdom of Saudi Arabia
  • D. Poornima Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India. https://orcid.org/0000-0002-7380-8882
  • Praveenkumar G. D. Assistant Professor, Department of Computer Technology -UG Kongu Engineering College- Erode, Tamilnadu, India
  • C. Balakumar Assistant Professor, Department of Computer Applications, Faculty of Arts, Science, Commerce and Management, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India.
  • D. Sasikala Assistant Professor, Department of Computer Science, Sri Vasavi College, Erode, Tamilnadu, India
  • Sardor Omonov he Department of Corporate Finance and Securities, Tashkent Institute of Finance, Tashkent, Uzbekistan

Keywords:

Customer Churn, Prediction, Logistic Regression, Stochastic Gradient Descent, Deep Learning

Abstract

Customer churn is a crucial issue in any company or organization, and it describes the loss of customers as a result of them switching to competitors. When there is an opportunity to discover customer churn sooner, the organization may take steps such as providing important knowledge for keeping and boosting the client count. Deep Learning (DL) models have recently gained popularity because to their remarkable performance boost in a variety of fields. In this paper, a DL-based Customer Churn Prediction (CCP) is introduced using Stochastic Gradient Descent Based Logistic Regression (SGD-LR) with an LR classifier model. Effective categorization may be achieved by combining SGD with LR. The provided SGD-LR model is evaluated against a benchmark dataset, with the outcomes examined over a range of epochs. Furthermore, a comparison study with the outcomes of existing approaches is conducted. The implementation results demonstrated that the provided SGD-LR model outperformed the current CCP models on the same dataset.

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Published

23.02.2024

How to Cite

Sundram, S. ., Poornima, D. ., G. D., P. ., Balakumar, C. ., Sasikala, D. ., & Omonov, S. . (2024). A Novel Stochastic Gradient Descent Based Logistic Regression (SGD-LR) Framework for Customer Churn Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 754–765. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5019

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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