Citation

Quek, Vivian Jia Yi and Pang, Ying Han and Lim, Zheng You and Ooi, Shih Yin and Khoh, Wee How (2024) Predicting churn with filter-based techniques and deep learning. International Journal of Electrical and Computer Engineering (IJECE), 14 (2). pp. 2135-2144. ISSN 2088-8708

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Abstract

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network; Attribute selection analysis; Churn prediction; Deep learning; Filter methods
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Apr 2024 01:39
Last Modified:	03 Apr 2024 01:39
URII:	http://shdl.mmu.edu.my/id/eprint/12296

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