Vol. 5 No. 1 (2025): Issue 5
Articles

Efficient Customer Churn Prediction through Bayesian Support Vector Regression

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  • Jinwoo Park,
  • Nari Kim,
  • Seojin Lee
DOI: https://doi.org/10.71070/oaml.v5i1.45

Published 2025-01-29

Keywords

  • Customer Churn,
  • Bayesian Support Vector Regression,
  • Prediction Accuracy,
  • Computational Efficiency,
  • Customer Relationship Management

How to Cite

Park, J., Kim, N., & Lee, S. (2025). Efficient Customer Churn Prediction through Bayesian Support Vector Regression. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.45
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Efficient Customer Churn Prediction through Bayesian Support Vector Regression is a crucial area of research due to its significance in helping businesses retain customers and enhance profitability. Presently, existing research faces challenges in accurately predicting customer churn and optimizing computational efficiency. This paper introduces a novel approach by proposing Bayesian Support Vector Regression for efficient customer churn prediction. The innovative aspect lies in the integration of Bayesian inference with Support Vector Regression to enhance prediction accuracy while reducing the computational burden. The study showcases the effectiveness of this method through comprehensive experiments and analysis, ultimately demonstrating its potential for improving customer relationship management strategies in various industries.

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References

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