editorinchief@ijrtmr.com +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal e-ISSN 2583-0368 Since 2021
  1. Home
  2. Archives
  3. 2026
  4. Vol. 6 Issue 2
  5. Article
Original Article

Churn Guard AI: Production Customer Intelligence System for Real-Time Churn Prediction Using Explainable Machine Learning

  • Paper ID 333
  • Volume 6, Issue 2
  • Pages 401-405
  • Published Online: March-April 2026
  • 13 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • S.Ramya1
  • Kavipriya.M2
  • Abinaya.D3
  • Jayanthi.R4

1 Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering Hosur,nTamil Nadu, India. 2 3 4 Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India.

Article Information

Published Online
March-April 2026
Pages
401-405
Article Type
Original Article
Check for updates
check update

References

S. Coussement, K. W. De Bock, and N. M. G. Van den Poel, ”Improving customer churn prediction utilizing actionable interpretable
machine learning,” IEEE Transactions on Engineering Management, vol. 68, no. 4, pp. 1100-1115, 2021.
2. A. Ahmed, H. Sabir, and T. M. K. Ali, ”Baseline methodologies in demographic customer churn modeling,” IEEE Access, vol. 8, pp.
24510-24519, 2020.
3. M. Q. Z. Ali and A. Khan, ”Deployment challenges and API architec-tures for robust machine learning services,” ACM Transactions
on Web Systems, vol. 12, no. 3, pp. 45-61, 2022.
4. S. M. Lundberg and S. I. Lee, ”A unified approach to interpreting model predictions,” Advances in Neural Information Processing
Systems, vol. 30, 2017.
5. T. R. K. Kumar and M. J. Lee, ”Benchmarking linear and non-linear predictive algorithms for telecommunications churn,” Springer
Journal of Data Science, vol. 14, pp. 104-123, 2019.
6. H. Chen, Y. Wang, and Z. Liu, ”Random Forest optimizations applied to high-dimensional customer activity logs,” Elsevier Expert
Systems with Applications, vol. 143, p. 113060, 20207. J. Zhang, C. Q. Wu, and D. Chen, ”XGBoost and variance reduction in predictive commercial domains,” IEEE Network, vol. 35,
no. 1, pp. 162-168, 2021.
8. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Liu, ”LightGBM: A highly efficient gradient boosting decision
tree,” Advances in Neural Information Processing Systems, vol. 30, pp. 3146-3154, 2017.
9. L. Kumar, S. R. Naidu, and P. Sharma, ”Analyzing sequential user logs utilizing recurrent neural networks for churn detection,”
IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 210-221, 2022.
10. D. R. E. A. Brown and S. J. Smith, ”Bridging the gap: From predictive machine learning constructs to actionable operational
microservices,” IEEE Software, vol. 40, no. 5, pp. 22-30, 2023.
11. A. M. Garcia,”Expla
APC from ₹1200 + 18% GST

Peer-reviewed open access₹1200 Indian (₹1400 with DOI)$80 USD internationalpaid after acceptance Peer-reviewed open access₹1200 Indian$80 USD international ₹1200 Indian · $80 intl

  • CrossRef DOI
  • ISSN 2583-0368
  • Open Access
  • Peer Reviewed
  • Plagiarism Checked
  • Google Scholar
Submit Paper