Customer Churn Prediction Using Machine Learning

• Author(s): Ashish Pandey; Anurag Pal; Dr. Ishrat Ali; Prof. (Dr.) Sanjay Pachauri
• Paper ID: 1712440
• Page: 2284-2286
• Published Date: 01-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Customer churn has become one of the most challenging issues for organizations operating in competitive markets. The ability to identify customers who are likely to discontinue services helps businesses take timely actions and reduce revenue loss. This study develops a machine-learning-based approach for predicting churn by analyzing customer behavior and service patterns. Multiple classification models, including Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting, are examined to determine their effectiveness. The experimental results indicate that ensemble methods deliver the highest accuracy and offer better insights for designing customer-retention strategies.

Keywords

Customer churn, Predictive analytics, Machine learning, Random Forest, Customer retention, Data mining.

Citations

IRE Journals:
Ashish Pandey, Anurag Pal, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri "Customer Churn Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2284-2286 https://doi.org/10.64388/IREV9I5-1712440

IEEE:
Ashish Pandey, Anurag Pal, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri "Customer Churn Prediction Using Machine Learning" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712440