Predicting Customer Churn Using ML Model

• Author(s): Aneesh Ahamed M; Dr. V Kanimozhi
• Paper ID: 1717691
• Page: 2017-2020
• Published Date: 18-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

This study focuses on customer churn prediction using machine learning techniques to help organizations identify customers who are likely to discontinue services. In today’s competitive business environment, retaining customers is more cost-effective than acquiring new ones, making churn management a strategic priority. The research utilizes customer demographic, behavioral, transactional, and interaction data to analyze patterns influencing churn. Various machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting, are applied and compared to determine the most effective approach. The study follows a structured methodology involving data preprocessing, feature engineering, model training, and evaluation using metrics such as accuracy, precision, recall, and ROC-AUC. The findings aim to provide actionable insights for businesses to develop targeted retention strategies, improve customer satisfaction, and reduce financial losses. Overall, the research contributes to data-driven decision-making and enhances customer relationship management through predictive analytics.

Keywords

Customer Churn, Machine Learning, Customer Retention, Customer Behavior, Data Analysis, Logistic Regression, Churn prediction model

Citations

IRE Journals:
Aneesh Ahamed M, Dr. V Kanimozhi "Predicting Customer Churn Using ML Model" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2017-2020 https://doi.org/10.64388/IREV9I11-1717691

IEEE:
Aneesh Ahamed M, Dr. V Kanimozhi "Predicting Customer Churn Using ML Model" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717691