Copyright © 2023 The Author(s): This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY-NC 4.0) which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited. International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 Available Online at : www.ijsrcseit.com doi : https://doi.org/10.32628/CSEIT23903141 15 E-Business Churn Prediction Model Using Machine Learning Ayyapureddi Siva Sai Rupesh 1 , Advin Manhar 2 1 Student, Department of Computer Science and Engineering, Amity University Chhattisgarh, Raipur, Chhattisgarh, India 2 Assistant Professor, Department of Computer Science and Engineering, Amity University Chhattisgarh, Raipur, Chhattisgarh, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 20 June 2023 Published: 05 July 2023 Businesses need to keep their clients in the present competitive environment in order to remain in the market. To achieve this, they must anticipate customer attrition and take proactive steps to keep clients. In this research, we offer a model for predicting customer churn based on machine learning that can forecast the probability of consumers leaving with accuracy. To anticipate customer turnover, we employ a variety of machine learning techniques, including logistic regression, random forest, and support vector machines. To assess the effectiveness of our methodology, we additionally employ a number of assessment measures. Our findings show that the suggested model works better than the current models and can aid companies in keeping consumers. Keywords : Machine learning, Logistic Regression, Random Forest, and Customer Churn Customer retention, classification, e-business churn forecast, accuracy, precision, recall, F1-score, Log loss, ROC AUC, calibration loss, cost matrix gain Publication Issue Volume 9, Issue 4 July-August-2023 Page Number 15-23 I. INTRODUCTION In today's competitive business landscape, retaining customers and minimizing customer churn has become a top priority for organizations across various industries. Customer churn is the phenomenon when customers cease utilising a company's products or services or stop doing business with it. A business's income and general growth might suffer significantly when key clients are lost. Because of this, it is now vital for firms to proactively identify clients who are at danger of leaving and take the necessary steps to keep them. Machine learning has become a potent tool for anticipating customer attrition because to its capacity to examine vast amounts of data and unearth patterns and insights. Machine learning models can find trends and signs that are suggestive of possible churn by using past customer data and advanced algorithms. These models can then be used to generate actionable insights and develop targeted retention strategies to mitigate customer churn.