Analysis of Credit Card Client Attrition using Machine Learning

• Published in: 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN) pp. 446 - 450
• Main Authors: Nandini, D., Kumar, M.Dilip, Jamal, K., Kumar, N.Bharath, Mannem, Kiran, Suneetha, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.07.2024
• Subjects: Correlation; Credit cards; Data models; Decision Tree(DT); Image processing; KNN; Logistic Regression(LR); Machine learning; Nearest neighbor methods; Predictive models; XGBoost
• DOI: 10.1109/ICIPCN63822.2024.00079

Churn Analysis on credit cards is the study of the phenomenon of consumers quitting a particular business credit card service. Credit card firms need to be able to predict customer churn in order to identify consumers who want to leave and ensure preventive measures for retaining the customers. Constructing a model capable of accurately identifying the clients who are most likely to stop using Credit Card is the main objective of this project. In order to make predictions, the study collects and analyzes user data from Kaggle.Machine learning techniques like K-Nearest Neighbour, XGBoost,Logistic Regression, DT and Hybrid Models integrating LR and KNN, LR and DT are trained to find patterns and correlations that point to client attrition.