Analysis of Credit Card Client Attrition using Machine Learning

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. Cons...

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Bibliographic Details
Published in2024 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
LanguageEnglish
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
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DOI10.1109/ICIPCN63822.2024.00079

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Summary: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.
DOI:10.1109/ICIPCN63822.2024.00079