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@article{167695,
        author = {Ha Quang Son},
        title = {Analyse effectiveness in predicting credit card default using Artificial Neural Networks (ANNs) & other Machine Learning algorithms in Microsoft Azure Studio},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {191-196},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167695},
        abstract = {This study aims to analyse the effectiveness of various machine learning algorithms, including Artificial Neural Networks (ANNs), in predicting credit card defaults using Microsoft Azure Machine Learning Studio. This research will compare the performance of different models, such as decision forest, support vector machine, boosted decision trees, logistic regression, in accurately forecasting if a credit card holder will default on the payment. Amongst these algorithms, ANNs is found to be of the highest accuracy. By leveraging on the open-sourced & large dataset of a popular consumer bank in Taiwan offering credit card facilities, the conclusion of this study will contribute to the research of predictive accuracy of different classification machine learning models in Microsoft Azure Studio. The interest of this study comes from the rising credit card delinquencies in the recent years, posing risks & increasing costs for the banks in managing consumer credit lines.},
        keywords = {Credit Card, Default, Neural Network, Machine Learning, Microsoft Azure},
        month = {September},
        }