Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Book Chapter
Publisher : Springer, Singapore
Source : In: Kumar A., Senatore S., Gunjan V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_51
Url : https://link.springer.com/chapter/10.1007/978-981-16-3690-5_51
Campus : Kochi
School : School of Computing
Department : Computer Science
Year : 2021
Abstract : This research aims to propose an efficient model for the prediction of default of credit cards. The model is constructed using Extreme Gradient Boosting Ensemble technique on Taiwan based credit default dataset, which contains both financial and demographic attributes of credit card holders. The performance of the model is improved using Feature Selection and Hyperparameter Optimization methods. The result shows that the proposed model has more accuracy than many existing models for default prediction.
Cite this Research Publication : Thomas R., Vimina E.R. (November 2021) "Enhancing the Classification Accuracy of Credit Default Using Extreme Gradient Boosting with Recursive Feature Selection". In: Kumar A., Senatore S., Gunjan V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_51