Open Access
Issue |
SHS Web Conf.
Volume 181, 2024
2023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|
|
---|---|---|
Article Number | 02003 | |
Number of page(s) | 10 | |
Section | Financial Analysis and Stock Market Strategies | |
DOI | https://doi.org/10.1051/shsconf/202418102003 | |
Published online | 17 January 2024 |
- Wooldridge: 115 Data Sets from “IntroductoryEconometrics: A Modern Approach, 7e” by Jeffrey M. Wooldridge version 1.4-3 from CRAN (rdrr.io) [Google Scholar]
- K. Arun, G. Ishan, K. Sanmeet, Loan approval prediction based on machine learning approach, IOSR J. Comput. Eng. 18, 18–21 (2016). [Google Scholar]
- J. Tejaswini, T.M. Kavya, R.D.N. Ramya, P.S. Triveni, V.R. Maddumala, Accurate loan approval prediction based on machine learning approach, J. Eng. Sci. 11, 523–532 (2020). [Google Scholar]
- M.A. Sheikh, A.K. Goel, T. Kumar, An approach for prediction of loan approval using machine learning algorithm, in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, 490–494 (2020). [CrossRef] [Google Scholar]
- A. Vaidya, Predictive and probabilistic approach using logistic regression: Application to prediction of loan approval, in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 1–6 (2017). [Google Scholar]
- A.S. Kadam, S.R. Nikam, A.A. Aher, G.V. Shelke, A.S. Chandgude, Prediction for loan approval using machine learning algorithm, Int. Res. J. Eng. Technol. 8 (2021). [Google Scholar]
- P. Tumuluru, L.R. Burra, M. Loukya, S. Bhavana, H.M.H. Csaibaba, N. Sunanda, Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms, in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), IEEE, 349–353 (2022). [CrossRef] [Google Scholar]
- U. Aslam, H.I. Tariq Aziz, A. Sohail, N.K. Batcha, An empirical study on loan default prediction models, J. Comput. Theor. Nanosci. 16, 3483–3488 (2019). [CrossRef] [Google Scholar]
- T. Ndayisenga, Bank loan approval prediction using machine learning techniques, Doctoral dissertation (2021). [Google Scholar]
- P.S. Murthy, G.S. Shekar, P. Rohith, G.V.V. Reddy, Loan Approval Prediction System Using Machine Learning, J. Innov. Inf. Technol. 21–24 (2020). [Google Scholar]
- P.S. Murthy, G.S. Shekar, P. Rohith, G.V.V. Reddy, Loan Approval Prediction System Using Machine Learning, J. Innov. Inf. Technol. 21–24 (2020). [Google Scholar]
- Y. Diwate, P. Rana, P. Chavan, Loan Approval Prediction Using Machine Learning, Int. Res. J. Eng. Technol. 8, 1741–1745 (2021). [Google Scholar]
- D.W. Hosmer Jr, S. Lemeshow, R.X. Sturdivant, Applied logistic regression, John Wiley & Sons (2013). [CrossRef] [Google Scholar]
- A. Palczewska, J. Palczewski, R.M. Robinson, D. Neagu, Interpreting random forest models using a feature contribution method, in 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), IEEE, 112–119 (2013). [CrossRef] [Google Scholar]
- A. Sánchez, V.D. Advanced Support vector machines and kernel methods, Neurocomputing 55, 5–20 (2003). [CrossRef] [Google Scholar]
- A. Altmann, L. Toloşi, O. Sander, T. Lengauer, Permutation importance: a corrected featureimportance measure, Bioinformatics 26, 1340–1347 (2010). [CrossRef] [PubMed] [Google Scholar]
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