Loan Eligibility Prediction Using Machine Learning
Kaivalya Gogula1, Nagaraju Chattu2
1Kaivalya Gogula, Masters of Science in Computer/Information Technology Administration and Management, St. Francis College, Brooklyn.
2Nagaraju Chattu, Masters of science in Business Analytics and Information Systems, University of South Florida, Tampa.
Manuscript received on 24 July 2024 | Revised Manuscript received on 20 August 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 12-15 | Retrieval Number: 100.1/ijsce.C814413030924 | DOI: 10.35940/ijsce.C8144.14040924
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Technology has made many improvements, and the banking industry is no exception. Submission of loan applications by people are so many everyday, making it more difficult for bank to approve loan. To choose an applicant for loan approval, Banks must consider other bank policies also. Based on a few factors, the bank must choose the proposal that has the best probability of getting granted. It would be time-consuming and unsafe to individually check each applicant before recommending them for loan approval. Based on the prior performance of the person to whom the loan amount was previously accredited, we utilize a machine learning technique in this study to forecast the person who is trustworthy for a loan. This will check the whether the applicant is eligible for the loan or not based upon the any previous loan or running loans whether the applicant is paying back the loan within the deadline or not and it will check many other factors to shortlist the applicant is genuinely eligible for loan or not
Keywords: Machine Learning, Loan Approval, Random Forest, Dataset.
Scope of the Article: