Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Efficient Machine Learning Classification model for Credit Approval Subject The topic of the resource Bank Loan; Credit Approval; Logistic Regression.; Machine Learning; Random Forest Description An account of the resource Credit authorization is a critical step for banks as well as every bank's main source of revenue is its line of credit. Thus, banks can profit from the loan interest they approve. Profitability or lost opportunity of a bank is highly dependent on loans that are whether consumers repay the debt or refuse. Loan collection is a significant factor in a bank's economic results. Forecasting the customer's ability to repay the loan in order to determine whether it should authorize or deny loan documents is a significant undertaking and a critical method in data analytics is being utilized to investigate the problem of loan default prediction: On the premise of assessment, the Logistic-Regression Classification Model, Random-Forest Classifier and Decision Tree Classification Models are compared. The mentioned classification algorithms were created as well as subsequently various evaluation metrics were obtained. By utilizing a suitable strategy, the appropriate clients for loan providing may be simply identified by assessing their probability of non-performing loans. This indicates that a bank really shouldn't simply prioritize wealthy consumers when giving loans, but it should also consider a client's other characteristics. This approach is critical in making credit judgments and forecasting default risk. 2023 IEEE. Creator An entity primarily responsible for making the resource Dr.D.M.V. R.; Lakshmanarao A.; Gupta C. Source A related resource from which the described resource is derived Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023, pp. 499-503. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAIS56108.2023.10073706" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAIS56108.2023.10073706</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152380864&amp;doi=10.1109%2FICAIS56108.2023.10073706&amp;partnerID=40&amp;md5=d0b0f428dfb39c9ba0328a42e8f89372" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152380864&amp;doi=10.1109%2fICAIS56108.2023.10073706&amp;partnerID=40&amp;md5=d0b0f428dfb39c9ba0328a42e8f89372</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166546216-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dr.D.M.V. R., Aditya Engineering College, Department of It, Surampalem, India; Lakshmanarao A., Aditya Engineering College, Department of It, Surampalem, India; Gupta C., Christ University, School of Commerce, Finance and Accountancy, India