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 Machine Learning based Loan Eligibility Prediction using Random Forest Model Subject The topic of the resource Borrowers; Customer Prediction; Data Collecction; Lloan Eligibility; Machine Learning; Random Forest Description An account of the resource When one or more people, organizations, or other entities lend money to other people, organizations, or entities, it is known as a loan. The recipient (that is the borrower) takes on a debt for which he or she is normally accountable for interest payments until the loan is repaid. The major goal of this proposed model is to ensure that an individual, institution, or organization seeking for a loan is properly verified before granting them the loan they require. Before authorizing a loan for any individual or business several factors must be considered. That including gender, education, and the number of dependents. The goal of proposed model is to automate the method, which will save time and energy while improving the efficiency of the process. This particular process input is having two different kind of data set. First one is train data set and second set is test data set. The first date set that is train data set is generally used to train and assess the machine learning model accuracy. The loan eligibility predictions are generated using the test data set. To forecast loan eligibility and train this random forest, machine learning method called Random Forest. The proposed random forest model is providing higher accuracy level. This model is providing 28 % higher accuracy level compare to regular prediction. 2022 IEEE. Creator An entity primarily responsible for making the resource Reddy C.S.; Siddiq A.S.; Jayapandian N. Source A related resource from which the described resource is derived 7th International Conference on Communication and Electronics Systems, ICCES 2022 - Proceedings, pp. 1073-1079. 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 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCES54183.2022.9835875" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCES54183.2022.9835875</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136323011&amp;doi=10.1109%2FICCES54183.2022.9835875&amp;partnerID=40&amp;md5=7fcf8f6c66506dc06bba7a8b81831425" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136323011&amp;doi=10.1109%2fICCES54183.2022.9835875&amp;partnerID=40&amp;md5=7fcf8f6c66506dc06bba7a8b81831425</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549634-6 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 Reddy C.S., Christ, Deemed to Be University, Department of Cse, Bangalore, India; Siddiq A.S., Christ, Deemed to Be University, Department of Cse, Bangalore, India; Jayapandian N., Christ, Deemed to Be University, Department of Cse, Bangalore, India