Developing an Efficacious System of Prediction of Loan Eligibility by Combining Artificial Intelligence (AI) and Data Analysis Tools and Techniques

Diksha Choudhary

Vol. 6, Jan-Dec 2020

Page Number: 186 - 192

Abstract:

Banks serve the necessities of everybody close to emergency clinics and schools. Individuals contact banks for different purposes. Yet, one of the most widely recognized administrations advertised by banks is credit. In any case, numerous standard individuals should know the financial strategies and advance qualification measures. This study intends to adoptive an AI (ML) model that can foresee whether an individual is qualified for a health loan by examining some essential qualities the client enters. This interaction gathers a dataset of all vital boundaries for a credit application from Kaggle. The collected dataset is then pre-processed using the invalid worth disposal strategy and encoding. At the same time, three ML models were created utilizing three distinct algorithms. They are the Random Forest, Naive Bayes NB, and LR. The pre-processed information will next be used to prepare the models. A couple of boundaries will be contrasted to evaluate the models' adequacy. The examination results demonstrate that the RF algorithm is the best concerning precision and error. The accuracy of the RF algorithm is 91%. What's more, it similarly predicts advanced qualification with lesser error values. The LR model has less accuracy and important error values, making it the most un-proficient algorithm for loan prediction.

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