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Predicting vehicle loan eligibility using random forest comparing with linear regression based on accuracy.

Predicting vehicle loan eligibility using random forest comparing with linear regression based on accuracy.

Authors :
Reddy, D. Thrinath
Parvathy, L. Rama
Source :
AIP Conference Proceedings. 2023, Vol. 2822 Issue 1, p1-8. 8p.
Publication Year :
2023

Abstract

The study's major goal is to identify accuracy in auto loan eligibility utilising machine learning methods, namely the Random Forest and the Linear Regression. For discovering accuracy in auto loan eligibility, the Random Forest and Linear Regression with response rate N=90 were repeated several times 180 times. The Random Forest method outperforms the Linear Regression technique (78.58%) in terms of accuracy. The loan prediction p=0.050 (p<0.05) independent samples T-test has a high level of significance. A comparison of findings revealed that the Novel Random Forest Method outperforms the linear Regression algorithm in terms of discovering accuracy in auto loan eligibility. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0094243X
Volume :
2822
Issue :
1
Database :
Academic Search Index
Journal :
AIP Conference Proceedings
Publication Type :
Conference
Accession number :
173612698
Full Text :
https://doi.org/10.1063/5.0172892