A New Framework for Loan Defaulter System Using Support Vector Machine Over a Conditional Random Field Algorithm for a Banker

The aim of the study is to predict loan defaulters case in the banking system in order to detect loan defaulters using a novel machine learning method. Here we are using some two algorithms. Materials and Methods: Datasets are downloaded from the kaggle website to train using Novel Support Vector al...

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Bibliographic Details
Published in:ECS transactions 2022-04, Vol.107 (1), p.13459-13471
Main Authors: Kumar, K. Kalyan, Ramakrishnan, V
Format: Article
Language:English
Online Access:Get full text
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Summary:The aim of the study is to predict loan defaulters case in the banking system in order to detect loan defaulters using a novel machine learning method. Here we are using some two algorithms. Materials and Methods: Datasets are downloaded from the kaggle website to train using Novel Support Vector algorithms by applying in 346 records having 10 attributes sample size =250. The model is to analyze and predict the loan defaulters using Support Vector Machine and Random Field algorithm. Results and Discussion: The data was split into training and test datasets. The Random Field algorithm has shown accuracyhas (0.58) with (p
ISSN:1938-5862
1938-6737
DOI:10.1149/10701.13459ecst