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Loan Eligibility Classification Using Machine Learning Approach

Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, due to its ability to analyze large amounts of data, develop predictive models, adapt to new information, and automate processes. This research paper presents a study on loan eligibility cl...

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Main Author: Law, Paul Lik Pao
Format: Undergraduates Project Papers
Language:English
Published: 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/40844/1/CB20025.pdf
http://umpir.ump.edu.my/id/eprint/40844/
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spelling my.ump.umpir.408442024-04-02T06:43:23Z http://umpir.ump.edu.my/id/eprint/40844/ Loan Eligibility Classification Using Machine Learning Approach Law, Paul Lik Pao QA75 Electronic computers. Computer science QA76 Computer software Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, due to its ability to analyze large amounts of data, develop predictive models, adapt to new information, and automate processes. This research paper presents a study on loan eligibility classification using a machine learning approach by comparing the performance of three Machine Learning algorithms which were Logistic Regression, Random Forest, and Decision Tree. This research was conducted using Python and Jupyter Notebook for data analysis and model development. The models were then evaluated on the testing set using evaluation metrics such as Accuracy, Precision, Recall, And F1-Score. The performance of the models was compared to identify the most effective algorithm for loan eligibility classification. Among the three ML approach, the LR model appears to be the most effective at classify loan eligibility, with the 82% accuracy score, 82% recall score, 81% precision score and 79% F1 score. 2023-05 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40844/1/CB20025.pdf Law, Paul Lik Pao (2023) Loan Eligibility Classification Using Machine Learning Approach. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Law, Paul Lik Pao
Loan Eligibility Classification Using Machine Learning Approach
description Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, due to its ability to analyze large amounts of data, develop predictive models, adapt to new information, and automate processes. This research paper presents a study on loan eligibility classification using a machine learning approach by comparing the performance of three Machine Learning algorithms which were Logistic Regression, Random Forest, and Decision Tree. This research was conducted using Python and Jupyter Notebook for data analysis and model development. The models were then evaluated on the testing set using evaluation metrics such as Accuracy, Precision, Recall, And F1-Score. The performance of the models was compared to identify the most effective algorithm for loan eligibility classification. Among the three ML approach, the LR model appears to be the most effective at classify loan eligibility, with the 82% accuracy score, 82% recall score, 81% precision score and 79% F1 score.
format Undergraduates Project Papers
author Law, Paul Lik Pao
author_facet Law, Paul Lik Pao
author_sort Law, Paul Lik Pao
title Loan Eligibility Classification Using Machine Learning Approach
title_short Loan Eligibility Classification Using Machine Learning Approach
title_full Loan Eligibility Classification Using Machine Learning Approach
title_fullStr Loan Eligibility Classification Using Machine Learning Approach
title_full_unstemmed Loan Eligibility Classification Using Machine Learning Approach
title_sort loan eligibility classification using machine learning approach
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40844/1/CB20025.pdf
http://umpir.ump.edu.my/id/eprint/40844/
_version_ 1822924256434978816
score 13.232414

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