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Loan eligibility classification using logistic regression

Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, d ue 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 c...

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Main Authors: Lik Pao, Paul Law, Mohd Arfian, Ismail
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40314/1/Loan%20eligibility%20classification%20using%20logistic%20regression.pdf
http://umpir.ump.edu.my/id/eprint/40314/2/Loan%20eligibility%20classification%20using%20logistic%20regression_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40314/
https://doi.org/10.1109/ICSECS58457.2023.10256402
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spelling my.ump.umpir.403142024-04-16T04:05:07Z http://umpir.ump.edu.my/id/eprint/40314/ Loan eligibility classification using logistic regression Lik Pao, Paul Law Mohd Arfian, Ismail Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, d ue 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 Fl-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% Fl score. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40314/1/Loan%20eligibility%20classification%20using%20logistic%20regression.pdf pdf en http://umpir.ump.edu.my/id/eprint/40314/2/Loan%20eligibility%20classification%20using%20logistic%20regression_ABS.pdf Lik Pao, Paul Law and Mohd Arfian, Ismail (2023) Loan eligibility classification using logistic regression. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 326-329. (192961). ISBN 979-835031093-1 https://doi.org/10.1109/ICSECS58457.2023.10256402
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
English
topic Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Lik Pao, Paul Law
Mohd Arfian, Ismail
Loan eligibility classification using logistic regression
description Machine learning is becoming increasingly vital in various domains, including loan eligibility classification, d ue 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 Fl-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% Fl score.
format Conference or Workshop Item
author Lik Pao, Paul Law
Mohd Arfian, Ismail
author_facet Lik Pao, Paul Law
Mohd Arfian, Ismail
author_sort Lik Pao, Paul Law
title Loan eligibility classification using logistic regression
title_short Loan eligibility classification using logistic regression
title_full Loan eligibility classification using logistic regression
title_fullStr Loan eligibility classification using logistic regression
title_full_unstemmed Loan eligibility classification using logistic regression
title_sort loan eligibility classification using logistic regression
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40314/1/Loan%20eligibility%20classification%20using%20logistic%20regression.pdf
http://umpir.ump.edu.my/id/eprint/40314/2/Loan%20eligibility%20classification%20using%20logistic%20regression_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40314/
https://doi.org/10.1109/ICSECS58457.2023.10256402
_version_ 1822924220305244160
score 13.23243

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