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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Format: | Conference or Workshop Item |
Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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Subjects: | |
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 |
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author | Lik Pao, Paul Law Mohd Arfian, Ismail |
author_facet | Lik Pao, Paul Law Mohd Arfian, Ismail |
author_sort | Lik Pao, Paul Law |
collection | UMP |
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. |
first_indexed | 2024-04-22T01:25:47Z |
format | Conference or Workshop Item |
id | UMPir40314 |
institution | Universiti Malaysia Pahang |
language | English English |
last_indexed | 2024-04-22T01:25:47Z |
publishDate | 2023 |
publisher | Institute of Electrical and Electronics Engineers Inc. |
record_format | dspace |
spelling | UMPir403142024-04-16T04:05:07Z 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 |
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 |
title | 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_short | Loan eligibility classification using logistic regression |
title_sort | loan eligibility classification using logistic regression |
topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
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 |
work_keys_str_mv | AT likpaopaullaw loaneligibilityclassificationusinglogisticregression AT mohdarfianismail loaneligibilityclassificationusinglogisticregression |