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...
| Main Author: | |
|---|---|
| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40844/ http://umpir.ump.edu.my/id/eprint/40844/1/CB20025.pdf |
| _version_ | 1848826163221233664 |
|---|---|
| author | Law, Paul Lik Pao |
| author_facet | Law, Paul Lik Pao |
| author_sort | Law, Paul Lik Pao |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:40:27Z |
| format | Undergraduates Project Papers |
| id | ump-40844 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:40:27Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Law, Paul Lik Pao Loan Eligibility Classification Using Machine Learning Approach |
| title | 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_short | Loan Eligibility Classification Using Machine Learning Approach |
| title_sort | loan eligibility classification using machine learning approach |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/40844/ http://umpir.ump.edu.my/id/eprint/40844/1/CB20025.pdf |