Loan eligibility classification using logistic regression

• 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

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.