Loan Approval Prediction Using Machine Learning: A Data-driven Approach to Binary Classification
Authors
Anoop, Shweta
Issue Date
2025.17.12
Degree
Master of Business Administration
Publisher
Dublin Business School
Rights holder
Rights
Open Access
Abstract
This research analyzes the usage of machine learning techniques for predicting loan approval accuracy and fairness. The research employs data from real world credit risk datasets and SMOTENC to tackle the class imbalance problem in building a robust binary classification model. Such methods implemented include Logistic Regression and Random Forest algorithms, and evaluated through the metrics accuracy, F1 score, and AUC ROC. The data privacy and bias mitigation are prioritized. The findings offer a replicable framework for efficient and inclusive evaluation of credit risk, and therefore contribute to innovative finance decision making.