Enhancing Loan Approval Prediction Using Ensemble Machine Learning Techniques Through Comprehensive Model Comparison and Performance Evaluation Analysis

Loan approval prediction is a crucial task in the financial sector, as it directly impacts risk management and decision-making processes. This study aims to enhance the accuracy of loan approval prediction by applying ensemble machine learning techniques and comparing their performance with a baseline model. The dataset used in this study contains borrower demographic, financial, and employment-related attributes, and missing values were handled using a deletion method to ensure data consistency. Several models were implemented, including Logistic Regression as the baseline model, as well as ensemble methods such as Random Forest, Gradient Boosting, and Voting Classifier. The models were evaluated using multiple performance metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The experimental results show that ensemble models consistently outperform the baseline model across all evaluation metrics. Random Forest achieved the highest ROC-AUC, indicating superior discriminative capability, while the Voting Classifier provided the best balance between precision and recall, resulting in the highest F1-Score. In addition, feature importance analysis revealed that CreditScore, Income, and Employment Type are the most influential factors in loan approval decisions. These findings demonstrate that ensemble learning methods are effective in improving predictive performance and can provide reliable support for loan approval decision-making systems.