International Journal of Science, Engineering and TechnologyAuthors: Assistance Professor Mr. R. Srinivas, Kattamuri Lakshmi Gayathri,, Kommalapati Bindu Sri,, Baligodugula Lakshmi Kranthi, Purimetla Jahnavi
Abstract: Loan eligibility prediction is a critical task in the financial sector, enabling institutions to assess applicants’ creditworthiness accurately and reduce default risks. Traditional machine learning models often struggle with heterogeneous and imbalanced financial data, which affects prediction reliability and fairness. This study proposes a Hybrid Machine Learning Framework (HMLF) that integrates feature selection, ensemble learning, and optimization to enhance predictive performance and interpretability. The framework combines Logistic Regression, Random Forest, Gradient Boosting, and Deep Neural Networks within a stacking ensemble to leverage the complementary strengths of each model. Feature engineering, normalization, and Synthetic Minority Oversampling Technique (SMOTE) are applied to improve data quality and class balance. The hybrid model is trained and validated on benchmark financial datasets using cross-validation to ensure generalization. Experimental results show that the proposed approach achieves higher accuracy, precision, recall, and F1-score compared to traditional single-model classifiers. The ensemble design improves stability and reduces bias in decision outcomes. The findings highlight that the proposed hybrid system provides a reliable, transparent, and scalable solution for automated loan eligibility prediction, supporting financial institutions in making data-driven and fair lending decisions.
