LOAN APPROVAL PREDICTION USING MACHINE LEARNING

Loan approval prediction is a critical task in the financial sector, enabling institutions to assess credit risk and make informed lending decisions. This research presents a comprehensive comparative study of various machine learning approaches for loan approval prediction, evaluating both traditional algorithms and deep learning architectures. We implemented and compared five traditional machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and SVM) against three neural network architectures (Simple Feed-Forward Neural Network, Deep Neural Network with Batch Normalization, and Wide & Deep Network). Using a synthetic dataset of 1,000 loan applications with features including income, credit score, and employment status, we achieved the highest accuracy of 93.5% with Gradient Boosting, closely followed by the Wide & Deep Neural Network at 93.5%. Feature importance analysis revealed credit score (45%) as the most influential factor, followed by income (40%) and employment status (15%). Additionally, we developed a professional Tkinter-based graphical user interface that enables real-time predictions, batch processing, and interactive visualizations. This system demonstrates the practical application of machine learning in financial decision-making and provides a framework for deploying predictive models in production environments.