Comparative Study of Machine Learning Algorithms for Loan Default Prediction

This review paper compares machine learning algorithms for loan default prediction, focusing on preprocessing techniques, Random Forest, Gradient Boosting Machine (GBM), Naive Bayes, and visualization methods. It highlights the critical role of these algorithms in financial risk assessment. The study evaluates their performance, strengths, weaknesses, and suitability for different data types. It emphasizes the importance of selecting the right algorithm based on dataset characteristics. The findings emphasize advanced machine learning's significance in improving risk management and lending decisions in the financial sector. Ongoing research aims to enhance prediction model accuracy further.