Comparative Analysis for Loan Approval Prediction System Using Machine Learning Algorithms
摘要
In the banking sector, predicting loan acceptance is a crucial task, and machine learning techniques may be used to create models that can do this. To identify which machine learning method produces the best results for this problem, we compare a variety of techniques, which include decision trees, random forests, support vector machines, Gaussian Naive Bayes, XGBoost, and K-Nearest Neighbor. The analysis takes a number of criteria into account, including each algorithm’s computational cost and scalability as well as accuracy, precision, recall, and F1 score. Based on their unique requirements and dataset, financial institutions may use the findings of this investigation to determine the best suitable algorithm for loan approval prediction.