With the banking sector now operating in a high-tech mode, the necessity for effective, precise, and computerized loan sanctioning systems has become very crucial. The old manual mode is usually tardy and prone to errors, prompting the usage of data-based decision-making procedures. This study delves into the use of machine learning methodologies for forecasting loan approval or rejection on the basis of applicant information like income, job status, credit history, and loan value. On a publicly available dataset, several models like Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines were created and tested. The research highlights the significance of data preprocessing, feature selection, and model tuning in getting the best predictive performance. The findings indicate that ensemble models, specifically Random Forest, result in the best accuracy, giving promising potential to accelerate the speed and fairness of the loan approval process. This research helps to construct more consistent and open credit assessment systems for financial institutions.
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