Smart Loan Approval Prediction System

Abstract

In the modern banking sector, loan approval is a critical process that determines whether an applicant is eligible for credit based on various financial and personal factors. Traditional manual evaluation methods are often time-consuming, inconsistent, and prone to human error. To address these challenges, this study proposes a Loan Prediction System using Machine Learning techniques to automate and improve the accuracy of loan eligibility decisions. The system utilizes historical loan data consisting of features such as applicant income, loan amount, credit history, employment status, and property area. Data preprocessing techniques such as handling missing values, encoding categorical attributes, and normalization are applied to ensure data quality. Multiple classification algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are implemented and compared based on accuracy and performance metrics. Experimental results indicate that the Random Forest model achieves the highest accuracy of 89%, outperforming other models. The proposed system enhances decision-making efficiency for financial institutions and reduces the risk of loan default by providing reliable and automated predictions.