The Loan Approval Prediction System is a machine learning-based application developed to automate the process of evaluating loan applications. It analyzes applicant data such as income, credit history, employment status, and loan amount to predict whether a loan should be approved or not. By using historical data and applying data preprocessing techniques, the system improves the accuracy and reliability of predictions. The model is built using classification algorithms such as Logistic Regression, Decision Tree, or Random Forest, and the best-performing model is selected for deployment. This system helps financial institutions reduce manual effort, minimize risk, and make faster, data-driven decisions. It also enhances efficiency and ensures a more consistent and unbiased loan approval process. Overall, The Loan Approval Prediction System is a machine learning-based application that predicts whether a loan will be approved based on user details like income and credit history. It uses models such as Logistic Regression, Random Forest, and XGBoost for accurate decision-making. Built with Python and Flask, it provides quick, data-driven loan predictions through a simple web interface.
Reddy et al. (2026) studied this question.