Loan Approval Prediction using Machine Learning

Abstract

Loan Approval Prediction is significant research addressing the crucial task of predicting whether loan applications will be approved or denied. With financial institutions facing the challenge of efficiently evaluating numerous loan applications, machine learning offers a promising solution. This research focuses on implementing two machine learning algorithms: Support Vector Machine (SVM) as the proposed algorithm and Random Forest as the existing algorithm. SVM is chosen for its ability to handle high-dimensional data and effectively classify applicants into approved or denied categories, while Random Forest serves as a benchmark for comparison due to its robustness and scalability. The system processes various applicant features such as credit history, income, employment status, and loan amount, extracting meaningful patterns to predict loan approval outcomes. By training the models on historical loan data and evaluating their performance using metrics like accuracy, the research aims to provide financial institutions with valuable insights to streamline their loan approval process, reduce risk, and improve decision-making efficiency. Through accurate prediction of loan outcomes, this research contributes to enhancing the overall efficiency and effectiveness of the lending industry. Keywords— Dataset, Supervised Machine Learning, Models, Accuracy, Machine Learning, Support Vector Machines (SVM), Random Forest, Predictions, Feature Engineering, Data Preprocessing, Classification Algorithms, Decision Trees, Evaluation Metrics, Loan Status, Algorithm Comparison.