Predict Loan Approvals in Banking Industry using Machine Learning Algorithms

Abstract

Predicting loan approval is a critical task in the banking sector, as it affects both financial institutions and loan applicants. Traditional methods often involve a time-consuming and error-prone manual process. This paper explores the application of machine learning algorithms, including KNeighborsClassifier, RandomForestClassifier, Support Vector Classifier (SVC), and Logistics Regression, in predicting loan approval. A comparative analysis of these algorithms is conducted to determine their effectiveness in this domain.