Loan Approval Prediction using Machine Learning: A Comparative Analysis of Classification Algorithms

The prediction of loan approval is a crucial task for financial institutions, and has been a longstanding challenge in the industry. Historically, banks and other lenders relied on manual processes and subjective criteria to evaluate loan applications, which often led to inconsistent decisions and increased risk of loan defaults. With the rise of machine learning techniques, there is now an opportunity to develop more accurate and reliable predictive models that can help financial institutions make better lending decisions.This study proposes a comparative analysis of various machine learning algorithms for predicting loan approval. The explored algorithms include Random Forest Classifier, K-Nearest Neighbors Classifier, Support Vector Classifier, and Logistic Regression. The dataset is prepared by performing exploratory data analysis and feature engineering.Statistics like as accuracy score, F1 score, and ROC score are used to judge the execution of each approach.The findings show that the Random Forest Classifier had the highest accuracy of 98.04%, followed by K-Nearest Neighbors Classifier (78.49%), Logistic Regression (79.60%), and Support Vector Classifier (68.71%). These results highlight the potential of machine learning algorithms to improve the loan approval process and reduce the risk of loan defaults.Overall, this study provides insights into the effectiveness of different machine learning algorithms for loan approval prediction, and can be useful for financial institutions in improving their decision-making process. The proposed approach can also be extended to other domains where classification is a critical task.