Loan approval prediction is a crucial task in the banking sector that involves assessing the creditworthiness of a candidate based on historical data. The process of evaluating loan applications through manual methods has been known to be prone to errors and can be time-consuming. This highlights the need for an automated system that utilizes machine learning algorithms to provide efficient and accurate loan approval predictions. The objective of this study is to develop a machine learning model using different classification algorithms to predict loan approval based on historical data of loan applicants. The study identifies relevant features for the prediction task by collecting, pre-processing, and analyzing the data. Three different classification algorithms are used to build the predictive models, and their performance is evaluated using cross-validation techniques. The study concludes that machine learning algorithms can significantly improve the loan approval prediction process in the banking sector.The Random Forest with Grid Search CV algorithm outperforms XGBoost and random forest algorithms used in this study in predicting loan approval, and the developed model can expedite the loan approval process and reduce errors.

Machine learning, Loan approval prediction, XGBoost, Random Forest, Grid Search CV, Cross-validation.