Loan approval prediction using machine learning techniques

Loans are the primary revenue generator for banks because they earn interest income from the credit, they extend through lending products. However, defaults on these loans can significantly impact profits. By identifying borrowers likely to default, banks can mitigate risk and reduce non- performing loans in their portfolio. This makes the study of this phenomenon very important. Previous research has shown there are many methods to study loan default prediction, which is essential for maximizing profits. However, comparing the nature and performance of different techniques is critical for reliability. The project focuses on leveraging machine learning techniques to enhance the efficiency and accuracy of loan approval processes in financial institutions. By analyzing a dataset comprising various applicant attributes and historical loan data, predictive models are developed to assess the likelihood of loan repayment or default. This project will stand out by using multiple feature engineering techniques such as Binning and Bucketing, Polynomial, Interaction Features to enhance the dataset. Through multiple feature engineering, model evaluations and ensembling techniques, the project aims to provide a comprehensive solution for automating and optimizing loan approval decisions. Keywords: Loans approval, Machine Learning.