Loan Prediction Analysis using Machine Learning Techniques

Abstract

Loans account for a large portion of bank profits. Despite the fact that many people are looking for loans. Finding a legitimate applicant who will return the loan is difficult. Choosing a real applicant may be difficult if the process is done manually. As a result, we are creating a machine learning-based loan prediction system that will choose the qualified applicants on its own. Both the applicant and the bank staff will benefit from this. There will be a significant reduction in the loan sanctioning period of time. In this research. The majority of the bank's revenue is generated directly from the interest income on loans. Even when the bank authorizes the loan following a lengthy verification and testimony process, there is no guarantee that the chosen hopeful is the best hopeful. When performed manually, this operation requires additional time. We have the ability to foretell whether a specific hopeful is secure or not, and machine literacy has mechanized the entire testifying procedure. Loan Prognostic is extremely beneficial for both banks' clients and potential borrowers. I use some machine learning algorithms techniques to predict the loan data. Those are the Decision Tree, K Nearest Neighbour, SVM, Naive Bayes, and Random Forest Classifier. Nevertheless, I did uncover promising setups for both purposes. I got the best accuracy from Naïve Bayes which was 82.16%.