Loan Amount Prediction Based on Credit History and Other Variables

Abstract

Loan approval is a very important process for banking organizations. The system approved or reject the loan applications. Recovery of loans is a major contributing parameter in the financial statements of a bank. It is very difficult to predict the possibility of payment of loan by the customer. In recent years many researchers worked on loan approvalprediction systems. Machine Learning (ML)techniques are very useful in predicting outcomes for large amount of data. In this paper three machine learning algorithms, Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) are applied to predict the loan approval of customers. The experimental results conclude that the accuracy of Logistic Regression machine learning algorithm is better as compared to Decision Tree and Random Forest machine learning approaches.