A Comparative Analysis for Loan Approval Prediction using Machine Learning

Abstract

The financial sector of any nation is the key factor in elevating the economy of nation. The biggest issue that finances departments are facing is to approve loans for valid candidates only. Our proposed model for predicting the loan approval for valid candidates uses three different supervised learning algorithms for classification of loan approved or not, on the basis of candidate attribute values. In proposed study ensemble technique has been used to find out best classifier algorithm to train our model. In this paper we are comparing KNN, Decision Tree, Logistic Regression, SVC, and Naïve Bayes classifier algorithms based on their accuracy for provided data set to find out the highly accurate and efficient algorithm for designing a model for approving loan to valid candidate which will also help in minimizing the financial frauds.