Performance Enhancement and Comparative analysis for Credit Approval Using XGBoost, SVM and Multi-Layer Perceptron

The demand for credit card is increasing exponentially due to the benefits and security it was providing for the customers. This makes the credit card companies to deliberate before approving the credit card for customers. So the customers, who met the criteria that were set by the companies, were able to get the credit card. The goal of this paper is to build a machine learning model whose accuracy and performance is enhanced due to boosting algorithms using the datasets on UCI (University of California, Irvine). This model will be building on xgboost algorithm and will be compared with SVM (Support Vector Machine), multi-layer perceptron (MLP). Our result shows that the overall performance of our boosting model is slightly better than the other two models.