Credit Score Based on Bank Loan Prediction System Using Machine Learning Techniques

  • Jagadish Kalava, Venkata Rao Maddumala, Venkata Ranga Rao Kommineni, Chavva Ravi Kishore Reddy

Abstract

Due to very new advanced technology associated with machine learning techniques used for loan approval. In financial marketing banking domain forever important for predicative system to different aspects. So our predicating credit defaulters are high task for bank loan approval for banking industry. In this paper our proposed system easily to approve or rejects with good accurate system using machine learning techniques for loan applications. In banking industries major contribution of recovery management. When the problem is ensue form the customer side our proposed system very easily to predict and possible way of payment of loan by the customer. We are use to Machine Learning (ML) algorithms are good advantages for large amount of data outcomes for predict system. We observe different machine learning algorithms are Logistic Regression (LR), Tree-based models like as Decision Tree (DT) and Random Forest (RF). These are more stable based on data set used our techniques for loan approval or reject of customers. Our experimental outcome of bring to end that the best accuracy of tree bases system algorithm, Logistic Regression and Random Forest machine learning approaches for bank loan prediction system.

Published
2020-03-30
How to Cite
Jagadish Kalava, Venkata Rao Maddumala, Venkata Ranga Rao Kommineni, Chavva Ravi Kishore Reddy. (2020). Credit Score Based on Bank Loan Prediction System Using Machine Learning Techniques. International Journal of Advanced Science and Technology, 29(3), 9418 - 9429. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/26716
Section
Articles