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@article{151769,
        author = {Nitesh Pandey and Ramanand Gupta and Sagar Uniyal and Vishal Kumar},
        title = {Loan Approval Prediction Using Machine Learning Algorithms Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {898-902},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151769},
        abstract = {Banking system have large number of products to earn profit, but their vital source of income is from its credit system. Because Credit system can earn from interests of that loans which they credit. Banking system always need accurate modelling system for large number of issues.The prediction of credit defaulters is one of the difficult task for anybank.But by forecasting the loan defaulters, the banks definitely may reduce its loss by reducing its non-profit assets,so that recovery of approved loans can take place without any loss and it can play as  the contributing parameter of the bank statement.This makes the study of this loan approval prediction important.Machine Learning techniques are very crucial and useful in prediction of these type of data.In this research paper four algorithms of classification based machine learning that is Logistic Regression,Decision tree, Support vector Machine and Random forest is applied and among them Support Vector Machine algorithm is most accurate to predict the loan approval with large accuracy.},
        keywords = {Loan, Machine Learning, Prediction, Testing, Training},
        month = {},
        }