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Dr. Jim Mathew Philip


Loan Prediction, Machine Learning, Supervised Learning, Random Forest.


Loans account for a huge chunk of bank profits. Even though many individuals are looking for loans. Finding a legitimate candidate who will return the loan is difficult. Several misunderstandings may arise while selecting the real candidate when the process is carried out manually. As a result, we are creating a machine learning based loan prediction system that will choose the qualified applicants on its own. Although banks in our financial system can offer a wide range of goods, their primary source of income comes from their credit lines. They may profit from the interest on those loans, therefore. The profitability or loss of a bank is mostly determined by the loans it makes, namely whether its clients are making their loan repayments. The bank's non-performing assets can be decreased by foreseeing loan defaulters. Because of this, it is crucial to examine this phenomenon. The subject of reducing loan default may be studied using a wide variety of techniques, according to earlier research from this era. The nature of the various approaches must be studied in order to compare them, though, as accurate forecasts are crucial for maximizing earnings. Predicting loan defaulters is a challenging subject that is studied using a crucial predictive analytics methodology. Both bank employees and applicants will benefit from this. The loan sanctioning process will be completed in a much shorter amount of time. We are employing a machine learning method to forecast the loan data in this project.

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Published in: Volume : 9, Issue : 3
Publication Date: 3/6/2023

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