Loan Default Prediction Using Machine Learning Techniques

Loan Default Prediction Using Machine Learning Techniques

1 Mrs.BHAVANA S PATIL, 2 SAKSHI J HIREMATH, 3 LAKSHMI BASAVARAJA CHOORI, 4 VIDYA S NARAYANI, 5 SAHANA YALLAPPA ABBIGERI

1 Assistant Professor & HOD, Department of Computer Science and Engineering, STJ Institute of Technology,

Ranebennur

2345 UG Students, Department of CSE, STJ Institute of Technology,  Ranebennur

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Abstract - Loan business is one of the major income sources for bank. Loan default problem is a major issue for loan business. Loans, specifically whether borrowers repay the loan or default on it, have a significant impact on a bank's profitability. By anticipating loan defaulters, the bank is able to reduce its non-performing assets. Three primary predictive analytics techniques—I Data Collection, II Data Cleaning, and III Performance Assessment—are used to research the prediction of loan defaulters. Experimental investigations reveal that when it comes to loan forecasting, the KNN model performs better than the Decision tree model.

Key Words: Machine learning, Loan prediction, Banking, Decision tree, KNN.