admin DOI:
Keywords:
Loan approval prediction, machine learning, Classification Learner, MATLAB, ensemble methods, neural networks, SVM, decision trees, k-nearest neighbors, training accuracy, testing accuracy, overfitting, generalization, model comparison, credit risk modeling, financial analytics, computational efficiency
Abstract
This work presents a detailed comparison of different machine learning algorithms with the help of MATLAB by using Classification Learner to predict loan approvals. We were using applicant data on income, credit history, and level of education and we tried everything: decision trees, neural networks. We have found on consideration significant sacrifices on accuracy against the ease of computation that can guide financial institutions in selecting the appropriate model according to their requirements.
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