Loan Amount Prediction Using Multi-Model Machine Learning

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Abstract

In the banking and finance sector, predicting the size of a loan is a crucial task for precisely determining a potential borrower's creditworthiness. This work suggests a novel method for more accurate loan amount prediction using multi-model machine learning approaches. To improve accuracy and resilience in loan amount estimates, the suggested method combines the strengths of various models, including support vector machines, decision trees, and linear regression. The dataset that was employed in this study includes a number of variables, including information on income, credit history, employment status, and loan purpose. The various machine learning models are fed these features and trained on past loan data. The models are integrated after training to produce an ensemble model that aggregates the predictions. This ensemble model makes use of each model's advantages to successfully capture various patterns and correlations in the data. Experimental findings on a real-world loan dataset show that the multi-model method is more accurate and generalizable than individual models.

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Keywords

Ensemble Model, Decision Trees, Support Vector Machines, MultiModel Machine Learning, Creditworthiness, Loan Amount Prediction, And Linear Regression