International Journal on Science and Technology

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Call for Paper Volume 17 Issue 3 July-September 2026 Submit your research before last 3 days of September to publish your research paper in the issue of July-September.
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Volume 17 Issue 3
July-September 2026

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Agriculture Loan Recommendation

Author(s) Kancham Venkatalakshumma, K. Neeharika
Country India
Abstract Technology has improved humankind's existence and standard of living. We intend to produce something fresh and unique every day. In the banking industry, candidates receive proof or backup before the loan amount is approved. We have machines to support our life and make us somewhat complete, and we have a remedy for every other issue. The system's evaluation of the candidate's past information determines whether or not the application is accepted. In the banking industry, many people seek for loans every day, yet banks have limited resources. In this situation, employing a classes-function method to make the correct prediction would be highly advantageous. For instance, the support vector machine classifier, logistic regression, random forest classifier, etc. The amount of loans, or whether the client or customer repays the loan, determines a bank's profit and loss. For the banking industry, loan recovery is crucial. In the banking industry, the process of improvement is crucial. utilizing several categorization techniques, a machine learning model was constructed utilizing the candidates' past data. This paper's primary goal is to use machine learning models trained on the historical data set to forecast whether a new applicant will be awarded the loan or not.
Keywords Agriculture Loan, machine learning, banking industry, random forest classifier, support vector machine.
Field Engineering
Published In Volume 17, Issue 3, July-September 2026
Published On 2026-07-04
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Crossref DOI prefix of IJSAT is 10.71097/IJSAT

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