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  Published Paper Details:

  Paper Title

A Method for Loan Approval Prediction Using a Machine Learning Algorithm

  Authors

  Vedant Shinde,  Pranav Sandbhor,  Nikhil Waghmare,  Satyajit Sirsat

  Keywords

outlier, Prediction, loan, component, Overfitting, Safe, Bank loans, Transform, machine learning

  Abstract


Many more people are seeking for bank loans as a result of the growth in the banking industry. These loans cannot all be approved. Gains from loans are what bank assets primarily make revenue from. The goal of banks is to allocate their resources towards secure clientele. Even though loans are approved by many banks these days following extensive verification and validation procedures, there is never a guarantee that the chosen consumer will be secure. As a result, it's critical that the banking industry use a variety of strategies to identify clients who make their loan payments on schedule. The random forest technique is used in this report to classify the data. Using a training dataset, the Random Forests method creates a model. This model is then applied to test data, yielding the desired result. Many more people are seeking for bank loans as a result of the growth in the banking industry. These loans cannot all be approved. Gains from loans are what bank assets primarily make revenue from. The goal of banks is to allocate their resources towards secure clientele.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02003

  Paper ID - 261167

  Page Number(s) - 10-14

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Vedant Shinde,  Pranav Sandbhor,  Nikhil Waghmare,  Satyajit Sirsat,   "A Method for Loan Approval Prediction Using a Machine Learning Algorithm", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.10-14, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02003.pdf

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Call For Paper July 2024
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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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