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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

  Abstract


In our monetary system, banks have various things to sell yet head sort of income of any banks is on its credit line. A bank's advantage or a setback relies by and large upon credits for instance whether the clients are dealing with the development or defaulting. By anticipating the development defaulters, the bank can lessen its Non Performing Assets. This makes the examination of this characteristic essential. Past examination in this time has shown that there are such endless strategies to focus on the issue of controlling Development default. Be that as it may, as the right expectations are vital for the boost of benefits, it is crucial for concentrate on the idea of the various strategies and their correlation.[1] A vital approach in prescient examination is utilized to concentrate on the issue of anticipating credit defaulters: The Calculated relapse model. Strategic Relapse models have been performed and the various proportions of exhibitions are registered.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02002

  Paper ID - 261168

  Page Number(s) - 4-9

  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.4-9, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02002.pdf

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Call For Paper July 2024
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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
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
ISSN
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