INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In the modern financial system, banks give firms or
people looking to buy anything the necessary initial investment. to
assess a borrower’s creditworthiness and forecast the possibility
that they will be granted a loan. For lenders, banks, and financial
organisations, a loan eligibility prediction system can be helpful
in automating the loan application process and determining the
risk of giving money to a certain applicant.
It is a piece of software that uses techniques for data analysis
and machine learning. The system includes compiling data
on sanctioned loans and loan applications from a variety of
sources. The data contains facts on the borrower’s income, job
history, debt-to-income ratio, loan amount, loan period, and
other relevant information. The data is then prepared for use
in the machine’s training by being cleaned, preprocessed, and
transformed.
Then, relevant traits that can influence loan eligibility are
identified from the data. This entails creating new factors or
changing the ones already in use to predict loan eligibility.
Following the division of the data into train and test sets, a
machine learning model is selected and trained from different
algorithms that are available. The testing set is used to evaluate
the model’s performance after it has been trained on the training
set.
After the method for predicting loan eligibility is created, it
can be incorporated into a programme that banks and lenders
can use to determine loan eligibility. The loan eligibility decisionmaking process should be well explained in the application, which
should also be easy to use. To make sure the model is reliable
and useful over time, the loan eligibility prediction system should
be constantly reviewed and updated with fresh data.
In conclusion, a loan eligibility prediction system will be
a useful tool for banks, financial institutions, and lenders to
automate the application process and determine the risk involved
in giving money to a certain borrower. The system entails
gathering, pre-processing, and manipulating data; extracting
pertinent features; choosing an appropriate machine learning
model; training the model; and implementing it in a lending and
banking application.
Keywords:
Loan eligibility prediction, machine learning
Cite Article:
"Loan Eligibility Prediction System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.d201-d205, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304328.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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