Loan eligibility prediction using machine learning algorithm
Today banks have become a principal instrument for offering both physical persons and organizations with the necessary financial means banks require for such goals like property acquisition or project financing. What ultimately decides this is the contemplation of a borrower's creditworthine...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/7027/ http://eprints.utar.edu.my/7027/1/fyp_IB_2024_LZH.pdf |
| Summary: | Today banks have become a principal instrument for offering both physical persons and
organizations with the necessary financial means banks require for such goals like property
acquisition or project financing. What ultimately decides this is the contemplation of a
borrower's creditworthiness and chances that he or she will repay back what was borrowed.
The implementing of a loan qualifier prediction system brings enormous advantage to the
lenders, banks, and financial institutions. This helps in reducing the gap between the two phase
that is loan application process and its decision- making process where credit is extended to
the appropriate applicant based on their risk level. The project objective is to create and analyse
a comparative model illustrating how to employ different machine learning algorithms in
domains like loan approval processes, pattern recognition, limitations assessment, and
performance metric evaluation. The study incorporates three prominent machine learning
algorithms: Predicting the target variable using logistic regression, decision tree and it’s variant
random forest for credit scoring model. The analysis's findings prove that, in terms of both
accuracy and error, the RF algorithm is the best out of three models. The final product of the
project is loan eligibility prediction website with machine learning model implemented for real
life scenario use. |
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