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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM41619

FinPredict:- A Machine Learning Based Loan Eligibility and Interest Rate Prediction System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM41619

Volume:

9 |

2 |

Feb 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM41619

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DSV

Devisetti Sai Venkatalakshmi1

VPPT

Veesam Parasuram Pavan Teja2

RCSSK

Rongali Chetan Sai Surya Kumar3

GPCR

Gona Purna Chandra Rao4

GR

Gude Ramadevi5

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Abstract

In the contemporary financial environment, determining loan eligibility accurately and efficiently is crucial for both financial institutions and prospective borrowers. FinPredict aims to revolutionize the loan approval process by leveraging advanced machine learning techniques to predict loan eligibility and recommend suitable banks along with their respective interest rates. Our model utilizes Decision Tree and Random Forest algorithms, achieving accuracies of 96.7% and 97.8%, respectively. The system is designed to be intuitive and user-friendly, providing users with a seamless experience to input their data and receive immediate feedback. This project empowers individuals in making informed financial choices while assisting financial institutions in streamlining their loan approval processes.

KEYWORDS

Loan Eligibility Prediction Machine Learning Decision Trees Random Forest Interest Rate Estimation Financial Technology

Cite This Article

Devisetti Sai Venkatalakshmi1, Veesam Parasuram Pavan Teja2, Rongali Chetan Sai Surya Kumar3, Gona Purna Chandra Rao4, Gude Ramadevi5, "FinPredict:- A Machine Learning Based Loan Eligibility and Interest Rate Prediction System," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 2, Feb 2025. https://doi.org/10.55041/IJSREM41619

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Publication Date

2/18/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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3

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5

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The authors declare no conflict of interest related to this research work.

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