Abstract :

The Loan Approval Process Is A Crucial Function In Financial Institutions, Where Accurate And Timely Decisions Are Essential. Traditionally, Loan Applications Are Evaluated Manually, Which Can Be Time-consuming, Inconsistent, And Influenced By Human Bias. This Project Presents A Machine Learning-based Approach To Automate The Loan Eligibility Prediction Process, Improving Efficiency And Decision-making. The Model Predicts Whether A Loan Applicant Is Eligible For A Loan Based On Various Demographic And Financial Attributes Such As Gender, Marital Status, Education, Applicant Income, Credit History, And Loan Amount. The Dataset Used In This Study Consists Of 614 Records With 13 Features Obtained From A Publicly Available Source. Data Preprocessing Techniques Were Applied, Including Handling Missing Values, Encoding Categorical Variables, And Feature Engineering. A New Feature, Total Income, Was Created By Combining Applicant And Co-applicant Incomes To Enhance Predictive Performance. Exploratory Data Analysis (EDA) Was Conducted Using Visualization Tools To Identify Patterns And Relationships In The Data.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1293-1301

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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