Abstract :

Fake Job Postings Have Become A Significant Issue On Online Job Portals And Social Media Platforms, Where Fraudulent Recruiters Exploit Job Seekers By Collecting Personal Information, Demanding Registration Fees, Or Conducting Financial Scams. These Activities Not Only Result In Financial Loss But Also Reduce Trust In Online Recruitment Systems. Therefore, Detecting Fraudulent Job Advertisements Is Essential To Ensure A Safe And Reliable Job Search Environment. This Project Focuses On Developing A Machine Learning-based System To Identify Whether A Job Posting Is Genuine Or Fake. The Model Analyzes Various Attributes Of Job Listings, Including Job Title, Company Profile, Job Description, Location, Salary Details, And Other Relevant Features. These Data Points Are Processed And Used To Train Classification Models Capable Of Distinguishing Between Legitimate And Fraudulent Postings. Data Preprocessing Techniques Such As Cleaning, Feature Extraction, And Text Analysis Are Applied To Improve Model Performance. Machine Learning Algorithms Including Logistic Regression, Decision Tree, Random Forest, And Naïve Bayes Are Utilized To Build And Evaluate The Predictive Model. The System Learns Patterns And Characteristics Commonly Associated With Fake Job Postings And Uses Them To Detect Suspicious Or Misleading Content.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1258-1264

Section:

Articles

License:

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

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