• Ms.THEEKSHA K
• Dr. Shashidhar Kini K

Abstract-Online job portals have become primary platforms for employment opportunities. However, with their popularity comes an increase in fraudulent job postings designed to exploit job seekers. These scams can lead to financial losses, identity theft, and psychological stress. This paper presents a machine learning-based system for classifying fake job postings using Natural Language Processing (NLP) techniques. By extracting linguistic and metadata-based features from job descriptions, our proposed model leverages classifiers such as Logistic Regression, Random Forest, and XGBoost. We used the Employment Scam Aegean Dataset (EMSCAD) for training and evaluation. Experimental results demonstrate that the XGBoost model provides the best performance with high precision and recall, showcasing its effectiveness in detecting fraudulent listings.

Fake job classification, machine learning, NLP, XGBoost, EMSCAD, text mining

"Fake Job Classifier Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1002-1006, July-2025, Available :http://www.jetir.org/papers/JETIRGX06187.pdf

"Fake Job Classifier Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1002-1006, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06187.pdf