Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{190550,
        author = {A. SUVETHA and D. NANDHINI},
        title = {Fraudulent job advertisement detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3102-3106},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190550},
        abstract = {Online job portals contain many fake job advertisements that mislead job seekers and cause financial or personal data loss. This project uses Machine Learning with TF-IDF, Logistic Regression, and Random Forest to classify job posts as real or fake. The model achieves high accuracy and provides a reliable method to reduce recruitment fraud.
Online job portals contain many fake job advertisements that mislead job seekers and cause financial or personal data loss. This project uses Machine Learning with TF-IDF, Logistic Regression, and Random Forest to classify job posts as real or fake. The model achieves high accuracy and provides a reliable method to reduce recruitment fraud.},
        keywords = {},
        month = {January},
        }