• Dhinesh Kumar S
• Raghul Manickam V S
• Radhamani V

The proliferation of online job listings and the increasing reliance on digital recruitment platforms have given rise to a pressing issue of fake job postings. In this era of digital job searching, job seekers are vulnerable to deceptive job offers, which can lead to financial losses and emotional distress. This research paper explores the development and application of machine learning algorithms for the detection of fake job postings. This study compiles a comprehensive dataset of job listings, encompassing various attributes such as job descriptions, company details, and application processes. By leveraging state-of-the-art machine learning techniques, including natural language processing and feature engineering, this research aims to identify patterns and characteristics that distinguish genuine job opportunities from fraudulent ones. The outcomes of this research are not only expected to enhance the job-seeking experience for individuals but also to assist job platforms, employers, and regulatory authorities in preventing the dissemination of deceptive job postings.

Machine Learning, Fake job, Classifier, Supervised learning, Random Forest Classifier

"Fake Job Recruitment Detection Using Supervised Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.h342-h345, October-2023, Available :http://www.jetir.org/papers/JETIR2310641.pdf

"Fake Job Recruitment Detection Using Supervised Machine Learning Approaches", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. pph342-h345, October-2023, Available at : http://www.jetir.org/papers/JETIR2310641.pdf