Detection of Fake Online Recruitment using Machine Learning Approach
Jayanth Medapati, Yashaswi Arradi, Ronan Kongala, Shanmugasundaram Hariharan, J. Shanmugapriyan, Karuppiah Natarajan · 2025
While many organizations these days prefer to post their job opportunities on the web so that job seekers can access them conveniently and easily, this practice might be an example of scam from the side of swindlers who offer job hunters tasks and services in exchange for money. Many people fall victims to this type of fraud and lose a considerable amount of money as a result. The proposed approach uses a variety of machine learning algorithms inclusive of supervised learning tools and natural language processing methods to analyze and sort job advertisements. By using both single classifiers and ensemble classifiers, the system assesses results and compares them, thus recognizing fraudulent job advertisements on the Internet. Model performance will be evaluated using metrics like accuracy, precision, recall, and F1-score. This study aims to demonstrate the potential of boosting techniques for achieving high accuracy in fake job posts prediction, potentially leading to improved outcomes Therefore, the value of the research in helping to create a more secure online job market can serve to establish a level of trust for job seekers and provides them with protection from the financial and emotional risks related to the misuse of deceptive job postings.