Analysis of Fraudulent Job Postings Using Machine Learning

In the age of digital recruitment, the proliferation of fraudulent job postings poses significant challenges for job seekers and legitimate employers alike. These deceptive listings not only waste time and resources but also endanger personal data and propagate scams. Addressing this issue, we present a comprehensive machine learning methodology to accurately discern between genuine and counterfeit job opportunities. Leveraging a rich dataset procured from Kaggle, this paper details the deployment of a logistic regression classifier, judiciously trained on a fusion of textual and meta-features extracted from job advertisements. The classifier underwent rigorous evaluation, manifesting an impressive accuracy of 96.78% in segregating authentic posts from fraudulent ones. The implementation of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization on textual data, alongside meta-features such as job description length, enabled the model to learn and predict with high precision. The implications of this research are substantial, offering a scalable and efficient tool for job platforms to safeguard their users and ensure the integrity of their listings.