Online job portals' explosive expansion has greatly expanded employment options, but it has also increased the number of fake job postings. These fraudulent job postings deceive job searchers, leading to monetary loss, data theft, and a decline in confidence in online job boards. This project offers a machine learning-based method for correctly and automatically identifying fraudulent job advertisements. The system makes use of a dataset that includes a variety of job-related characteristics, including requirements, benefits, job title, company profile, job description, and other metadata. To convert unprocessed textual data into useful numerical representations, data preparation methods such as tokenisation, feature extraction, and text cleaning are used. Predictive models are constructed using a variety of classification algorithms, including Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. Metrics, including accuracy, precision, recall, and F1-score, are used to assess these models' performance. The classifier that performs the best among the implemented models is chosen for deployment. Users will be able to steer clear of fraudulent job advertising because of the proposed system's ability to efficiently categorise job advertisements as authentic or fraudulent. By offering an automated and scalable method for detecting fraudulent jobs using machine learning techniques, this research helps to improve the dependability of online employment platforms.
Ekambaram et al. (Thu,) studied this question.
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