A Comparative Study On Fake Job Post Prediction

The surge in online job portals has significantly facilitated job seekers in finding opportunities, but it has also given rise to the alarming issue of fraudulent job postings.To address this challenge, we present a novel approach for Fake Job Post Prediction using ma-chine learning algorithms, specifically Support Vector Machines, Decision Trees (DT), and Random Forests (RF).The primary objective is to develop a robust and accurate predictive system that can distinguish between genuine and fake job advertisements, thereby safe-guarding job seekers from potential risks.The proposed system leverages the discriminative power of SVM to create a hyperplane that effectively separates legitimate and deceptive job postings.In some case they may get into the scams like paying money in the form of application fees in the need of job or the assurance of getting job after paying the money.The framework helps us to detect the posted jobs are fake or not.