A Comparative Study on Fake Job Post Prediction Using Different Machine Learning Techniques

Abstract

Lately, with the progression of current innovation and advertising, the advancement of new positions has turned into a test in this day and age. Along these lines, crafted by distorting predictions will be of worry to all. Likewise with numerous order assignments, making bogus predictions requires a great deal. The development requires the utilization of an assortment of strategies for AI and sequencing calculations, for example, KNN, memory sequencing, swarm collectors, and profound organizations, to anticipate whether the innovation is valid or bogus. We tried the Employment Scam Aegean Dataset (EMSCAD), which contains 18,000 occasions. We utilized three complete segments for this profound pipeline. The classifications shrouded in the preparation represent around 98% of the classifications (DNN) for foreseeing extortion.