Fake Job Post Prediction

T Subburaj; Chandana K L

doi:10.34293/sijash.v11iS1-July.6345

T Subburaj Department of Computer Application Rajarajeswari College of Engineering
Chandana K L Department of Computer Application Rajarajeswari College of Engineering

DOI: https://doi.org/10.34293/sijash.v11iS1-July.6345

Keywords: Machine Learning, Pyspark, Crime Identification.

Abstract

The latest advances in online communities and modern technology have made it simpler than before for people to advertise new job openings. As a consequence, everyone will be alarmed. possibility of bogus job advertising. It is difficult to predict fake job smiling, same with many other category difficulties. According to this study, a classifier based on random forests It can be employed to determine the latter. a job advertisement is genuine. With 18000 data from the Jobs Scam Aegean Dataset (EMSCAD), this hypothesis was tested. The success rate for classification of the machine training technique is about 98% in detecting a fraudulent job posting.