Fake Job Detection using Machine Learning

Abstract

Nowadays, fake job posts are a growing problem since they attempt to tarnish the ex- pectations of the job seeker, who seeks the job for his own needs. Due to bad economic conditions, the job market is flooded with many people who lost their jobs. So there is an increase in people looking for a job. Fraudsters see this as an opportunity to make use of job seekers’ innocence and lack of knowledge. Fraudsters use fake job posts to lure desperate people looking for a job by giving them offers they can not refuse. Job seek- ers can get scammed because they do not have any kind of tool to check these fake job posts. The proposed system is a web-based application that employs a machine learning model, which is trained on a sizable data set utilizing actual data taken from three different author- tic sites, together with a data set from Kaggle, to determine if a job listing is fake or real. Next is is used to develop the front end of the application and python is used to implement machine learning algorithms. The user types content into the online application or the user can take an advertisement from job-finding websites such as Total, Glassdoor, and Indeed. Machine learning algorithms are used to assess the legitimacy of the job post