| Fake jobs are common in the job market. Every day, many people look for jobs through job portals. According to CNBC[1], the number of employment scams doubled in 2018 as compared to 2017. Fake jobs must be discerned from the vast selection of jobs to avoid wasting energy and money looking for a job that doesn’t exist. Most of these portals do not have a system that could check whether the job that the employer posts is real or fake. Several people have built prediction models to detect fake jobs from the information given. In the papers, different algorithms under machine learning are used, such as random forest, linear SVC, and XG boost. Various data pieces are being analyzed. In the data set, the most commonly seen information about jobs are job descriptions, job names, locations, and salary. Text information is wisely transferred to word frequency. A comparison between several classification model is conducted, and the best prediction model is successfully selected. |
