ABSTRACT
The process of searching jobs is one of the most problematic issue freshers face, this process is used by various scamsters to lure freshers into scams and profit from the students. In order to avoid this, this paper proposes a system with deep learning and flask for front-end, that can identify fake jobs. The deep learning algorithm extracts specific features from the website’s article and based on those features predicts if the job is genuine or not. The proposed system makes use of a deep learning based system and a web page to help non-technical users to analyze these fake scams and secure their jobs .

While browsing for jobs online we saw that many scamsters demanded money for booking slots to interviews that did not exist and also extort money from students with promise of giving them jobs in return, this served as motivation for this proposal.The objectives that are to be considered are: Prediction of real or fake job. And a front-end page to allow non-technical user to use the model
The proposed system is basically an ANN classification model based on Multinomial Naive Bayes algorithm to determine fake job posting or real one. The model is trained to be as efficient as possible by making the dataset to be a part of double-blind study and also considering the various formats of posting jobs in professional websites and other sites too. This therefore makes searching of jobs much more efficient and also allows the users to be worry free when they search for jobs online.

Keywords: Jobs, Deep learning, ANN

| DOI: 10.17148/IARJSET.2021.8857