Fake Job Prediction using Sequential Network

Abstract

With increased number of data and privacy breaches day-by-day it becomes extremely difficult for one to stay safe online. Number of victims of fake job posting is increasing drastically day by day. The companies and fraudsters lure the job-seekers by various methods, majority coming from digital job-providing web sites. We target to minimize the number of such frauds by using Machine Learning to predict the chances of a job being fake so that the candidate can stay alert and take informed decisions, if required. The model will use NLP to analyze the sentiments and pattern in the job posting. The model will be trained as a Sequential Neural Network and using very popular GloVe algorithm. To understand the accuracy in real world, we will use trained model to predict jobs posted on Linked In. Then we worked on improving the model through various methods to make it robust and realistic.