Abstract:
Now a days it becomes extremely difficult for one to stay safe online. Number of victims of fake
job posting is increasing drastically day by day. Identify the fraudulent job advertisement manually
is very difficult. To avoid fraudulent post for job in the internet, an automated tool using machine
learning based classification techniques is proposed in the project. This study research attempts to
prohibit privacy and loss of money for individuals and organization by creating a reliable model
which can detect the fraud exposure in the online recruitment environments. This research presents
a major contribution represented in a reliable detection model using ensemble approach based on
Random forest classifier to detect Online Recruitment Fraud (ORF). ORF detection is an important
problem to solve but it has not received much attention from the research community and it is
currently a relatively unexplored area. Detection of fraud job offers from a legitimate set of job is a
technically challenging problem. The main challenge the class imbalance problem as the number of
fraud jobs are relatively less as compared to the legitimate jobs. This makes learning the features of
fraud jobs for automated prediction a challenging task. These fraudulent job post detection draws a
good attention for obtaining an automated tool for identifying fake jobs and reporting them to people
for avoiding application for such jobs.
Different classifiers are used for checking fraudulent post in the web and the results of those
classifiers are compared for identifying the best employment scam detection model. It helps in
detecting fake job posts from an enormous number of posts. Random Forest classifier can be
regarded as the best model for this fake job detection scheme. Random forests creates decision trees
on randomly selected data samples, gets prediction from each tree and selects the best solution by
means of voting. It also provides a pretty good indicator of the feature importance. The proposed
model achieved an accuracy 95%.
