Predicting Online Job Recruitment Fraudulent Using Machine Learning

Employing individuals via the Internet has been a boon for businesses in the modern day. It is much simpler and more convenient than traditional recruitment methods. However, several scammers are abusing this platform, which may result in financial and privacy loss for job seekers and damage to the reputable organisation's name. In this research, we proposed a technique for detecting Online Recruitment Fraud (ORF). This model uses a publicly available dataset containing 17,780 job postings. We apply the four classification models to determine which classification model performs best for our suggested model. In this model, we use decision trees, random forests, Naive Bayes and logistic regression methods. We have estimated and evaluated the accuracy of several prediction systems. The random forest classifier provides the greatest accuracy, 97.16%, on our dataset. We have endeavoured to develop a method for detecting bogus recruiting postings.