Predicting Fraudulant Job Ads with Machine Learning

Abstract

Online Recruitment frauds are becoming an important issue in cyber-crime region. Companies find it easier to hire people with the help of the internet rather than the old traditional way. But it has greatly attracted scammers. In this project, we have proposed a solution on how to detect ORF. We have presented our results based on the previous model and the methodologies, to create the ORF detection model where we have used Jobs_Frauds.csv . We have selected this dataset from Kaggle . Furthermore, Dummy Classifier, Random Forest Classifier, Support Vector Machine, Gradient Boosting, Naïve Bayes Classifiers, XG Boost, SGD classifier, Passive Aggressive and KNN are the algorithms that have been used. We have found the accuracy of different prediction models, where Passive Aggressive (98.12%) and Gaussian Naïve Bayes (96.72%) give the highest accuracy. Through this project, we tried to create a precise way for detecting fraudulent hiring posts.