Fake Job Post Prediction Using Data Mining
Shri Udayshankar B, Veeraj R Singh, P Sampras, Aryan Dhage
Abstract
The proliferation of online job boards is a testament to the ease with which new positions may be publicized in today's connected society. As a result, the problem of predicting fraudulent job postings will be of paramount importance. Predicting the outcome of a bogus job posting is a challenging classification assignment, similar to many others. In order to determine if a job posting is genuine or not, this study proposes using a variety of data mining methods and classification algorithms, including KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier, multilayer perceptron, and deep neural network. The Employment Scam Aegean Dataset (EMSCAD) was used for our experiments; it consists of 18000 data points. Using a deep neural network as a classifier yields excellent results in this setting. This classifier is a deep neural network with three thick layers. Classification accuracy (DNN) for identifying fake job postings is roughly 98% thanks to the trained classifier.