Fake Job Post Prediction Using Machine Learning Algorithms

Fake Job Post Prediction Using Machine Learning Algorithms
Author(s):
Gulshan P., Mukund T., Ajay A., Pankaj Kumar, Aruna M G, Dr. Malatesh S H
Keywords:
Random Forest, KNN, Naive Bayes, Real and Fake, support vector machine, deep learning, and classification.
Abstract
During the pandemic, there is strong rise in the number of online job posted on various job portals. So, fake job posting prediction task is going to be big problems for all. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. . This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on EMSCAD which containing 18000 employee samples. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.
Article Details
Unique Paper ID: 156281 Publication Volume & Issue: Volume 9, Issue 3 Page(s): 286 - 290
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