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
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies