INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
A Comparative Study On Fake Job Prediction Using Different Machine Learning Technique
Authors Name:
A . Varshitha
, A. Likhitha , A. Supriya , T.Shakila
Unique Id:
IJSDR2304178
Published In:
Volume 8 Issue 4, April-2023
Abstract:
Over the years, because of the development of present day technologies and social communications, advertising new holidays has emerge as a completely common hassle in modern global. So the faux job reporting business can be a huge trouble for everybody. Like many different classification troubles, fake process prediction leaves many issues. In this newsletter, a Random Forest classifier is requested to predict whether or not a car process is genuine or fraudulent. We experimented with the Aegean Employment Scam Dataset (EMSCAD) containing 18,000 samples. The trained classifier showed approximately 98% class accuracy to predict the fraudulent vacancy
Keywords:
False Job Prediction, Semi Supervised Learning
Cite Article:
"A Comparative Study On Fake Job Prediction Using Different Machine Learning Technique", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1072 - 1075, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304178.pdf
Downloads:
000336256
Publication Details:
Published Paper ID: IJSDR2304178
Registration ID:205353
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1072 - 1075
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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