A Comparative Study on Fake Job Post Prediction Using Different Data Mining Techniques
Objective
The main objective of the project is to predict the fakes in job postings using machine learning algorithms.
Abstract
In recent years, due to advancement in modern technology and social communication, advertising new posts has become very common issue in the present world. So, fake posting prediction task is going to be a great concern for all. Like many other classification tasks, fake posting prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like XG Boost, Cat Boost, Gradient Boost, Random Forest and Decision Tree to predict a post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples performs great for this classification task. We used several machine learning (ML) algorithms to predict a fraudulent post.
Keywords: XG Boost classifier, CatBoost classifier, Gradient Boost classifier, Random Forest ,SVM and Decision Tree.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W Specifications:
- Processor : I5/Intel Processor
- RAM : 8GB (min)
- Hard Disk: 128 GB
S/W Specifications:
- Operating System: Windows 10
- Technology: Python 3.6+
- IDE : PyCharm,
- Data base : MySql
- Front end: HTML, CSS, JS
- Libraries Used: Numpy, IO, OS, Flask, pandas
Learning Outcomes
· About Classification in machine learning.
· About preprocessing techniques.
· About Decision Tree.
· About Random Forest.
· About Gradient Boosting.
· About Support Vector Machine (SVM).
· About Liner Regression.
· Knowledge on PyCharm Editor.
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
