A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques | Python

A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques | Python IEEE Final Year Project.
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📌Project Title: A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques.
💡Implementation Code: Python.
🔬Algorithm / Model Used: Random Forest Classifier.
🔬Algorithm / Model used for Text processing Prediction: K-Neighbors Classifier.
🔬Dataset used: Employment Scam Aegean Dataset (EMSCAD).
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/.
IEEE Base paper Abstract:
In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naïve 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 Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. 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.
REFERENCE:
Sultana Umme Habiba, Md. Khairul Islam, Farzana Tasnim, “A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques”, IEEE Conference, 2021.
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[@satyasree9419]

Which libraries used in this project

[@jpinfotechprojects]

pandas, numpy, scikit-learn

[@SudheerbabuKoppoku-ix3kj]

I need this project using django framework instead of flask

[@jpinfotechprojects]

Sorry it's not available with us in Django