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Fake job post prediction through a comparative study on diverse data mining techniques

  • Unique Paper ID: 166582
  • Volume: 11
  • Issue: 2
  • PageNo: 1421-1429
  • Abstract:
  • In the contemporary digital landscape, the surge of deceptive job postings presents a substantial challenge for both job seekers and employers. This paper aims to tackle this pressing issue by developing sophisticated predictive models. The objective is to identify patterns distinguishing genuine job postings from fraudulent ones. By leveraging the Employment Scam Aegean Dataset (EMSCAD) from Kaggle, which includes both real and fake job postings, our study employs various data mining techniques and classification algorithms such as Decision Tree, Random Forest, Logistic Regression, K-NN, Support Vector Machine, Naïve Bayes, and Neural Network. Through rigorous testing and comparative analysis of various methodologies, the study seeks to establish the most effective approach for predicting and monitoring fraudulent job postings, thereby contributing to a safer and more reliable job market environment. Enhancing the precision of fraud detection not only safeguards job seekers but also fortifies the integrity of the job market.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 1421-1429

Fake job post prediction through a comparative study on diverse data mining techniques

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