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In modern society, especially among beginners, education levels are rising in relation to employment experience levels. In the process of finding suitable jobs. They give precedence to some fake jobs and invest time in recruitment processes. So, to discover the fake recruitment, we created the project we were working on. We employ machine learning methodologies that employ classification techniques to detect such bogus recruiting detection processes. The research presents an application that uses machine learning-based categorization algorithms to prevent fraudulent job posts online. To establish the best job fraud detection model, the outputs of various classifiers are compared. These classifiers are used to detect fake web posts. It aids in the detection of bogus job ads among a huge number of postings. Two types of classifiers are used to detect bogus job ads: single classifiers and ensemble classifiers. However, experimental results demonstrate that ensemble classifiers outperform single classifiers in terms of detecting fraud.

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Identification of Fake Job Recruitment Using Several Machine Learning (ML) Models

  • 会议论文

  • 发表日期：

    2025年

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  • Immidi Kali Pradeep,
  • Seerla Mohan Krishna,
  • Kompella Bhargava Kiran,
  • B. C. H. S. N. L. S. Sai Baba,
  • Venkata Durgarao Matta

摘要

In modern society, especially among beginners, education levels are rising in relation to employment experience levels. In the process of finding suitable jobs. They give precedence to some fake jobs and invest time in recruitment processes. So, to discover the fake recruitment, we created the project we were working on. We employ machine learning methodologies that employ classification techniques to detect such bogus recruiting detection processes. The research presents an application that uses machine learning-based categorization algorithms to prevent fraudulent job posts online. To establish the best job fraud detection model, the outputs of various classifiers are compared. These classifiers are used to detect fake web posts. It aids in the detection of bogus job ads among a huge number of postings. Two types of classifiers are used to detect bogus job ads: single classifiers and ensemble classifiers. However, experimental results demonstrate that ensemble classifiers outperform single classifiers in terms of detecting fraud.