Unmasking Deception: A Proactive Approach to  Detecting and Preventing Fake Job Offers

Author(s)	Lembhe Akshata, Sapkal Dipraj, Khairnar Gaurav
Country	India
Abstract	The research presents an innovative approach to combating fraudulent job postings on the internet through the utilization of machine learning-based classification techniques. By leveraging different classifiers, including single classifiers and ensemble classifiers, the study aims to discern fake job postings amidst a vast array of online listings. Through an extensive analysis of experimental results, the research identifies ensemble classifiers as the optimal choice for detecting employment scams, surpassing the efficacy of single classifiers. Employing a dataset sourced from Kaggle, the study focuses on distinguishing between real and fake job postings, with the latter comprising a minority of the dataset, as anticipated. By adhering to these structured stages, the research aims to contribute to the advancement of methods for identifying and mitigating fraudulent activities in online job postings, thereby enhancing the integrity of online recruitment processes.
Keywords	Attrition, Classifier, algorithms
Field	Mathematics > Statistics
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-29
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.14344
Short DOI	https://doi.org/gtktb5

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International Journal For Multidisciplinary Research