Article
Deceptive Recruitment Detection using Machine Learning
The proposed research project presents an integrated machine learning framework for detecting fraudulent activities on online job platforms using Random Forest and XGBoost algorithms. The system applies a comprehensive multi-layered approach that combines natural language processing, feature engineering, and statistical analysis to identify deceptive or misleading job postings. The study analyzes more than twenty distinct features extracted from five major categories, including evaluation of job description text, examination of company profiles, analysis of job requirements, verification of location details, and assessment of salary information.The developed model effectively detects suspicious patterns commonly associated with fraudulent job listings, such as poor grammatical structure, unrealistic salary offers, and unverifiable company information. To enhance detection performance, the system incorporates several data preparation techniques, including data preprocessing, outlier detection methods, statistical validation techniques, and handling class imbalance in the dataset. Furthermore, the research evaluates the model using appropriate testing strategies and statistical performance metrics, including measures such as precision, recall, and overall accuracy. The results demonstrate that the proposed framework provides a practical and efficient solution for identifying fraudulent job postings. By improving the reliability of job listings, this system can contribute to enhancing the security, transparency, and trustworthiness of online employment platforms.
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