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@article{184640,
        author = {Pratiksha Warake},
        title = {Fake Job Detection in ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2924-2930},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184640},
        abstract = {The new research project has developed an integration of machine learning based tools for detecting online job platform fraud using the Random Forest and XGBoost algorithms. By utilizing a sophisticated multi-layered method that leverages natural language processing, feature engineering, and statistical analysis, the research demonstrates the ability to detect falsely posted job listings. The research also looks at together over 20 features drawn from five different categories of analysis: analysis of the job description text, analysis of the company profile, analysis of the requirements, analysis of location, and analysis of salary. Overall, the model performs well at detecting anomalous patterns in job listings including high rates of poor grammar, unrealistic salary ratings, and unverifiable profiles ip the company description. Ideally, the new system will include many other data cleansing operations, including preprocessing, outliers detection statistical methodologies, and removing imbalance in class population, so while fraud will be detected, the precision and recall rates of these methods will help ensure fraud is detected effectively. The methods employed introduced an ideal testing and statistical metrics to measure performance, making this a useful tool for improving the safety and security of online job platform.},
        keywords = {Fraud Detection, Feature Engineering, Predictive Analytics, Random Forest, XGBoost},
        month = {September},
        }