Smart Detection of Fake Job Post Using Albert + Xgboost Intelligence

The increase of false job adverts across the internet has resulted in a spike in employment fraud which greatly endangers ajob seeker’s finances as well as their identity. Many people fail to identify which job listings are authentic and which onesare fake, making them an easy target for fraudsters. This project proposes a solution in the form of a Fake Job PostDetection System which applies ALBERT for the extraction of deep contextual features from the job descriptions andXGBoost for categorizing the posts into real or fake. It is trained on a Kaggle dataset containing labeled job postings withdetails such as job title, company name, and description. The system takes user-inputted job details and identifies patternsassociated with fake job listings. ALBERT extracts linguistic features and XGBoost makes predictions based on theextracted features. To build trust Explainable AI (XAI) is incorporated wherein the user can comprehend the decisionmaking process. This system not only assists job seekers in spotting fraudulent job advertisements but also encourages asafer job-seeking experience