Predicting Job Eligibility for Fresh Graduate Engineers in Bangladesh Using Machine Learning

In Bangladesh, over 0.8 million students graduate each year, with more than 2 million individuals remaining unemployed, a significant proportion holding engineering degrees. Fresh engineering graduates often struggle to determine their suitability for specific job opportunities. In this paper, we propose a system leveraging machine learning techniques to predict job eligibility for fresh graduates based on their academic and personal backgrounds. By inputting job requirements and candidate profiles, the model predicts whether the graduate fulfills the criteria for the position. Among the models applied, the Decision Tree model achieved the highest accuracy at 93.5%, making it the best performer. Additionally, using a BERT-based model, we recommend the specific skills needed for candidates to better align with job requirements. This system not only assists fresh graduates in identifying suitable job opportunities but also helps employers streamline the hiring process. Ultimately, this predictive model contributes to more efficient job matching in Bangladesh’s labor market.