Enhancing Freshers Job Seeker Success at Bangladesh Job Fairs: A Machine Learning Approach to Predictive Analytics

Abstract

With over one million fresh graduates entering the job market in Bangladesh each year, employers often struggle to find technically capable individuals who meet industry needs, leading to a significant mismatch between available talent and job requirements. This paper proposes a machine learning-based solution to enhance the job search process for fresh graduates by analyzing their CVs and recommending the most suitable job opportunities. The system evaluates the efficiency of each job relative to the candidate’s qualifications, allowing job seekers to see how well a particular position aligns with their academic and professional background. By considering factors such as job preference and relevance, the system offers personalized job recommendations, improving the accuracy of matches. The approach not only streamlines the recruitment process, saving time for both employers and job seekers, but also addresses the challenge of finding the right fit in a competitive job market. Using ensemble algorithms, the system achieves a 94% accuracy rate in predicting the best job matches for candidates, significantly improving job placement success. Additionally, the system’s ability to analyze complete CVs ensures that job seekers receive the most relevant recommendations, helping to bridge the gap between the skills fresh graduates possess and the positions available in the market. This has the potential to make a substantial social impact by aligning talent with industry needs and supporting more efficient hiring practices across Bangladesh.