Abstract:
Unemployment is one of the critical challenges in Bangladesh, with an approximate
unemployment rate of 4.20%. However, over the past year, more than 8 lakh graduates
from public and private universities entered the job market. This research exposes the
machine learning and deep learning algorithms to address issues related to job success rate.
By employing ensembled learning-based algorithms the study achieves an impressive
accuracy rate of 95% that significantly outperforms traditional methods. This methodology
aims to help employers to identify their ideal employees and attract candidates who can be
a good fit for the industry. Therefore, we aspire to help institutions in redesigning their
curriculum that will help students to prepare for the market needs. This research holds
immense significance for the country, not only by addressing the employment gap but also
by enhancing the overall job market. Through the development of computational methods,
the study aims to improve the unemployment crisis by addressing problems like shortage
of skill, while accelerating the socioeconomic aspect of the country. The integration of
predictive analysis within the hiring process will benefit both companies and colleges in
the study-to-work transition. Moreover, deploying such system in Bangladesh has great
potential to overcome social and economic challenges, paving the way for a more
prosperous and stable future.
