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<p>One of the major factors for evaluating the success of a professional course is calculating the percentage of student placement. Another significant aspect of placement is the high salary packages offered to the selected candidates. These two issues are the primary focus for many institutes seeking to attract students for admission. This presents a research challenge aimed at maximizing both the number of placements and the potential salary packages for each student. In this research, students are categorized based on their previous academic performance and skill sets using a decision tree model. The random forest algorithm is applied to each student cluster to predict their employability. Furthermore, a bipartite graph-based mapping technique, along with a modified Hungarian algorithm, is employed to match employable students with job opportunities that offer the highest possible salary packages. The novelty and advantages of this complete roadmap for the training and placement department are established through theoretical evidence, case studies based on real-life datasets, and comparative studies.</p>

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A Comprehensive Analysis on the Skill-Set of the Students to Improve Campus Drive Using Machine Learning Approaches

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Partha Ghosh,
  • Md Jakir Hossain Molla,
  • Leena Jana Ghosh,
  • Sk Md Obaidullah,
  • Jyotsna Kumar Mandal,
  • Soumya Sen

摘要

One of the major factors for evaluating the success of a professional course is calculating the percentage of student placement. Another significant aspect of placement is the high salary packages offered to the selected candidates. These two issues are the primary focus for many institutes seeking to attract students for admission. This presents a research challenge aimed at maximizing both the number of placements and the potential salary packages for each student. In this research, students are categorized based on their previous academic performance and skill sets using a decision tree model. The random forest algorithm is applied to each student cluster to predict their employability. Furthermore, a bipartite graph-based mapping technique, along with a modified Hungarian algorithm, is employed to match employable students with job opportunities that offer the highest possible salary packages. The novelty and advantages of this complete roadmap for the training and placement department are established through theoretical evidence, case studies based on real-life datasets, and comparative studies.