Anticipating Placement Status of Engineering Students using Machine Learning based Prediction Models - A Case Study of Computer Science Stream

Quality of placements is an important parameter considered by accreditation bodies while ranking Universities or Institutes of Higher Education. Thanks to the increasing automation of placement grooming related activities, universities have started exploring using intelligent computing techniques to anticipate placement status of upcoming batches and identifying factors that play an important role in improving placement quality. In this work, machine learning techniques have been used to predict placement students of engineering students of computer science discipline. Logistic Regression and Decision Tree algorithms gave best prediction performance. Marks in ‘Xth’, ‘XIIth’, ‘Current CGPA’ and ‘Present Attendance’ were among the important factors affecting placement status of students.