Student Campus Placement Prediction Analysis using ChiSquared Test on Machine Learning Algorithms

Abstract

Every higher education institute aims to provide the best career opportunities for their students as part of the outcome based education system. In India, campus placements for students while pursuing their 4th year of engineering is a predominant factor since the reputation of any institute largely depends on reputed recruiting companies visiting campus and the number of placement offers being given to eligible students. Hence, campuses offer personality development training to their students just before the commencement of the placement season while students try to maintain a minimum CGPA which would ensure their eligibility to apply for companies of their choice. The purpose of this paper is to predict a student’s chances of obtaining a pre-placement offer while still in campus on the basis of various academic and non-academic factors. The dataset used for the prediction analysis consists of student related aspects such as their university seat numbers, academic grades and personality training parameters. The training models have been designed using the WEKA tool and in addition to supervised machine learning classification algorithms, Chi-squared tests has been implemented on the dataset to only obtain those attributes that might be the highest requirement for campus placements of students.