Undergraduate engineering students employment prediction using hybrid approach in machine learning

Vinutha Krishnaiah (BMS Institute of Technology and Management)
Yogisha Hullukere Kadegowda (Ramaiah Institute of Technology, Visvesvaraya Technological University)

International Journal of Electrical and Computer Engineering
Vol 12, No 3: June 2022

The knowledge discovery from student’s data can be very useful in predicting the employment under different categories. The machine learning is helping in this regard up to the great extent. In this paper, a hybrid model of machine learning has proposed to predict the jobs categories, students may get in their campus placement. The considered groups of students are from undergraduate courses from engineering stream having the semester’s scheme in their academic. The mapping of jobs has predicted based on their previous seven semesters marks as well as their personality index. The proposed hybrid model consists of three different model based on multilayer feed forward architecture, radial basis function neural network and K-means based clustering method. The proposed model provided the relative chances of available each job category with high accuracy and consistency.