Prediction of Admissions and Jobs in Technical Courses with Respect to Demographic Location Using Multi-linear Regression Model

Abstract

With more employment opportunities, we have seen tremendous growth in number of institutions which offer professional courses across globe. One side, there are very well-established institutes where infrastructure, faculty profile and teaching facilities have established a niche, while on the other side, new institutes, continuously looking for recognition from government bodies on the basis of infrastructure only. Studies published on various online and offline surveys show that with increased number of institutes offering professional courses like engineering, the employment potential has come down. This paper describes a multiple linear regression-based model which can predict employment potential of different courses according to a specific region. The model includes the job employment potential as a contributing parameter to approve government recognition to such institutes. The proposed work can help in balancing the number of institutes at a location which are more promising in providing jobs to engineering graduates. Our work predicts employment potential rate of each course. The number of admissions to an institute and number of students getting an employment in campus have been considered as two parameters. These values are used to provide the employment potential for each course. If the predicted value of employment potential of the requested course lies within the range of the value of employment potential at that specific location, then course can be considered for approval. We have observed that our proposed work predicts the employment potential with an accuracy of around 92%.