SVM kernel based predictive analytics on faculty performance evaluation

In recent years, higher education has been gaining importance in graduate students to make successful careers. So, academic organizations are given utmost importance for quality in academics to build the careers of the students. Faculty performance plays a vital role in academic institutions. In this paper, the performance of faculty members is evaluated on the basis of different parameters are taken for assessment and predicted by building models using data mining techniques. In this evaluation, the sample data is collected, preprocessed, and model learning is done using Support vector machines (SVM) with several kernel methods such as linear, sigmoid, radial basis, polynomial and Pearson VII function-based universal kernel (PUKF). The idea of this proposed paper is to investigate and analyze by considering various parameters for predicting the performance of faculty. The parameters considered are Faculty profile, Quality of Teaching, Maintaining Relationships, Learning Assessment, Counseling and Mentoring, Administrative Functions, Research and Development, Organizational Qualities and Outcome. Performance of various kernels is evaluated with the data and models with SVM-PUKF yields better accuracy by 97.84% when compared with other three standard kernels.