Real Time Sports Distribution Analysis Model for Improved Student Interest Prediction in Sports Mining

Abstract

Mining the interest of students in sports becomes more important task towards the development of sports around international standards. Numbers of techniques have been recommended earlier to predict the student’s interest on sports. The interest on sports has been predicted according to time spent and frequency of playing in earlier approaches, but suffers to produce higher results on prediction. To improve the performance, an efficient sports distribution analysis model (SDAM) is recommended in this article. The sports distribution analysis model identifies the list of sports interests of various students by analyzing the traces of student activities in academic and social environments.  The logs of sports activities are split into number of time window and for each of them, the method estimates the frequency of sports activity on each interest. According to these values, the method performs distribution analysis and measure sports distribution factors. Based on these values, the method performs interest prediction. The SDAM (Sports Distribution Analysis Model) achieve higher interest prediction performance with less false ratio.