Abstract—Civilization distinguishes human being from other
creatures. Civilization is adorned with education. Education
uplifts not only the standard but also the historical records of
nations. Teachers, students, public, government and curriculum
are the main components of education. This study analyzes the
number of school teachers and predicts the future annual
enrollment of teachers using the SVM model (support vector
machine). Time series data of 46 years (1971-2017) have been
taken from the Handbook of Statistics on Pakistan Economy.
Our results depict that school teachers must be further enrolled
at all levels. Our method leads to good precision.
Index Terms—Enrollment polices, future prediction, school
teachers, and teacher enrollment.
Samina Kausar, Xu Huahu, and Xiangmeng Wang are with the School of
Computer Engineering and Science, Shanghai University, Shanghai, 200444,
China (e-mail: saminamalik7@yahoo.com, huahuxu@shu.edu.cn,
xiangm_wang@163.com).
Muhammad Shahid Iqbal is with the School of Computer Science and
Technology, Anhui University, Hefei, China (e-mail:
nawabishahid@yahoo.com).
Muhammad Yasir Shabir is with the Department of Computer science
and Information Technology, University of Kotli, AJ & K, Pakistan (e-mail:
yasir.shabir14@gmail.com).
Tamoor Khan is with the School of Economic Information Engineering,
Southwestern University of Finance and Economics, Chengdu, People’s
Republic of China (e-mail: tamoorkhan525.iiui@yahoo.com).
Cite: Samina Kausar, Xu Huahu, Muhammad Shahid Iqbal, Xiangmeng Wang, Muhammad Yasir Shabir, and Tamoor Khan, "Prediction of Teacher Enrollment for Pakistani Schools by Using SVM," International Journal of Information and Education Technology vol. 9, no. 10, pp. 710-714, 2019.
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).