doi: 10.18178/ijiet.2019.9.10.1291
Prediction of Teacher Enrollment for Pakistani Schools by Using SVM
Samina Kausar1
,
Xu Huahu1 ,
Muhammad Shahid Iqbal2 ,
Xiangmeng Wang1 ,
Muhammad Yasir Shabir3 ,
Tamoor Khan4
- 1School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
- 2School of Computer Science and Technology, Anhui University, Hefei, China
- 3Department of Computer science and Information Technology, University of Kotli, AJ & K, Pakistan
- 4School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, People’s Republic of China
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.
Keywords
- Enrollment polices
- future prediction
- school teachers
- and teacher enrollment
How to 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. https://doi.org/10.18178/ijiet.2019.9.10.1291
Copyright & License
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).