Student Marks Prediction Using Linear Regresion
DOI:
https://doi.org/10.29070/2s1xp761Keywords:
Classification Prediction, Machine Learning, Data Cleaning, Data Processing, Linear RegressionAbstract
This study examines how machine learning applications affect teaching and learning in higher education, as well as how to improve the learning environment. Students' interest in online and digital courses grew significantly, and websites like Course Era, Udemy, and others became increasingly important. We use innovative machine learning applications in teaching and learning while taking into account the students' background, previous academic performance, and other factors. Because of the enormous class numbers, it would be impossible to support each individual student in each open learning course, which could raise the dropout rate at the conclusion. In this study, we use linear regression, a machine learning algorithm, to predict outcomes.Downloads
References
Hadzic and D. R. Morgan (2009). "On packet selection criteria for clock recovery," Proceedings of the National Academy of Sciences, vol. IEEE Int. Symp. Precision Clock Synchronization Meas. Contr. Commun.
C. S. Turner (2008). “Slope filtering: an FIR approach to linear regression", IEEE Signal Process. Mag., vol. 25, pp. 159-163.
D. Veitch, J. Ridoux and S. B. (2009). “Robust synchronization of absolute and difference clocks over networks," by Korada in IEEE/ACM Trans. Networking, vol. 17, pp. 417-430.
D. R. Morgan and I. Hadžić: Non-uniform linear regression with block wise sample-minimum preprocessing", IEEE Trans. Signal Process
Ankitha A Nichat, Dr. Anjali B Raut (2017). “Predicting and Analysis of student Performance Using Decision Tree Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.5, Issue 4.
S.A. Oloruntoba, J.L. Akinode (2017). “Student Academic Performance Prediction Using Support Vector Machine”.
Dhanashree Mane, Pranali Namdas, Pooja Gargade, Dnyaneshwari Jagtap, S.S. Rathi (2018). Predicting student Performance Using Machine Learning Approach”. VJER Vishwakarma Journal of Engineering Research, Volume 2 Issue 4.