Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Reseach Article

Feature Selection for Performance Prediction using Decision Tree

by Anirudhd Soni, Anansha Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 17
Year of Publication: 2021
Authors: Anirudhd Soni, Anansha Gupta
10.5120/ijca2021921515

Anirudhd Soni, Anansha Gupta . Feature Selection for Performance Prediction using Decision Tree. International Journal of Computer Applications. 183, 17 ( Jul 2021), 25-29. DOI=10.5120/ijca2021921515

@article{ 10.5120/ijca2021921515,
author = { Anirudhd Soni, Anansha Gupta },
title = { Feature Selection for Performance Prediction using Decision Tree },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 17 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number17/32019-2021921515/ },
doi = { 10.5120/ijca2021921515 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:05.072174+05:30
%A Anirudhd Soni
%A Anansha Gupta
%T Feature Selection for Performance Prediction using Decision Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 17
%P 25-29
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the proliferation of digitalization from past decades, there has been exponential growth in data. The data is considered as the new oil. Useful insights can be extracted from the data and can be used for the growth of industry or organization, the branch of computer science that deals with the discovery of novel information and insightful pattern from the raw data is called Data Mining, it is used almost in every field from banking, healthcare to entertainment and surveillance. Here, specifically, the paper discusses about the data mining used in the field of education called Educational Data Mining (EDM), it is an inchoate data mining research area that aims to improve the students' performance, provide quality education, helps students to determine career choices, etc. This research paper aims to describe feature selection criterion and how combination of features like internal marks, mid-semester marks, etc. helps in determining students' performance using decision tree classification method. The results obtain can be used by instructors and teachers to plan structured approaches for the students performing low in their academics, need attention and counseling from their tutor guardians. Thus, with early prediction action can be taken within time to improve the result of students and the overall performance of an institution.

References
  1. Pearson, Karl (20 June 1895). "Notes on regression and inheritance in the case of two parents". Proceedings of the Royal Society of London. 58: 240–242.
  2. Galton, F. (1886). "Regression towards mediocrity in hereditary stature". Journal of the Anthropological Institute of Great Britain and Ireland.
  3. statistics.laerd.com/statistical-guides/pearson-correlation-coefficient-statistical-guide.php.
  4. Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
  5. Nidhi, Mukesh Kumar, NandiniNayar, Gaurav Mehta, “Student’s Academic Performance Prediction in Academic using Data Mining Techniques”. International Conference on Intelligent Communication and Computational Research.
  6. Breiman L (1984) Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability series. Chapman & Hall.
  7. AgungTriayudi, WahyuOktriWidyarto, Educational Data Mining Analysis Using Classification Techniques. Virtual Conference on Engineering, Science and Technology (ViCEST) 2020
  8. M. Ramaswami and R. Bhaskaran , A Study on Feature Selection Techniques in Educational Data Mining. JOURNAL OF COMPUTING, VOLUME 1, ISSUE 1, DECEMBER 2009.
  9. Amjad Abu Saa, Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications,Vol. 7, No. 5, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining Classification and Regression Trees Pearson Correlation Feature selection