Home|Journals|Articles by Year|Audio Abstracts
 

Research Article

EEO. 2020; 19(4): 8258-8267


Algorithms For Predicting Student Dropouts With Feature Selection

Ubhaida Aslam, Dr. Ravindra Kumar Gupta.




Abstract

Proof that a feature is applicable has become a prerequisite for using data mining calculations successfully in real-world contexts. In order to achieve the relevant feature subsets in the writing's order and grouping goals, numerous feature selection approaches have been offered. The concepts of feature pertinence, general strategies, assessment standards, and the qualities of feature selection are presented in this work. Last but not least, the chi square test will be used in feature selection calculations to predict school dropouts. This paper's goal is to find comparable instances of usage in the data compiled from datasets so that, in the end, we may make predictions for each student based on various segment, scholastic, and point-of-view features. In conclusion, information gleaned from the study may shed light on how to support students who are in risk even more effectively. We will wrap up this work with real-world applications (such early student dropout prediction), challenges, and potential directions for future study.

Key words: Feature Selection, Filter method, Student dropout, Data mining.






Full-text options


Share this Article


Online Article Submission
• ejmanager.com




ejPort - eJManager.com
Refer & Earn
JournalList
About BiblioMed
License Information
Terms & Conditions
Privacy Policy
Contact Us

The articles in Bibliomed are open access articles licensed under Creative Commons Attribution 4.0 International License (CC BY), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.