World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , Academia , 

World Journal of Engineering Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

An Official Publication of Society for Advance Healthcare Research (Reg. No. : 01/01/01/31674/16)

ISSN 2454-695X

Impact Factor : 7.029

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • New Issue Published

    Its Our pleasure to inform you that, WJERT March 2024 Issue has been Published, Kindly check it on https://www.wjert.org/home/current_issues

  • WJERT New Impact Factor

    WJERT Impact Factor has been Increased to 7.029 for Year 2024.

  • WJERT: MARCH ISSUE PUBLISHED

    March 2024 Issue has been successfully launched on 1 March 2024.

Indexing

Abstract

IMPLEMENTATION AND ANALYSIS OF MACHINE LEARNING APPROACHES AND TECHNIQUES FOR STUDENT DROPOUT PREDICTION

*Manpreet Singh and Er. Harjeet Singh

ABSTRACT

The aversion of understudies dropping out is viewed as very significant in numerous instructive organizations. In this paper we portray the aftereffects of an instructive information examination contextual analysis concentrated on discovery of dropout of Systems Engineering (SE) college understudies following 6 years of enlistment in a Colombian college. Unique information is expanded and improved utilizing a component building process. Our test results demonstratedthat straightforward calculations accomplish dependable levels of exactness to recognize indicators of dropout. Data mining techniques outcomes were thought about in request to propose the best alternative. Additionally. Also, we present a few discoveries identified with information quality to improve the understudies information gathering process.

[Full Text Article] [Download Certificate]