Input Analysis for Accreditation Prediction in Higher Education Sector by Using Gradient Boosting Algorithm

A.Deepa ¹ , E. Chandra Blessie²

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.3 , pp.23-27, Jun-2018

Online published on Jun 30, 2018

Copyright © A.Deepa, E. Chandra Blessie . 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.
 

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Abstract :
The main objective of this paper is to analyze the input criteria using Gradient Boosting Algorithm to predict the NBA accreditation strategy with generating solutions and suggestions to the institutions on that specific point. The NBA (The National board of accreditation) is formed by the prestigious AICTE (All India Council of Technical Education).The Aim of the council is to evaluate technical institutions periodically and inspecting programs basis according to specified norms and standards as per the council. Now a day’s colleges are feeling more prestigious to get the NBA accreditation. In order to get the accreditation college need to pass in various conditions like Overall Infrastructure, Academic Process, Result outcome and etc. These conditions have sub categories with points. The institutions need to meet out the allotted points to get a NBA Accreditation. This paper uses a Machine Learning Algorithm namely Gradient Boosting which can be used for the prediction of the status of the institutions and it considers the Input criteria and only one sub point within it that is the Student Intake Procedure. Before accreditation team’s arrival for inspection it checks some specific points in these criteria on a sample data and suggests the weak points and provides solutions for improvement.

Key-Words / Index Term :
NBA, NAAC, AICTE, Machine Learning, ANN, Gradient Boosting Algorithm

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