Author(s): Sonali Kashyap, Shikhar Bajaj, Jabanjalin Hida

Email(s): sonalikashyap11895@gmail.com

DOI: 10.5958/0974-360X.2017.00362.6   

Address: Sonali Kashyap, Shikhar Bajaj, Jabanjalin Hida
School of Computing Science and Engineering, Vellore Institute of Technology Vellore, India
*corresponding author

Published In:   Volume - 10,      Issue - 7,     Year - 2017


ABSTRACT:
Background and Objective: This project aims to analyze the addiction of student towards alcohol in early life and how that can affect their life. As it were Children who begin to drink by age 13 will probably go ahead to have more regrettable evaluations and most dire outcome imaginable, to be avoided from school. Materials and Methods: Our work intends to approach student addiction on alcohol in secondary level using Data Mining (DM) techniques. The concepts used are Classification and the algorithms of the data-mining and clustering to analyze the result. The outcome demonstrates that a decent prescient precision can be accomplished, given that dependence of liquor can affect to the understudy execution. Comes about: moreover, the outcome additionally gives the relationship between's liquor use and the social, sex and study time properties for every understudy. Conclusion: On the combination of two or more Classification algorithms decision tree method is more effective in predicting the performance of the student. Orange tool is used for the report and analysis of the project.


Cite this article:
Sonali Kashyap, Shikhar Bajaj, Jabanjalin Hida. Prediction of Student Academics Performance Based on their Previous Performance and Family Support. Research J. Pharm. and Tech. 2017; 10(7): 2075-2080. doi: 10.5958/0974-360X.2017.00362.6

Cite(Electronic):
Sonali Kashyap, Shikhar Bajaj, Jabanjalin Hida. Prediction of Student Academics Performance Based on their Previous Performance and Family Support. Research J. Pharm. and Tech. 2017; 10(7): 2075-2080. doi: 10.5958/0974-360X.2017.00362.6   Available on: https://rjptonline.org/AbstractView.aspx?PID=2017-10-7-13


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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