Prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques

  • T. Sasikala
  • M. RajeshEmail author
  • B. Sreevidya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)


Alcohol consumption by students has become a serious issue nowadays. Addiction to alcohol leads to the poor academic performance of students. This paper describes few algorithms that help to improve the efficiency of academic performance of students addicted to alcohol. In the paper, we are using one of the popular Data Mining technique—“Prediction” and finding out the best algorithm among other algorithms. Our project is to analyze the academic excellence of the college professionals by making use of WEKA toolkit and R Studio. We implement this project by making use of alcohol consumption by student datasets provided by kaggle website. It is composed of 395 tuples and 33 attributes. A classification model is built by making use of Naïve Bayes and ID3. Comparison of accuracy is done between R and WEKA. The prediction is performed in order to find out whether a student can be promoted or demoted in the next academic year when previous year marks are considered.


Data mining Prediction Naïve Bayes ID3 WEKA R studio Confusion matrix 


  1. 1.
    Al-Radaideh, Q., Al-Shawakfa, E., Al-Najjar, M.I.: Mining student data using decision trees. The Int. Arab J. Inf. Technol.—IAJIT (2006)Google Scholar
  2. 2.
    Devasia, T., Vinushree T.P., Hegde, V.: Prediction of students performance using educational data mining. In: International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 91–95 (2016)Google Scholar
  3. 3.
    Asif, R., Hina, S., Haque, S.I.: Predicting student academic performance using data mining methods. Int J Comput. Sci. Netw. Secur. (IJCSNS) 17(5), 187–191 (2017)Google Scholar
  4. 4.
    Ramesh, V., Parkavi, P., Yasodha, P.: Performance analysis of data mining techniques for placement chance prediction. Int. J. Sci. Eng. Res. 2, 2229–5518 (2011)Google Scholar
  5. 5.
    Krishna, K.S., Sasikala T.: Prognostication of students performance and suggesting suitable learning style for under performing students. In: International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS—2018), December 2018Google Scholar
  6. 6.
    Fabio, M.P., Roberto, M.O., Ubaldo, M.P., Jorge, D.M., Alexis, D.L.H.M., Harold, C.N.: Designing A Method for Alcohol Consumption Prediction Based on Clustering and Support Vector Machines. Res. J. Appl. Sci., Eng. Technol. 14, 146–154CrossRefGoogle Scholar
  7. 7.
    Sreevidya B., Rajesh M., Sasikala T.: Performance analysis of various anonymization techniques for privacy preservation of sensitive data. In: Hemanth J., Fernando X., Lafata P., Baig Z. (Eds.) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer (2019)Google Scholar
  8. 8.
    Krishnaiah, V., Narsimha, G., Subhash Chandra, N.: Diagnosis of lung cancer prediction system using data mining classification techniques. Int. J. Comput. Sci. Inf. Technol. 4, 39–45 (2013)Google Scholar
  9. 9.
    Shelke, N.: A survey of data mining approaches in performance analysis and evaluation. Int. J. Adv. Res. Comput. Sci. Softw. Eng. (2015) Google Scholar
  10. 10.
    Jyothi, J.K. Venkatalakshmi, K.: Intellectual performance analysis of students by using data mining techniques. Int. J. Innov. Res. Sci. Eng. Technol. 3, (2014)Google Scholar
  11. 11.
    Sreevidya, B.: An enhanced and productive technique for privacy preserving mining of association rules from horizontal distributed database. Int. J. Appl. Eng. Res. (2015)Google Scholar
  12. 12.
    Bhise, R.: Importance of data mining in higher education system. IOSR J. Hum. Soc. Sci. 6, 18–21 (2013)Google Scholar
  13. 13.
    Sumitha Thankachan, Suchithra, Data mining warehousing algorithms and its application in medical science. IJCSMC, 6 (2010) Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of EngineeringBengaluruIndia
  2. 2.Amrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations