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Prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques

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Book cover Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1040))

Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India

    T. Sasikala, M. Rajesh & B. Sreevidya

  2. Amrita Vishwa Vidyapeetham, Coimbatore, India

    T. Sasikala, M. Rajesh & B. Sreevidya

Authors
  1. T. Sasikala
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  2. M. Rajesh
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  3. B. Sreevidya
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Corresponding author

Correspondence to M. Rajesh .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Division of Information and Communication, Baekseok University, Cheonan-si, Ch’ungch’ong-namdo, Korea (Republic of)

    Gyoo-Soo Chae

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Sasikala, T., Rajesh, M., Sreevidya, B. (2020). Prediction of Academic Performance of Alcoholic Students Using Data Mining Techniques. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_14

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