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Exploratory Data Analysis for Predicting Student’s Grades

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1381))

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Abstract

Over the period, the academic level of the students has improved. However, it is found in many research studies that students find it difficult in getting success in some core courses like Mathematics and Reasoning. The recent development in machine learning techniques and various data mining tools has made it possible to extract useful information from the available raw data. This research paper analyzes the student performance dataset available on the University of California, Irvine (UCI) Machine Learning Repository. The student’s grades are predicted using various machine learning techniques using Python programming in Jupyter Notebook.

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Acknowledgements

I wish to record our deep sense of gratitude and profound thanks to Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia for inspiring guidance and constant encouragement with the work during all stages to bring this research paper into fruition.

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Authors and Affiliations

  1. College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Kingdom of Saudi Arabia

    Kailash Kumar

Authors
  1. Kailash Kumar
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Correspondence to Kailash Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Shobhit University Gangoh, Gangoh, Uttar Pradesh, India

    Tarun K. Sharma

  2. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Chang Wook Ahn

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Kumar, K. (2021). Exploratory Data Analysis for Predicting Student’s Grades. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-1696-9_33

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