Abstract
Performance analysis of student-athletes has been a concern of many research studies. A number of factors including social, emotional, financial conditions are found to have adverse effect on academics and sport performances. Similarly, the academic stress and sports performance have been associated with various factors belonging to personality attributes, cognitive competencies, concentration level, socioeconomic background, locality, etc. However, these were hidden and no attempts were made to discover them. In the underlined research work, these aspects were discovered using data mining techniques. We have devised out our own dataset for the work from actual field data. Principal component analysis was implemented in SPSS platform for finding significant factors in our study.
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Bhalchandra, P., Muley, A., Sarode, R., Singh, S.K., Joshi, M., Wasnik, P. (2018). Discovering Significant Performing Variables of Athlete Students Through Data Mining Techniques. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_59
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DOI: https://doi.org/10.1007/978-981-10-7871-2_59
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