Abstract
Data mining (DM) is used to analyze and classify data and identify hidden patterns stored in a data warehouse in an attempt to predict future trends, which are quintessential to knowledge discovery and provide tremendous support not only to the world of business but also to that of academia. There are various open-source and freely available software tools such as Weka, R, and Orange as well as programming languages like Python used for DM. This study focuses on comparing the performance of these tools by performing Naïve Bayes classification on student placement data. Percentage of marks scored by students in S.S.C. and H.S.C. examinations and their engineering aggregate were inputs to the tools. Moreover, the tools were trained and tested to decide whether a student would be placed or not. Comparative analyses of the tools were done to determine which tool was able to provide the highest prediction accuracy on student placement data.
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Mavani, U., Lobo, V.B., Pednekar, A., Pereira, N.C., Mishra, R., Ansari, N. (2020). Naïve Bayes Classification on Student Placement Data: A Comparative Study of Data Mining Tools. In: Tuba, M., Akashe, S., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Advances in Intelligent Systems and Computing, vol 933. Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_35
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DOI: https://doi.org/10.1007/978-981-13-7166-0_35
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