Abstract: With the emergence of new technologies, new academic trends introduced into Educational system which results in large data which is unregulated and it is also challenge for students to prefer to those academic courses which are helpful in their industrial training and increases their career prospects. Another challenge is to convert the unregulated data into structured and meaningful information there is need of Data Mining Tools. Hadoop Distributed File System is used to hold large amount of data. The Files are stored in a redundant fashion across multiple machines which ensure their endurance to failure and parallel applications. Knowledge extracted using Map Reduce will be helpful indecision making for students to determine courses chosen for industrial trainings. In this research, we are deriving preferable courses for pursuing training for students based on course combinations. Here, using HDFS, tasks run over Map Reduce and output is obtained after aggregation of results.

Keywords: Distributed File System, data mining, educational data mining, Hadoop, MapReduce.

PDF | DOI: 10.17148/IJARCCE.2020.9611

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