Predicting Academic Course Preference using Hadoop
G. Divya Jyothi1, N. Shirisha2, P. Subhashini3, M. Anusha4
1G. Divya Jyothi *, Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, India.
2N. Shirisha*, Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, India.
3P. Subhashini, Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, India.
4M. Anusha, Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2527-2530 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7123118419/2019©BEIESP | DOI: 10.35940/ijrte.D7123.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.
Keywords: Big Data, Mapreduce.
Scope of the Article: Big Data Analytics Application Systems.