Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)

Prediction Model Hadoop-based for High-risk Students

Authors
Jichao Yu, Xiaogao Yu
Corresponding Author
Xiaogao Yu
Available Online December 2019.
DOI
10.2991/mmsta-19.2019.7How to use a DOI?
Keywords
prediction model; high-risk students; big data; Hadoop platform
Abstract

In order to effectively predict high-risk students, this paper proposed a weighted voting combination prediction model based on Hadoop for high-risk students. Firstly, the design idea of the model was given, and the data of students were stored and processed by Hadoop platform. Secondly, according to the prediction method of students' characteristics and selection, a specific weighted voting combination prediction model Hadoop-based for high-risk students was constructed. Finally, the prediction model was evaluated and the rationality of the model was proved. The model can analyze the collected data of students and predicts the high-risk students in big data environment.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
Series
Advances in Computer Science Research
Publication Date
December 2019
ISBN
978-94-6252-856-7
ISSN
2352-538X
DOI
10.2991/mmsta-19.2019.7How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jichao Yu
AU  - Xiaogao Yu
PY  - 2019/12
DA  - 2019/12
TI  - Prediction Model Hadoop-based for High-risk Students
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 30
EP  - 33
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.7
DO  - 10.2991/mmsta-19.2019.7
ID  - Yu2019/12
ER  -