Publication Type:

Conference Paper

Source:

2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

ISBN:

9781509045594

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030232529&doi=10.1109%2fICACCS.2017.8014632&partnerID=40&md5=5da437ef7c0f81475e45dd5d4773f897

Keywords:

Artificial intelligence, Data mining, decision making, Decision making procedure, Decision tree classifiers, Decision trees, Education, Educational Institutes, Educational organizations, Forecasting, Higher learning institutions, Learning systems, Machine learning techniques, Personnel training, Python, Recommender systems, Sci-kit Learn, Students

Abstract:

One of the biggest challenges that higher learning institutions face today is to improve the placement performance of students. The placement prediction is more complex when the complexity of educational entities increase. Educational institutes look for more efficient technology that assist better management and support decision making procedures or assist them to set new strategies. One of the effective ways to address the challenges for improving the quality is to provide new knowledge related to the educational processes and entities to the managerial system. With the machine learning techniques the knowledge can be extracted from operational and historical data that resides within the educational organization's databases using. The dataset for system implementation contains information about past data of students. These data are used for training the model for rule identification and for testing the model for classification. This paper presents a recommendation system that predicts the students to have one of the five placement statuses, viz., Dream Company, Core Company, Mass Recruiters, Not Eligible and Not Interested in Placements. This model helps the placement cell within an organization to identify the prospective students and pay attention to and improve their technical as well as interpersonal skills. Furthermore, the students in pre-final and final years of their B. Tech course can also use this system to know their individual placement status that they are most likely to achieve. With this they can put in more hardwork for getting placed in to the companies that belong to higher hierarchies. © 2017 IEEE.

Notes:

cited By 0; Conference of 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017 ; Conference Date: 6 January 2017 Through 7 January 2017; Conference Code:130103

Cite this Research Publication

S. K. Thangavel, Bkaratki, P. D., and Sankar, A., “Student placement analyzer: A recommendation system using machine learning”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, 2017.

207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS