UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2107359


Registration ID:
312329

Page Number

c672-c681

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Title

A Real Time Application to Predict Student Placement Status

Abstract

Students placements are important at an educational institution, and they can affect the college's reputation. One of the most significant difficulties that higher education institutions confront today is improving student placement performance. When educational entities get more complex, placement prediction becomes more difficult. Educational institutions seek more efficient technology to aid in improved administration and decision-making procedures, as well as to help them develop new strategies. Providing new knowledge about innovative ideas to the management is one of the most effective approaches to solve the issues of enhancing quality. Knowledge may be derived from operational and historical data stored in the databases of educational organisations using machine learning techniques. The data set for system implementation includes student data from the past. These data are utilized to train the model for rule detection and classification, as well as to test the model. This method proposes a recommendation mechanism that forecasts the placement status of students. This strategy aids an organization's placement cell in identifying potential students, as well as paying attention to and improving their technical and interpersonal abilities. As a result, they will be able to put in more effort in order to get placed in higher-ranking companies. This method presents a suggestion mechanism for predicting students' placement status. For student placement prediction, data science techniques are used, and the system employs the most efficient algorithms, such as the "naive bayes algorithm" and the "KNN algorithm." Both algorithms are implemented in the C# programming language, and the results are compared, and the system determines which algorithm is the more efficient. Our system produced excellent results, with around 85% for the "naive bayes algorithm" and around 95% for the "KNN method." When compared to the naive bayes algorithm, the KNN approach produces better results.

Key Words

Machine learning,Naive Bayes,KNN,Data Science,Placements,Supervised Learning

Cite This Article

"A Real Time Application to Predict Student Placement Status", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.c672-c681, July-2021, Available :http://www.jetir.org/papers/JETIR2107359.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Real Time Application to Predict Student Placement Status", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppc672-c681, July-2021, Available at : http://www.jetir.org/papers/JETIR2107359.pdf

Publication Details

Published Paper ID: JETIR2107359
Registration ID: 312329
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: c672-c681
Country: Mysuru, Karnataka, India .
Area: Other
ISSN Number: 2349-5162


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