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Conference object . 2021
License: CC BY
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Article . 2021
License: CC BY
Data sources: Datacite
ZENODO
Article . 2021
License: CC BY
Data sources: Datacite
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Student Placement Prediction Using Weka

Authors: Jaimi Santhosh; Navyamol K.T;

Student Placement Prediction Using Weka

Abstract

Nowadays, placement is one of the important activities in educational institutions. The key role that the educational institution is to prepare and guide students well through the placement process by providing them better training for getting placed in reputed companies. Best placement service of an institution will also attract people to take admissions. A placement prediction system helps to know the possibility of a student to be placed. Machine learning techniques like Naïve Bayes and Random forest algorithm are used for the prediction. These algorithms independently predict the results based on various parameters. This technique will also helps to upgrade the skills of students to meet the eligibility criteria

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Keywords

Naïve Bayes Algorithm, Machine learning, Prediction, Random forest

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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