Machine Learning Predication Techniques for Student Placement Job Role Predictions

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dc.contributor.authorMeher, Kunal
dc.date.accessioned2025-05-20T05:52:30Z
dc.date.available2025-05-20T05:52:30Z
dc.date.issued2024
dc.descriptionuGDX
dc.description.abstractPlacements is of endless importance for university students and educational institutions. It helps students build a solid foundation for their future careers and ensure a good enrollment record that gives them a good advantage in school or university. Machine learning is an analytical method that establishes analysis patterns. This article describes the features of the machine that can predict whether a student will see or not, as currently it only depends on the student's qualification, age and experience. The predictor uses three machine learning algorithms: Decision Tree, Naive Bayes, and Random Forest are used. The algorithms are then assessed according to the accuracy of the predictions.
dc.identifier.issn1660-6795
dc.identifier.urihttps://atlasuniversitylibraryir.in/handle/123456789/755
dc.language.isoen
dc.publisherNanotechnology Perceptions
dc.subjectRandom forest
dc.subjectnaive Bayes
dc.subjectdecision trees
dc.subjectmachine learning
dc.subjectclassification
dc.subjectmodel estimation
dc.subjectand data analysis
dc.titleMachine Learning Predication Techniques for Student Placement Job Role Predictions
dc.typeArticle

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