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Mohana Bangale


SVM; KNN; placement prediction; classification; Naive Bayes; Machine Learning; Python.


Predicting the performance of a student is a nice concern to the upper education institutions. The purpose of placement management system is to modify the present manual system by the assistance of computerized software system fulfilling their needs, so their valuable data/information is stored for a longer time with simple accessing and manipulation of data. Student’s academic achievements and their placement in campus selection is a difficult issue in current manual system. Monitoring the student’s progress for his or her campus placement helps in monitoring the student’s progression within the academic surroundings. the aim of organizations is to supply superior opportunities to their students. This proposed student prediction system is most important approach which can be used to differentiate the student data/information on the basis of the student performance. Managing placement and coaching records in any larger organization is quite tough because of the large number of students. This system can classify the student knowledge with ease and can be useful to several educational organizations. There are several classification algorithms and mathematics-based techniques which can be taken nearly as good assets for classifying the students’ information set in the education field. In Our system, Naïve Bayes, SVM, KNN algorithm is applied to predict student performance which can facilitate to identify performance of students and also provides suggestion to improve performance for students such as we are going to classify the student's knowledge set for placement and non-placement classes based on that result, education organizations can give superior training to their students. Based on data received by system, student’s performance is analysed in numerous views to check the achievements of the students through their activities and suggests improvement for better placement.

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Published in: Volume : 5, Issue : 2
Publication Date: 2/1/2019

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