Open AccessPrediction of Student Performance System using Machine Learning Techniques
Author(s) -
Preethi J*,
S. Uma Maheswari
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3427.049620
Subject(s) - arrears , naive bayes classifier , student achievement , upgrade , computer science , educational data mining , machine learning , academic achievement , data pre processing , mathematics education , artificial intelligence , data mining , data science , support vector machine , world wide web , psychology , payment , operating system
Educational organizations are unique and play the utmost significant role in the development of any country. In the Educational database, due to the enormous volume of data for predicting student's achievement becomes more complicated. To upgrade a student's performance and triumph is more efficient in a practical way using Educational Data Mining Techniques. Data Mining Techniques could deliver favor and brunt to educators and academic institutions. The student's data ((i.e.) Name,10th %,12th cut off, CGPA, No of arrears, etc.) are gathered. Then, the datasets are imported into the Anaconda Navigator. Then, analysis and classification based on attributes of the students and the schemes are performed. Then using the prediction algorithm Naïve Bayes what are all the features the particular student is eligible for are predicted as placed. The student's input that has disparate data about their past and present academics report and then apply the Naïve Bayes algorithm using Anaconda Navigator to search the student's achievement for placement. A proposed methodology based on a classification approach to finding an improved estimation method for predicting the placement for students. This project can find the association for academic achievement of each particular student and their placement achievement in campus selection.