Prediction of Student Performance System using Machine Learning Techniques
J. Preethi1, S. Maheswari2
1Preethi J*, Computer Science and Engineering, National Engineering College, Tamil Nadu, India.
2Dr. S. Maheswari, Computer Science and Engineering, National Engineering College, Tamil Nadu, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 32-37 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3427049620/2020©BEIESP | DOI: 10.35940/ijitee.F3427.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Classification. Naïve Bayes. Placement. Prediction.
Scope of the Article: Regression and prediction