• Ms. G. Swathi
• Mr. V. Ravikanth

It looks into opinion mining using supervised learning approaches to uncover the sentiment behind the student feedback that supports and characterizes teaching and learning decisions. The research team used a combination of AI and human language processing tools on student input data gathered from module investigation overview results at VR Siddhartha Engineering College in Vijayawada. The work also offers a comparative overall performance look at remarks supported, extracted alternatives like examination, teaching, and so on. This is done in order to provide a grade-by-grade explanation of the method of success of opinion mining on or after student comments using the open-source tool Python. Comparing the results to seeking for superior overall performance with regard to many evaluation criteria specified for the Any institution among humanity is managing a vast amount of information. Volume, velocity, and selection—the three Vs—are frequently used to describe this "Big Data" The three dimensions are typically more important to examine as a whole than any one of them alone. Due to the abundance of structured records—such as grades, conscription data, and development quotes—as well as unstructured records—such as student opinions expressed in surveys—it would take too much time and effort to manually summarize the facts in order to draw conclusions and make decisions.

Performance Analysis, Support Vector Machines, Predictive analytics, Machine Learning (ML).

" Machine Learning Based Student Performance Prediction using Feedback Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.h741-h745, July-2023, Available :http://www.jetir.org/papers/JETIR2307789.pdf

" Machine Learning Based Student Performance Prediction using Feedback Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. pph741-h745, July-2023, Available at : http://www.jetir.org/papers/JETIR2307789.pdf