Student Feedback Analysis Using Machine Learning for Performance Insights

This research uses machine learning to assess educational achievement and analyze student feedback. To evaluate feedback scores and identify important areas for development, sentiment analysis and text classification methods in particular, Support Vector Machines (SVM) and Naive Bayes are employed. By automating feedback classification, the system produces data insights that are visualized and can help teachers improve the effectiveness of their lessons and curriculum design. The model can offer practical suggestions for meeting the requirements of students by examining sentiment trends. This technology can also adjust to continuous enhancements in academic performance through real-time feedback collection, facilitating a data-driven approach to student-centered learning.