EXAMINING STUDENT PERFORMANCE BY USING MACHINE LEARNING (KEY IJP************798)

• Parvez Altaf Hunkuntee,Sangita Mhantappa Mali,Shivanjali Iranna Bhosagi,Kishor Shrimant Mane, Prof. U.s. Dhodmise

Abstract

To understand the student's rate of progress, it is crucial to forecast their performance. "Prevention is better than cure," goes the saying. The success of students can be significantly increased with early identification of at-risk students and preventive actions. The recommended task is utilized to assess a student's performance right now and forecast their outcomes in the future. Every year, many kids fall behind because of inadequate supervision and assistance from Staff. Based on the results, teachers can concentrate on the students who are more likely to receive lower grades in the final semester and can also help the student by identifying needs for the final exams. This project's major goal is to show how likely it is to train and model the dataset and how feasible it is to create a predictive model for student performance with a dependable accuracy rate by using linear regression algorithm .