Student Performance Prediction Using Machine Learning

Abstract

In the era of data-driven decision-making, educational institutions leverage technology to predict student performance effectively. This paper presents a comprehensive study on existing literature in student performance analysis and proposes a detailed design for an automated system using Django, Pandas, and Machine Learning. Student academic performance prediction is a crucial task in the education sector, enabling early identification of students who may require additional support. This study leverages machine learning techniques to analyze various factors affecting student performance, such as academic records, attendance, behavioural traits, and socioeconomic status. The system employs the Support Vector Machine (SVM) algorithm for classification, predicting students' performance levels as High, Medium, or Low