Predictive Analysis of Student Performance Using Machine Learning

Educational institutions collect vast amounts of student data, yet predictive insights remain underutilized. This research project focuses on predicting student academic performance using machine learning models.

Problem Statement

Early identification of at-risk students is challenging using traditional evaluation methods.

Research Objectives

The project aims to analyze academic, behavioral, and attendance data to predict performance and dropout risk.

Methodology

Data preprocessing is followed by feature selection and model training using classification and regression algorithms. Model performance is evaluated using accuracy and recall metrics.

Technologies Used

Python, data analysis libraries, visualization tools, and machine learning frameworks are employed.

Expected Outcomes

The system enables early intervention strategies and personalized academic support.

Conclusion

This project applies data science research to improve educational outcomes and decision-making.