Student Performance Predictor

This project aims to develop a machine learning model to predict students' academic performance (final grades) based on various factors such as age, gender, study habits, attendance, and more.

Technical Implementation

• Ensemble learning with feature importance analysis
• Handling of imbalanced data with SMOTE
• Model interpretation with SHAP values
• Multiple algorithm comparison and selection

Key Features

• Early identification of at-risk students
• Personalized intervention recommendations
• Class-level performance analytics
• Exportable reports for academic planning
• Interactive dashboard for educators