An intelligent system to predict student performance and generate customized feedback using machine learning models. Designed to assist educators and institutions in evaluating student progress and guiding improvement.
- 📊 Predict student performance based on scores, attendance, and other inputs.
- ✍️ Generates smart feedback reports personalized for each student.
- 🧠 Supports multiple ML models (KNN, Random Forest, XGBoost, Logistic Regression).
- 📈 Comparative analysis of model accuracies.
- 🖼️ Includes an interactive Gradio GUI dashboard.
| Area | Tools |
|---|---|
| Language | Python |
| Data Handling | Pandas, NumPy |
| ML Models | Random Forest, XGBoost, KNN, Logistic Regression |
| GUI | Gradio |
| Visualization | Matplotlib, Seaborn |
| IDE | Google Colab |
| Deployment | GitHub (future-ready for Streamlit / Hugging Face) |
- ✅ Real-time performance prediction
- ✅ Graph-based feedback
- ✅ Accurate model selection
- ✅ Excel/CSV support for bulk input
- ✅ Clean, interactive dashboard