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Predictive ML ApplicationCompleted
Student Score Predictor
A machine-learning application for predicting academic performance and explaining model predictions.
- Python
- Pandas
- Scikit-learn
- Streamlit
- SHAP
Overview
A machine-learning application for predicting academic performance and explaining model predictions.
Key Features
- Student input form
- Regression-based prediction
- Multiple ML models
- Grade/performance interpretation
- SHAP explanations
- Recommendations
- Streamlit interface
Technology / Architecture
- Python
- Pandas
- Scikit-learn
- Streamlit
- SHAP
Challenges & Learnings
Challenges
- Making model predictions understandable through interpretation rather than only showing a score.
- Keeping the input flow concise while still collecting enough information for prediction.
Learnings
- Explainability helps educational prediction tools feel more useful and responsible.
- Regression outputs need clear framing so users understand them as estimates.