System Architecture
An end-to-end machine learning pipeline that processes student data through automated feature engineering and model selection, delivering predictions via a Flask web interface. The system achieves 88% R² score using optimized ensemble models.
Technical Implementation
- ▹ Automated data preprocessing (OneHotEncoding, StandardScaler)
- ▹ Hyperparameter tuning with GridSearchCV
- ▹ Ensemble model comparison (7 algorithms)
- ▹ Model persistence with dill
Core Features
Automated Model Selection
Evaluates 7 regression models (XGBoost, CatBoost, Random Forest) and selects best performer based on R² score
Data Pipeline
Automated data validation and transformation with Scikit-learn ColumnTransformer
Web Interface
Responsive Flask interface with form validation and score clamping (0-100)
Model Interpretability
SHAP value integration for feature importance visualization
Technical Specifications
ML Pipeline
- Data ingestion from CSV/API
- Automated feature engineering
- Hyperparameter optimization
- Model evaluation (R², MAE, MSE)
- Prediction serving via Flask
Performance Metrics
Development Challenges
⚠️ Categorical Feature Handling
Implemented robust OneHotEncoding with rare category handling and automated pipeline persistence
⚡ Model Deployment
Optimized Flask app for production with gunicorn and proper error handling
