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| Category | Tools |
|---|---|
| ML | scikit-learn, XGBoost, LightGBM |
| Data | pandas, NumPy |
| Evaluation | sklearn metrics, optional MLflow |
1python -m venv .venv
2.venv\Scripts\activate # Windows
3pip install -r requirements.txt1# Train (expects data in data/ or env DATA_PATH)
2python train.py
3
4# Predict on new samples
5python inference.py --input data/sample.csv --output predictions.csv01_cwv-predict/
├── config.py # Paths and column names
├── train.py # Train XGBoost regressors per CWV metric
├── inference.py # Batch prediction from CSV
├── requirements.txt
├── .env.example
├── data/
│ ├── cwv_features.csv # Sample training data (features + targets)
│ └── sample.csv # Sample input for inference (features only)
└── models/ # Saved models and metrics (after train)data/cwv_features.csv (features + lcp_ms, inp_ms, cls_score), data/sample.csv (features only for inference).dom_size, resource_count, script_bytes, image_count, server_ttfb_ms; targets: lcp_ms, inp_ms, cls_score.DATA_PATH in .env. See .env.example.