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.env.example

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+# CWV-Predict — environment variables
+# Copy to .env and fill values (do not commit .env)
+
+DATA_PATH=data/cwv_features.csv
+MODEL_DIR=models
+RANDOM_STATE=42

README.md

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+# CWV-Predict: Core Web Vitals Prediction from Page Features
+
+**Type:** Academic | **Domain:** SEO, Web Performance  
+**Hugging Face:** [syeedalireza/cwv-predict](https://huggingface.co/syeedalireza/cwv-predict)
+
+Predict LCP, INP/FID, and CLS from page and server features before deployment (pre-deployment CWV estimation).
+
+## Author
+
+**Alireza Aminzadeh**  
+- Hugging Face: [syeedalireza](https://huggingface.co/syeedalireza)  
+- LinkedIn: [alirezaaminzadeh](https://www.linkedin.com/in/alirezaaminzadeh)  
+- Email: alireza.aminzadeh@hotmail.com
+
+## Problem
+
+Core Web Vitals (CWV) affect rankings and UX. Estimating CWV before deployment helps prioritize fixes and avoid regressions.
+
+## Approach
+
+- **Features:** Resource counts, DOM size, script size, server response time, image dimensions, etc.
+- **Targets:** LCP (ms), INP/FID (ms), CLS (score).
+- **Models:** Gradient boosting (XGBoost/LightGBM) for tabular features; optional lightweight embeddings for URL/path.
+
+## Tech Stack
+
+| Category | Tools |
+|----------|------|
+| ML | scikit-learn, XGBoost, LightGBM |
+| Data | pandas, NumPy |
+| Evaluation | sklearn metrics, optional MLflow |
+
+## Setup
+
+```bash
+python -m venv .venv
+.venv\Scripts\activate   # Windows
+pip install -r requirements.txt
+```
+
+## Usage
+
+```bash
+# Train (expects data in data/ or env DATA_PATH)
+python train.py
+
+# Predict on new samples
+python inference.py --input data/sample.csv --output predictions.csv
+```
+
+## Project structure
+
+```
+01_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
+
+- **Sample data (included):** `data/cwv_features.csv` (features + `lcp_ms`, `inp_ms`, `cls_score`), `data/sample.csv` (features only for inference).
+- **Columns:** `dom_size`, `resource_count`, `script_bytes`, `image_count`, `server_ttfb_ms`; targets: `lcp_ms`, `inp_ms`, `cls_score`.
+- Override paths via `DATA_PATH` in `.env`. See `.env.example`.
+
+## License
+
+MIT.

config.py

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+"""
+Configuration for CWV-Predict.
+"""
+import os
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parent
+DATA_PATH = os.getenv("DATA_PATH", str(BASE_DIR / "data" / "cwv_features.csv"))
+MODEL_DIR = Path(os.getenv("MODEL_DIR", str(BASE_DIR / "models")))
+RANDOM_STATE = int(os.getenv("RANDOM_STATE", "42"))
+
+MODEL_DIR.mkdir(parents=True, exist_ok=True)
+
+# Default feature columns (override if your CSV uses different names)
+FEATURE_COLUMNS = [
+    "dom_size",
+    "resource_count",
+    "script_bytes",
+    "image_count",
+    "server_ttfb_ms",
+]
+TARGET_COLUMNS = ["lcp_ms", "inp_ms", "cls_score"]

data/cwv_features.csv

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+dom_size,resource_count,script_bytes,image_count,server_ttfb_ms,lcp_ms,inp_ms,cls_score
+1200,45,280000,8,180,2100,85,0.08
+2400,82,520000,15,320,3400,120,0.15
+800,28,150000,5,95,1600,45,0.03
+3100,120,890000,22,450,4200,180,0.22
+950,32,210000,6,110,1850,52,0.05
+1800,58,380000,12,240,2700,95,0.11
+2700,75,610000,18,380,3600,140,0.18
+1100,38,245000,7,125,1950,58,0.06
+2200,65,440000,14,290,3100,108,0.13
+1400,42,310000,9,195,2350,72,0.09
+3500,95,720000,20,410,4500,195,0.25
+760,25,135000,4,88,1520,42,0.02
+1950,62,395000,13,255,2850,102,0.12
+2600,72,580000,17,365,3500,132,0.16
+1050,35,225000,6,118,1900,55,0.05
+2900,78,650000,19,395,3800,155,0.19
+1650,48,345000,10,210,2500,82,0.10
+3300,88,750000,21,430,4400,188,0.23
+890,30,175000,5,102,1720,48,0.04
+2100,68,470000,14,305,2950,115,0.14

data/sample.csv

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+dom_size,resource_count,script_bytes,image_count,server_ttfb_ms
+1500,50,300000,10,200
+2200,70,500000,16,350

inference.py

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+"""
+Run CWV predictions on a CSV with feature columns.
+"""
+import argparse
+from pathlib import Path
+
+import pandas as pd
+import xgboost as xgb
+
+from config import MODEL_DIR, FEATURE_COLUMNS, TARGET_COLUMNS
+
+
+def load_models():
+    """Load saved XGBoost models per target."""
+    models = {}
+    for t in TARGET_COLUMNS:
+        key = t.replace("_ms", "").replace("_score", "")
+        path = MODEL_DIR / f"cwv_{key}.json"
+        if path.exists():
+            models[t] = xgb.XGBRegressor()
+            models[t].load_model(str(path))
+    return models
+
+
+def main():
+    parser = argparse.ArgumentParser(description="CWV prediction")
+    parser.add_argument("--input", default="data/sample.csv", help="Input CSV path")
+    parser.add_argument("--output", default="predictions.csv", help="Output CSV path")
+    args = parser.parse_args()
+
+    if not Path(args.input).exists():
+        raise FileNotFoundError(f"Input file not found: {args.input}. Run from project root or use an absolute path.")
+    df = pd.read_csv(args.input)
+    available = [c for c in FEATURE_COLUMNS if c in df.columns]
+    if not available:
+        raise ValueError(f"Input must contain at least one of {FEATURE_COLUMNS}")
+
+    X = df[available].fillna(0)
+    models = load_models()
+    if not models:
+        raise FileNotFoundError(f"No trained models in {MODEL_DIR}. Run train.py first.")
+
+    out = df.copy()
+    for target, model in models.items():
+        out[f"pred_{target}"] = model.predict(X)
+
+    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
+    out.to_csv(args.output, index=False)
+    print(f"Predictions saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

models/cwv_cls.json

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models/metrics.json

@@ -0,0 +1,14 @@
+{
+  "lcp_ms": {
+    "mae": 213.48529052734375,
+    "r2": 0.9315611124038696
+  },
+  "inp_ms": {
+    "mae": 13.750458717346191,
+    "r2": 0.8237434029579163
+  },
+  "cls_score": {
+    "mae": 0.01267258580774069,
+    "r2": 0.9413852745662366
+  }
+}

requirements.txt

@@ -0,0 +1,11 @@
+# CWV-Predict — Core Web Vitals prediction
+# Python 3.9+
+
+numpy>=1.21.0
+pandas>=1.3.0
+scikit-learn>=1.0.0
+xgboost>=1.5.0
+lightgbm>=3.3.0
+
+# optional: experiment tracking
+# mlflow>=2.0.0

train.py

@@ -0,0 +1,64 @@
+"""
+Train CWV prediction models (LCP, INP, CLS).
+"""
+import json
+from pathlib import Path
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error, r2_score
+import xgboost as xgb
+
+from config import (
+    DATA_PATH,
+    MODEL_DIR,
+    RANDOM_STATE,
+    FEATURE_COLUMNS,
+    TARGET_COLUMNS,
+)
+
+
+def load_data(path: str) -> pd.DataFrame:
+    """Load CWV feature dataset."""
+    if not Path(path).exists():
+        raise FileNotFoundError(
+            f"Data not found at {path}. Place a CSV with columns {FEATURE_COLUMNS + TARGET_COLUMNS} or set DATA_PATH."
+        )
+    return pd.read_csv(path)
+
+
+def main():
+    df = load_data(DATA_PATH)
+    available_features = [c for c in FEATURE_COLUMNS if c in df.columns]
+    available_targets = [c for c in TARGET_COLUMNS if c in df.columns]
+    if not available_features or not available_targets:
+        raise ValueError(
+            f"Need columns: features {FEATURE_COLUMNS}, targets {TARGET_COLUMNS}. Found: {list(df.columns)}"
+        )
+
+    X = df[available_features].fillna(0)
+    y = df[available_targets]
+
+    X_train, X_val, y_train, y_val = train_test_split(
+        X, y, test_size=0.2, random_state=RANDOM_STATE
+    )
+
+    metrics = {}
+    for target in available_targets:
+        model = xgb.XGBRegressor(random_state=RANDOM_STATE, n_estimators=100)
+        model.fit(X_train, y_train[target])
+        pred = model.predict(X_val)
+        metrics[target] = {
+            "mae": float(mean_absolute_error(y_val[target], pred)),
+            "r2": float(r2_score(y_val[target], pred)),
+        }
+        model_path = MODEL_DIR / f"cwv_{target.replace('_ms', '').replace('_score', '')}.json"
+        model.save_model(str(model_path))
+
+    with open(MODEL_DIR / "metrics.json", "w") as f:
+        json.dump(metrics, f, indent=2)
+    print("Metrics:", json.dumps(metrics, indent=2))
+
+
+if __name__ == "__main__":
+    main()