{
  "model_name": "Housing Price Prediction Linear Regression Model",
  "model_type": "Regression",
  "algorithm": {
    "name": "Linear Regression",
    "library": "scikit-learn",
    "version": "1.0"
  },
  "training_dataset": {
    "name": "Housing Price Dataset",
    "description": "Structured housing data containing property size, number of rooms, construction year, city, country, and listing price.",
    "doi": "To be added after dataset deposit"
  },
  "input_features": [
    "property_size_m2",
    "total_rooms",
    "construction_year"
  ],
  "target_variable": "listing_price_eur",
  "hyperparameters": {
    "fit_intercept": true,
    "copy_X": true,
    "positive": false
  },
  "evaluation_metrics": {
    "mean_absolute_error": 18200.11,
    "root_mean_squared_error": 24500.42,
    "r2_score": 0.87
  },
  "intended_use": "Educational FAIR data science demonstration for reproducible housing price prediction.",
  "limitations": "The model is a baseline regression model and does not include advanced real estate market factors such as neighborhood quality, interest rates, energy efficiency, or exact geographic coordinates.",
  "ethical_considerations": "The model should not be used for financial, legal, mortgage, or real-world property valuation decisions.",
  "related_files": {
    "model_card": "docs/model-card.md",
    "training_script": "src/training/train_model.py",
    "evaluation_script": "src/evaluation/evaluate_model.py",
    "model_artifact": "outputs/model.pkl"
  }
}