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mlops-ref-retail-demand

Joblib
cbc-reference-model
mlops-100-day
demand-forecasting
time-series
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mlops-ref-retail-demand
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{
  "model_name": "CBC Retail Demand Forecaster",
  "hf_repo": "careerbytecode/mlops-ref-retail-demand",
  "task": "regression (next-hour demand forecast, hourly time series)",
  "model_type": "XGBoost regressor, 12 past-only lag/rolling/calendar features",
  "framework": "xgboost",
  "serialization": "joblib (full XGBRegressor)",
  "loader": "joblib.load -> XGBRegressor; call .predict(DataFrame[FEATURES]) -> predicted trips",
  "random_state": 42,
  "feature_columns": [
    "lag_1",
    "lag_2",
    "lag_3",
    "lag_24",
    "lag_168",
    "roll_mean_24",
    "roll_mean_168",
    "roll_std_24",
    "hour",
    "day_of_week",
    "is_weekend",
    "day_of_month"
  ],
  "split": {
    "train": 460,
    "test": 116,
    "method": "forward time-ordered 80/20"
  },
  "dataset": "NYC Yellow Taxi Jan-2024 hourly (744h), NYC.gov Terms of Use",
  "python_version": "3.14.4",
  "library_versions": {
    "xgboost": "3.2.0",
    "scikit-learn": "1.8.0",
    "pandas": "2.3.3",
    "numpy": "2.4.6",
    "joblib": "1.5.3"
  },
  "training_date": "2026-06-04T20:25:14.625353+00:00"
}