Upload training_config.json

training_config.json

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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"
+}