SIENTIAPDE-1430: Introduce comprehensive integration testing with JSON-based scenarios and detailed README documentation. Enhance training workflow to support advanced model configurations, including polynomial regression with mandatory scaler validation. Ensure robust prediction handling by calculating training predictions (y_train_pred) before denormalization and automatically configuring datetime indices for time-series operations.

model_manager/utils/models/train_model_params.py

@@ -248,6 +248,12 @@ class TrainModelParams:
                 f'degree must be at least 2 for Polynomial Regression, got {self.degree}'
             )
 
+        if self.model_name == 'Polynomial Regression' and self.scaler_name == 'None':
+            raise ValueError(
+                'scaler_name must be set (e.g., "Standard Scaler") for Polynomial Regression '
+                'to avoid numerical overflow with large feature values'
+            )
+
         if self.model_name == 'Linear Regression' and self.degree != 1:
             raise ValueError(f'degree must be 1 for Linear Regression, got {self.degree}')