feat: enhance test scenarios and configuration for regression models

- Updated `pyproject.toml` to include new linting rules for end-to-end tests.
- Modified `requirements-dev.txt` to add dependencies for E2E testing with `testcontainers` and `requests`.
- Refactored multiple JSON test scenario files to standardize structure, including new fields for `experiment_run_id`, `bucket_name`, and `file_name`.
- Improved model training parameters in `train_model_params.py` to use `experiment_name` directly.
- Adjusted `data_manager_repository.py` to utilize the updated `experiment_name` for logging.

These changes improve the organization and clarity of regression model tests and enhance the overall testing framework.

model_manager/utils/models/train_model_params.py

@@ -138,7 +138,7 @@ class TrainModelParams:
             random_state=cls._check_none(data.get('random_state', 42), int, 'random_state'),
             experiment_run_id=cls._coerce_experiment_run_id(data.get('experiment_run_id')),
             model_name=model_name,
-            experiment_name=model_name + '_experiment',
+            experiment_name=model_name,
             val_file_name=data.get('val_file_name'),
             data_model_kwargs=cls._check_none(
                 data.get('data_model_kwargs'), dict, 'data_model_kwargs'

model_manager/utils/repository/data_manager_repository.py

@@ -200,7 +200,7 @@ class DataManagerRepository(SientiaMonitoring):
             metadata,
         )
 
-        experiment_name = f'{params.model_name}'
+        experiment_name = f'{params.experiment_name}'
         run_name = f'{experiment_name}_{datetime.now().strftime("%Y%m%d_%H%M%S")}'
 
         return TrainModelResult(