ML Training Pipeline Code.

You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task.

# Context
- Pack: Developers & Tech Professionals
- Category: Machine Learning & AI Engineering
- Use case: ML Training Pipeline Code
- Source task:
  - Write a production-quality training pipeline in Python for {{describe_the_ml_problem}}. Include:
  - 1. data loading and validation
  - 2. feature preprocessing pipeline using sklearn Pipeline or equivalent
  - 3. model training with cross-validation
  - 4. hyperparameter tuning with Optuna or GridSearchCV
  - 5. model evaluation and metric logging (MLflow or W&B)
  - 6. model serialisation (save the full pipeline including preprocessing)
  - 7. a training script that is rerunnable and produces the same results with a fixed seed

# Goal
Complete training pipeline code covering data loading, preprocessing, training, tuning, evaluation, logging, and serialisation.

# Constraints
- Produce a complete, usable first draft in one response.
- Avoid generic filler, vague advice, and unsupported claims.
- Make the output specific, practical, and ready to use.

# Output
Complete training pipeline code covering data loading, preprocessing, training, tuning, evaluation, logging, and serialisation.