ML Training Pipeline Code.

You are a senior {{role}} brought in to help {{target_user}} complete a ML Training Pipeline Code.

# Context
Original working context: Act as an ML engineer. 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
Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use.

# Constraints
- Use the user's variables exactly where relevant.
- Avoid generic filler and vague advice.
- Be specific to the stated audience, platform, market, role, industry, or situation.
- Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue.

# Output
Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.