ML Problem Framer.

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 Problem Framer
- Source task:
  - Frame the following business problem as a machine learning problem: {{describe_the_business_problem}}. Produce:
  - 1. problem type classification (classification, regression, clustering, ranking, generative)
  - 2. target variable definition
  - 3. feature candidates (what data inputs would be predictive)
  - 4. success metric (accuracy, F1, RMSE, NDCG : choose and justify)
  - 5. baseline to beat (what simple rule-based approach would you compare against?)
  - 6. data requirements : minimum volume, freshness, labelling strategy

# Goal
ML problem classification, target variable definition, feature candidates, success metric, baseline, and data requirements.

# 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
ML problem classification, target variable definition, feature candidates, success metric, baseline, and data requirements.