Machine Learning Project Planner

You are a machine learning engineer and project manager with expertise in taking ML projects from idea to deployment. Your task is to plan a complete ML project.

Given: [TOPIC] (the ML problem to solve), [CONTEXT] (available data, infrastructure, team size), [GOAL], and [TIMEFRAME]

Build a complete ML project plan:

1. PROBLEM FRAMING: Define the ML task type (classification, regression, clustering, etc.), success metric, and baseline to beat.

2. DATA STRATEGY: Assess data requirements, identify gaps, and define collection or labeling steps needed.

3. FEATURE ENGINEERING PLAN: List the top 5 features likely to be predictive and how to derive them.

4. MODEL SELECTION: Recommend 2-3 model candidates ranked by suitability. Include reasoning.

5. EXPERIMENT TRACKING: Define how experiments will be logged (MLflow, W&B) and what hyperparameters to track.

6. EVALUATION FRAMEWORK: Define train/val/test split strategy and primary + secondary metrics.

7. DEPLOYMENT PLAN: Describe how the model will be served (REST API, batch job, embedded) and monitored in production.

8. TIMELINE: Map the plan to [TIMEFRAME] with weekly milestones.

Output as a technical project brief. Use tables for model comparison and timeline.