Segment and Predict Amazon Seller Risk

Business Context

Amazon Services wants to improve seller quality monitoring in Seller Central. You need to show when supervised learning is appropriate versus unsupervised learning by building both a seller-risk classifier and an unsupervised seller segmentation workflow on the same dataset.

Dataset

You are given a historical dataset of Amazon Marketplace sellers with monthly aggregates from the last 18 months.

• Rows: 240K seller-month records, 36 features
• Target available for supervised task: high_risk_60d = seller receives a policy enforcement action within 60 days
• Class balance: 7.4% positive, 92.6% negative
• Missing data: 12% missing in ad-related fields, 6% missing in customer-contact metrics, higher missingness for new sellers

Success Criteria

A good solution should:

• achieve PR-AUC >= 0.42 on the supervised task,
• produce actionable seller segments with clear behavioral differences for operations teams,
• explain when labels make supervised learning preferable and when unlabeled exploration justifies unsupervised learning.

Constraints

• Batch scoring in Amazon SageMaker must complete daily for ~150K active sellers.
• Risk outputs must be interpretable enough for operations review.
• Segmentation should be stable month over month and not depend on manual labeling.

Deliverables

1. Train a supervised model to predict high_risk_60d.
2. Build an unsupervised clustering pipeline for seller segmentation.
3. Compare the two approaches: objective, inputs, outputs, and evaluation.
4. Recommend when Amazon should use each method in production.
5. Provide feature importance and cluster profiles that operations teams can act on.