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FlyRank Internship — Lane Datasets (Pseudonymized)
Six task-focused, pseudonymized lanes derived from the FlyRank warehouse. Salted/namespaced hashed keys join across lanes and the warehouse release.
Provenance: these are fixed example cuts prepared in June 2026 (90-day windows as of their build date). They exist to show the shape of lane work — the warehouse release (v2 full history) is the source of truth, and capstone-grade features should be engineered from it directly (see starter notebook 03).
Default lanes (intern-safe)
| Lane | Rows | Typical task |
|---|---|---|
ranking_lifecycle |
205,749 | ranking lifecycle, archetypes, baseline scoring |
decline_recovery |
56,253 | decline/recovery classification (define a forward label) |
engagement_fix |
33,202 | CTR / engagement opportunity analysis |
ai_opportunity |
11,571 | AI-referral pattern analysis (positive-only — not a clean binary classifier) |
Restricted-origin lanes (raw query/URL origin → keep separate)
| Lane | Rows | Typical task |
|---|---|---|
cannibalization_restricted |
712,454 | cannibalization severity / action ranking |
semantic_clustering_restricted |
350,707 | structured clustering / archetypes |
Rules
Load a lane with datasets:
from datasets import load_dataset
ds = load_dataset("FlyRank/internship-lanes", "ranking_lifecycle")
Product flags / scores (health_score, trend_*, recommended_action, action_type) are leakage-risk context, not default features. Define your own labels, features, splits, and a leakage-safe prediction point. Public outputs must be anonymized/aggregate only.
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