starter CSV + card

README.md

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+---
+license: other
+language:
+- en
+tags:
+- seo
+- search-console
+- content-performance
+- tabular
+- education
+- flyrank-internship
+pretty_name: FlyRank Internship — Starter (Content Refresh, Anonymized)
+size_categories:
+- 10K<n<100K
+---
+
+# FlyRank Internship — Starter Dataset (Anonymized)
+
+The public, safe starting point for the FlyRank **Applied Search Intelligence** ML internship.
+**30,000** anonymized content-performance rows across **32** pseudonymized clients (53 columns).
+
+**Public-safe:** hashed `content_id` / `client_id` + numeric/categorical metrics only — **no** titles, URLs, keywords, domains, or client names.
+
+## What it's for
+Week 1–2 quick wins and the ready-now capstone lanes (ranking-signal analysis, lifecycle / opportunity scoring, content-archetype clustering).
+
+## Verified reference results (this 30k slice)
+- Rule baseline **Precision@50 = 0.26** → Random Forest **Precision@50 = 0.74**
+- `search_volume` vs `impressions_90d` correlation ≈ **0.0012** (essentially zero — a real myth-buster)
+- Weighted CTR by position: `top_3` **0.49%** → `page_1` 0.35% → `deep` **0.04%**
+- Length is *not* the differentiator: growing vs declining word count ≈ 2,850 vs 2,910
+
+## Safety rules
+Anonymized, but still treat row-level outputs as not-for-careless-publishing.
+Do **not** use product flags (`health_score`, `needs_ctr_fix`, `is_quick_win`, …) as model features — they leak the decline label. Keep all public outputs anonymized/aggregate.