README.md

---
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.