docs: update README

README.md

@@ -1,3 +1,68 @@
----
-license: cdla-sharing-1.0
----
+---
+license: cdla-sharing-1.0
+language:
+  - en
+tags:
+  - music
+  - code
+pretty_name: Spotify-YouTube Combined Ensemble Features
+size_categories:
+  - 10K<n<100K
+---
+
+# Spotify–YouTube Combined Ensemble Features
+
+A **single-table, modeling-ready** CSV that **joins Spotify track metadata**, **librosa audio features** (from matched YouTube audio), and **YouTube engagement** fields on a common key (`track_id`). Built for **ensemble / viral prediction** experiments in the *viral-content-predictor* project (e.g. `03_combined_model_training.ipynb`).
+
+## File
+
+| File | Role |
+|------|------|
+| `combined_features_cleaned.csv` | One row per track (after pipeline joins); mixed numeric, categorical encodings, and labels. |
+
+## Typical contents (schema evolves with the pipeline)
+
+A representative export has on the order of **~10⁴–10⁵ rows** and **~100+ columns**, including:
+
+| Group | Examples |
+|-------|----------|
+| **IDs / text** | `track_id`, `track_name`, `artists` |
+| **Spotify** | `popularity`, `loudness`, `valence`, `danceability`, `energy`, `tempo_spotify`, `speechiness`, `liveness`, `acousticness`, `instrumentalness`, one-hot `explicit_*`, `mode_*`, `time_signature_*` |
+| **Audio (librosa)** | Spectral, MFCC, chroma, tonnetz, onset, ZCR, `tempo_librosa`, etc. |
+| **YouTube engagement** | `view_count`, `like_count`, `comment_count`, derived rates such as `like_rate`, `comment_rate` |
+| **Targets** | `viral` (binary), `virality_score` (or project-specific label columns) |
+
+Exact names and counts depend on the notebook version that produced the file—inspect with:
+
+```python
+import pandas as pd
+df = pd.read_csv("combined_features_cleaned.csv", nrows=5)
+print(df.shape[1], "columns")
+print(df.columns.tolist())
+```
+
+## Provenance
+
+- **Assembled** from cleaned Spotify tables, **YouTube**-aligned metadata/features, and **audio feature** extractions already aligned in the project DAG.
+- **Consumers:** `notebooks/03_combined_model_training.ipynb`, `notebooks/exploratory/explore_combined_enesmble_voting.ipynb`, etc., reading `data/processed/combined_features_cleaned.csv`.
+
+## Modeling notes (leakage)
+
+The table may include **YouTube engagement** columns that **directly relate** to how “viral” was defined. For many experiments those columns are **excluded from `X`** when training the viral classifier so labels are not trivially predictable—see the **exclude list** in `03_combined_model_training.ipynb`. Keep or drop columns according to your task.
+
+## Usage
+
+```python
+import pandas as pd
+
+df = pd.read_csv("combined_features_cleaned.csv")
+```
+
+## Limitations
+
+- **Snapshot:** reflects the pipeline run that produced it, not live Spotify/YouTube.
+- **License / rights:** comply with **CDLA-Sharing-1.0** (this card), Spotify and YouTube terms, and any third-party dataset licenses you merged.
+
+## Citation
+
+Cite this repository and the specific notebook revision or release tag used to build the CSV.