A four-stage data pipeline that scrapes commenter overlap between channels, scores it as an audience-fit signal, and trains a classifier to flag collaboration candidates.
Percentage of each channel's commenters who also commented on the reference channel (Coke Studio Pak) — the core signal the pipeline is built to measure.
Nizam Torwali's 52.94% comes from a channel with only 51 unique commenters — small comment counts make overlap percentage noisy, which is exactly the kind of case the classifier has to weigh against subscriber count.
Four independent scripts, chained through MongoDB, run in sequence across each candidate channel.
Scrape commenters
Pull a channel's videos via the YouTube Data API, then every top-level commenter on each one.
Node.js · YouTube Data API v3 · axios
Import to MongoDB
Watch for scraped CSVs and stream each one into a per-channel collection as it lands.
Node.js · MongoDB · csv-parser
Compare audience overlap
Intersect commenter ID sets against the reference channel and compute overlap percentage.
Node.js · MongoDB aggregation
Classify collaboration fit
Train a decision tree on overlap score and subscriber count to predict good collaborators.
Python · pandas · scikit-learn
Decision tree trained on audience overlap score and subscriber count, evaluated on a held-out test split.
| Pred: No | Pred: Yes | |
|---|---|---|
| True: No | 13 | 0 |
| True: Yes | 0 | 7 |