spotify-data-analytics 0.1.0

End-to-end Spotify data analytics pipeline (ETL + analysis)

🎵 Spotify Data Analytics

End-to-end Spotify Data Analytics project using Python, Spotify Web API, and Power BI. Includes ETL pipeline, data cleaning, feature engineering, and insights on music trends & audio features. #data-engineering #data-analytics #spotify #python #ETL

📊 Sample Visuals

Here are some sample analytics from the project:

Distribution of Track Popularity

Top 10 Genres by Average Popularity

✨ What you get

• Python ETL using Spotipy (Spotify Web API)
• Cleaned analytics-ready datasets (CSV/Parquet)
• Feature engineering & correlations to explain what makes a hit
• Ready-made notebook for quick exploration
• Dashboard spec and sample visuals to replicate in Power BI

🧱 Project structure

spotify-data-analytics/
├─ README.md
├─ requirements.txt
├─ LICENSE
├─ .gitignore
├─ .env.example
├─ src/
│  ├─ extract_data.py
│  ├─ transform_data.py
│  ├─ load_data.py
│  └─ utils.py
├─ notebooks/
│  └─ spotify_exploration.ipynb
├─ data/
│  ├─ raw/
│  └─ processed/
├─ reports/
│  └─ dashboard_spec.md
└─ .github/workflows/
   └─ ci.yml

💼 Business Use Cases

• Music Industry Insights: Identify audio features (tempo, energy, danceability) contributing to hit songs.
• Artist Growth Tracking: Analyze listener trends and popularity across regions.
• Playlist Optimization: Curate playlists based on user listening patterns and genre preferences.
• Market Research: Help record labels evaluate trends to guide promotions and album releases.
• Recommendation Systems: Build smarter music recommendations using data analytics insights.

🚀 Setup

1. Create a Spotify app

• Go to https://developer.spotify.com/dashboard
• Create an app, copy Client ID and Client Secret
• Add a Redirect URI: http://localhost:9090/callback (Settings → Redirect URIs)

2. Clone & install

git clone https://github.com/LingeswaranR-22/spotify-data-analytics.git
cd spotify-data-analytics
python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

3. Set environment

cp .env.example .env
# Fill SPOTIFY_CLIENT_ID, SPOTIFY_CLIENT_SECRET

▶️ Run the pipeline

Extract (API → CSV):

python src/extract_data.py --markets IN US GB --playlists "Top 50 - Global" "Viral 50 - Global"

Transform (clean, join, features):

python src/transform_data.py

Load (optional—parquet, sample Snowflake stub):

python src/load_data.py --to parquet

Data will appear under data/raw and data/processed.

🔎 What it pulls

• Tracks from curated playlists (e.g., Top 50 - Global, Viral 50 - Global).
• Track audio features (danceability, energy, valence, tempo, acousticness, etc.).
• Artist details (name, popularity, genres).

📈 Example questions answered

• Which audio features correlate with popularity?
• What genres dominate top charts?
• Which artists are breaking out across markets?
• What tempo/energy ranges are most common among hits?

🗺️ Dashboard (Power BI / Looker)

Create visuals on data/processed/ outputs:

• Genre Popularity (bar/treemap)
• Audio Features vs Popularity (scatter)
• Artist Popularity vs Followers (bubble)
• Tempo distributions (histogram)

A dashboard plan is in reports/dashboard_spec.md.

🧪 CI (lightweight)

A tiny GitHub Action runs flake checks & unit smoke tests on push.

📝 Notes on auth

• Most public playlist/track data works with Client Credentials (no user login).
• If you need user playlists, flip the --user-auth flag and complete the browser login once.

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Author: Lingeswaran R
License: MIT