This project presents a Spotify Analytics Dashboard built using Power BI and enriched with Python for API integrations and custom visuals. It provides deep insights into streaming data, artist and track performance, and listener behavior patterns throughout 2024. Special highlights include a Python-generated heatmap using Matplotlib & Seaborn, album cover integration using Spotify's Developer API, and interactive Power BI visuals.
- Interactive Dashboard: Built in Power BI with dynamic filters for artist, track, and release year.
- Custom Python Visuals: Includes a Matplotlib + Seaborn heatmap for weekly/monthly track usage.
- Spotify API Integration: Fetches album cover images and additional metadata using Python.
- Engaging UI Design: Custom dashboard background designed in Figma with smooth layout.
- Avg Stream Per Year vs Top Song Avg
- Energy Level Indicator Gauge
- Streams by Day of the Week
- Track Name Count by Month
- Spotify Track Usage Heatmap (Python-based)
- Spotify Stream by Track
- Track Details Panel (Valence, Danceability, Speechiness, etc.)
| Component | Technology |
|---|---|
| Data Viz | Power BI |
| API Integration | Python, Spotify for Developers |
| Custom Visuals | Matplotlib, Seaborn |
| Data Processing | Pandas, NumPy |
| UI Design | Figma |
| Web API | Requests |
This dashboard is useful for:
- Music Analysts: Explore stream trends and track performance.
- Marketing Teams: Understand user behavior and engagement metrics.
- Developers & Data Scientists: Learn API integration and Python-Power BI synergy.
- Music Enthusiasts: Discover patterns in top tracks and artists.
Contributions are welcome! Feel free to fork, enhance features, or suggest new visualizations.