Spotify Music Analysis Dashboard

Questions This Project Answers

1. What makes a song popular on Spotify — is it danceability, energy, or something else?
2. What do the most popular songs have in common?
3. How do audio features differ across genres?

Live Dashboard

View Interactive Dashboard

Key Insights

1. Popularity has NO strong correlation with any single audio feature — marketing and artist fame matter more than sound
2. Pop-film and K-pop are most popular genres — mood-based genres beat traditional pop
3. Latin artists dominate top 10 — Bad Bunny, Bizarrap, Manuel Turizo reflect global latin music wave
4. Popular songs are short — sweet spot is 2-5 minutes, nothing above 8 minutes goes viral
5. Explicit songs are slightly more popular (36 vs 33 avg popularity)
6. Grunge = high energy, low danceability. Chill = low energy, high acousticness. Each genre has a distinct audio fingerprint
7. 18000+ songs have zero popularity — most Spotify content is never discovered

Tech Stack

• Python, Pandas — data cleaning and EDA
• PostgreSQL — SQL analysis
• Plotly, Seaborn — visualizations
• Streamlit — interactive dashboard

Project Structure

spotify-analysis/
├── data/
│   ├── raw/
│   └── cleaned/
├── notebooks/
│   ├── 01_data_cleaning.ipynb
│   ├── 02_eda.ipynb
│   └── queries.sql
├── dashboard/
│   └── app.py
└── README.md

Dataset

Spotify Tracks Dataset via Kaggle — 114,000 tracks across 114 genres.