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Spotify Tracks Dataset - EDA Analysis
Student: Adi Toledano
Dataset Source: maharshipandya/spotify-tracks-dataset
Goal: Analyze what features influence a song's popularity on Spotify
Dataset Overview
- Rows: 113,397 songs
- Columns: 19 features
- Source: HuggingFace - maharshipandya/spotify-tracks-dataset
Research Questions & Key Findings
1. What is the distribution of song popularity?
- Average popularity: 33.3
- Most songs have low popularity, few reach the top
2. Which genres are most popular?
- 🥇 Pop-Film: 59.3
- 🥈 K-Pop: 57.0
- 🥉 Chill: 53.7
3. Do danceability and energy affect popularity?
- Danceability correlation: 0.034 (almost none)
- Energy correlation: -0.000 (none)
- Surprising finding: audio features barely affect popularity!
4. Strong correlations found:
- Energy ↔ Loudness: 0.76
- Energy ↔ Acousticness: -0.73
- Danceability ↔ Valence: 0.48
5. Are explicit songs more popular?
- Explicit songs: 36.5 average
- Non-explicit songs: 33.0 average
- Explicit songs are slightly more popular
6. Who are the most popular artists?
- 🥇 Sam Smith & Kim Petras: 100
- 🥈 Bizarrap & Quevedo: 99
- 🥉 Manuel Turizo: 98
Conclusion
Genre and artist identity matter more than audio features when it comes to a song's popularity on Spotify!
Files
spotify_eda.ipynb- Full analysis notebookspotify_cleaned.csv- Cleaned dataset
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