| # Spotify Tracks Dataset - EDA Analysis |
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| **Student:** Adi Toledano |
| **Dataset Source:** maharshipandya/spotify-tracks-dataset |
| **Goal:** Analyze what features influence a song's popularity on Spotify |
| <video src="https://huggingface.co/datasets/adiprog14/spotify-eda-analysis/resolve/main/presentation%20%231.mp4" controls="controls" style="max-width: 720px;"></video> |
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| ## Dataset Overview |
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| - **Rows:** 113,397 songs |
| - **Columns:** 19 features |
| - **Source:** HuggingFace - maharshipandya/spotify-tracks-dataset |
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| ## Research Questions & Key Findings |
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| ### 1. What is the distribution of song popularity? |
| - Average popularity: 33.3 |
| - Most songs have low popularity, few reach the top |
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| ### 2. Which genres are most popular? |
| - 🥇 Pop-Film: 59.3 |
| - 🥈 K-Pop: 57.0 |
| - 🥉 Chill: 53.7 |
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| ### 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! |
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| ### 4. Strong correlations found: |
| - Energy ↔ Loudness: 0.76 |
| - Energy ↔ Acousticness: -0.73 |
| - Danceability ↔ Valence: 0.48 |
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| ### 5. Are explicit songs more popular? |
| - Explicit songs: 36.5 average |
| - Non-explicit songs: 33.0 average |
| - Explicit songs are slightly more popular |
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| ### 6. Who are the most popular artists? |
| - 🥇 Sam Smith & Kim Petras: 100 |
| - 🥈 Bizarrap & Quevedo: 99 |
| - 🥉 Manuel Turizo: 98 |
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| ## Conclusion |
| Genre and artist identity matter more than audio features |
| when it comes to a song's popularity on Spotify! |
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| ## Files |
| - `spotify_eda.ipynb` - Full analysis notebook |
| - `spotify_cleaned.csv` - Cleaned dataset |