adiprog14/spotify-eda-analysis

# 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
<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

- **Rows:** 113,397 songs
- **Columns:** 19 features
- **Source:** HuggingFace - maharshipandya/spotify-tracks-dataset

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## 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


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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