orianrivlin/SpotifyFeatures_sample

Assignment #1 - EDA & Dataset - orian rivlin

Goal: Explore which audio features are most strongly related to a track’s popularity on Spotify. This repository includes the dataset sample, a well-documented notebook, saved figures, and a short video walkthrough.

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Dataset

• Name: Spotify Features (Sample)
• File: SpotifyFeatures_sample.csv
• Rows: ~10,000  |  Columns: 18 (mostly numeric)
• Target: popularity (0–100)
• Main numeric features: danceability, energy, loudness, speechiness, acousticness, instrumentalness, liveness, valence, tempo, duration_ms
• Source: Spotify Tracks DB (via Kaggle)

Main question: Which audio features are most strongly related to popularity?

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Data Cleaning (Step 2.1)

• Removed duplicate rows.
• Checked missing values (none in key columns)
• Dropped rows with NA in core numeric features if present.
• Built a numeric-only DataFrame for stats and correlations.

Result: 10,000 rows × 11 numeric features** ready for EDA.

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EDA Highlights

1) Distributions

• Popularity is concentrated around 40-60 with few very high hits (80+).
• Danceability ~ bell-shaped around ~0.56.
• Energy skews higher (many tracks at 0.7-0.9).
• Valence is broadly spread (neutral on average).

2) Correlations with Popularity

Top relationships:

Feature	Corr. with popularity
loudness	+0.31
energy	+0.27
danceability	+0.06
tempo	+0.02
valence	−0.06
acousticness	−0.35

Interpretation. Popular tracks tend to be louder and more energetic, and less acoustic.

3) Popular vs. Unpopular (Top 10% vs Bottom 10%)

• Energy and (slightly) danceability are higher among top-10% tracks.
• Tempo shows little difference.
• Valence is only slightly higher for top tracks.

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Key Insights

• Popularity on Spotify is uneven: only a small minority of tracks become very popular.
• Loudness and energy are the strongest positive correlates of popularity; acousticness is the strongest negative correlate.
• Highly popular songs tend to sound modern/produced (loud, energetic), while purely acoustic/instrumental tracks underperform on average.

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Prestation Video

Video: (https://youtu.be/CIxhSgZXnyw)

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Files

• SpotifyFeatures_sample.csv – dataset sample
• spotify_eda_notebook.ipynb – code & plots
• materials/ – exported materials used in this README
• README.md – this summary

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Notes and Decisions

• Outliers (very long tracks; very low/high popularity) were kept as real observations; statistics were interpreted cautiously.
• Correlations are modest overall (music success is multifactorial); results describe associations, not causation.