| # 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. | |
| --- | |
| ## 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? | |
| --- | |
| ## 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. | |
| --- | |
| ## 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. | |
|  | |
| --- | |
| ## 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. | |
| --- | |
| ## 2-3 Minute Video | |
| Paste your video link here (Loom/YouTube/Drive): | |
| **Video:** (https://youtu.be/CIxhSgZXnyw) | |
| --- | |
| ## Files | |
| - `SpotifyFeatures_sample.csv` – dataset sample | |
| - `spotify_eda_notebook.ipynb` – code & plots | |
| - `materials/` – exported materials used in this README | |
| - `README.md` – this summary | |
| --- | |
| ## 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. |