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README.md

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+# 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).
+
+![Popularity distribution](materials/popularity_hist.png)
+![Feature distributions](materials/features_dists.png)
+
+### 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**.
+
+![Correlation heatmap](materials/corr_heatmap.png)
+
+### 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.
+
+![Top vs Bottom 10%](materials/top_vs_bottom_boxplot.png)
+
+---
+
+## 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:** _ADD_LINK_HERE_
+
+---
+
+## 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.