Noam12345/my_first_asignment_ds

🎵 What Makes a Hit Song? — Spotify EDA

Assignment #1 — EDA & Dataset | March 2026

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📽️ Video Presentation

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📌 Overview

This project explores the 30,000 Spotify Songs dataset to answer one core question:

How do audio features — danceability, energy, loudness, speechiness, tempo, acousticness, instrumentalness, liveness, valence, and duration — affect the popularity of a song?

The goal is to identify the optimal range of audio features that music producers should aim for to increase the chances of their song becoming a hit.

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📂 Dataset

Property	Details
Source	Kaggle — 30000 Spotify Songs
Raw Size	32,833 rows × 23 features
Final Size	32,790 rows × 25 features (after cleaning, feature engineering & column pruning)
Target Variable	track_popularity (0–100)
Hit Definition	track_popularity > 75 (top ~8.1% of all tracks)

Audio Features Explained

Feature	What It Means	Example
danceability	How easy it is to dance to (0.0–1.0)	Low = slow ballad, High = club banger
energy	Perceptual intensity and activity (0.0–1.0)	Death metal = high, Bach prelude = low
loudness	Average loudness in decibels (dB)	Closer to 0 = louder, −60 = almost silent
speechiness	Presence of spoken words (0.0–1.0)	>0.66 = mostly speech, <0.33 = music
acousticness	Confidence that a track is acoustic (0.0–1.0)	Guitar unplugged = high, EDM synths = low
instrumentalness	Likelihood the track has no vocals (0.0–1.0)	Jazz instrumental = high, pop singer = low
liveness	Presence of a live audience (0.0–1.0)	>0.8 = likely live recording
valence	Musical positiveness (0.0–1.0)	Happy/cheerful = high, sad/angry = low
tempo	Estimated tempo in BPM	60 BPM = slow, 180 BPM = very fast
duration_min	Song length in minutes	2 min = short, 6 min = long

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🛠️ Setup

from google.colab import drive
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Developed in Google Colab with the dataset loaded from Google Drive.

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🧹 Data Cleaning

Missing Values: Found 5 rows with missing track_name, track_artist, and track_album_name — all removed.

Duplicates: None found — no action needed.

Impossible Values:

• tempo = 0 BPM → dropped (1 track). A song with 0 BPM cannot exist.
• loudness > 0 dB → dropped (6 tracks). Industry standard caps loudness at 0 dB.

Final shape after cleaning: (32,790, 23)

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⚙️ Feature Engineering

Three new features were created:

• release_year — extracted from track_album_release_date (covers 1957–2020)
• is_hit — True if track_popularity > 75 (top ~8.1% of tracks by popularity score)
• loudness_scaled — loudness rescaled from dB (−60 to 0) to a 0–100 range for readability
• duration_min — changed duration ms to duration in min.

🗑️ Column Pruning

Two columns were intentionally dropped after feature engineering:

• track_album_release_date — used only to derive release_year. Once extracted, the original date string is redundant.
• playlist_subgenre — all genre analysis operates at the broad playlist_genre level. This column was never computed, visualized, or referenced.

Final dataset shape: (32,790, 25)

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📊 Visualizations & Key Findings

1. Outlier Detection — All 10 Audio Features

Box plots across all 10 features reveal the spread and outliers in the dataset. Key observations:

• Tempo clusters tightly between 100–130 BPM
• Instrumentalness is nearly flat near 0 — ~90% of tracks have vocals
• Speechiness & Liveness are heavily right-skewed — most tracks are studio recordings with minimal spoken word
• Valence is evenly spread, reflecting a healthy mix of happy and sad music

All outliers were kept — extreme values (e.g. 10-min songs, instrumental tracks) are valid artistic choices, not data errors.

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2. Hit Rate Threshold Sensitivity

To define "hit" objectively, every threshold from 60 to 85 was tested. Results:

Threshold	Hit Rate	Decision
60	27.2%	Too generous — over a quarter of songs can't all be hits
65	20.1%	Still too broad
70	13.5%	Getting closer
75	8.1%	✅ Selected — meaningful minority, large enough to analyze
80	4.1%	Too strict — loses too much signal
85	1.9%	Only mega-hits qualify

Threshold = 75 was selected as the sweet spot.

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3. Popularity Distribution & Hit/Non-Hit Split

The popularity distribution shows a natural drop-off above 70, confirming that 75 sits right at the edge of the top tier. Result: 2,657 hits (8.1%) vs 30,133 non-hits (91.9%). For every 1 hit, there are ~11 non-hit songs.

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4. Hits vs Non-Hits — Median Feature Comparison

All 10 features normalized to 0–1 and compared between hits and non-hits:

• ↑ Danceability — hits score consistently higher. Groove matters.
• ↑ Loudness — hits are louder. Tight mastering is industry standard.
• ↑ Acousticness — hits have a slightly more organic sound.
• ↑ Valence — hits trend happier. Feel-good music has a streaming edge.
• ↓ Speechiness — hits are more melodic, less spoken-word.
• ↓ Instrumentalness — hits almost always have vocals.
• ↓ Liveness — hits are studio recordings, not live performances.
• ↓ Tempo — hits are slightly slower (~118 BPM vs ~122 BPM average).
• ↓ Duration — hits are shorter. Streaming rewards conciseness.
• ~ Energy — similar between groups; hits are slightly lower in energy.

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5. Correlation Heatmap — All Audio Features

Key relationships revealed by the heatmap:

• Energy ↔ Loudness (r = +0.68) — the strongest link. Louder songs almost always feel more energetic.
• Energy ↔ Acousticness (r = −0.54) — acoustic tracks feel calmer and less intense.
• Loudness ↔ Acousticness (r = −0.36) — louder songs are less acoustic.
• Danceability ↔ Valence (r = +0.33) — happier songs tend to be more danceable.
• Instrumentalness ↔ Popularity (r = −0.15) — the clearest predictor: vocals = popularity.
• Duration ↔ Popularity (r = −0.14) — shorter songs perform better.

All other features vs popularity show correlations below 0.10 — no single feature is a magic predictor on its own.

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6. Audio Features vs Track Popularity — Scatter Plots

Each feature plotted against popularity with a linear trend line (r values shown):

• Instrumentalness (r = −0.15) and Duration (r = −0.14) show the clearest negative trends.
• Acousticness (r = +0.09) is the strongest positive predictor — a touch of organic sound helps.
• Tempo (r ≈ 0.00) is essentially flat — tempo alone does not drive popularity.
• Energy, Speechiness, Liveness, Valence all show weak but directionally consistent trends.

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7. Music Trends Over Time

Hit rates peaked in the mid-1960s (~17%) and surged again post-2015, reaching ~14% by 2019.

Why recent songs score higher: Spotify's popularity score is recency-weighted — it favors tracks with recent, high-volume streaming activity over all-time totals. Combined with the viral amplification of social media (TikTok, Instagram Reels), newer songs accumulate streams far faster than songs from previous decades could. This is a measurement artifact, not evidence that modern music is objectively better.

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8. Songs count per year.

Clearly shows a difference in amount of data per each year.

9. Hit Ratio by Genre

Genre	Hit Ratio	Notes
Pop	0.157	Nearly double the overall average — designed for mass appeal
Latin	0.138	Strong global wave (Bad Bunny, J Balvin) shows in the data
R&B	0.098	Above average — loyal streaming audience
Rap	0.060	Below average
EDM	0.044	Lowest — EDM success lives in festivals, not Spotify streams

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10. Sweet Spot by Genre — KDE Analysis

KDE plots showing where hit songs cluster for Pop, Latin, and R&B across all 10 features:

Universal across all genres:

• High energy (0.6–0.8), high loudness, near-zero instrumentalness and speechiness, low liveness, short duration (3–4 min)

Genre-specific differences:

• Tempo: Pop/R&B peak at 95–125 BPM; Latin shows a second peak at ~175 BPM (reggaeton)
• Danceability: Latin requires the highest (~0.78), R&B allows more variation
• Valence: Pop and Latin lean happier; R&B spans a wider emotional range
• Acousticness: R&B allows more organic elements than Pop

✅ Summary & Conclusions

General range for popular music

Feature	Direction	Hit Sweet Spot
Danceability	↑ positive	0.70–0.85
Energy	~ neutral	0.60–0.80
Loudness	↑ positive	85–90 (scaled)
Speechiness	↓ negative	< 0.10
Tempo	~ irrelevant	Genre-dependent
Acousticness	↑ slight positive	Slightly above average
Instrumentalness	↓ strong negative	~0.0 (vocals essential)
Liveness	↓ negative	< 0.15 (studio only)
Valence	↑ slight positive	Slightly above average
Duration	↓ negative	3–4 minutes

Genre Sweet Spot Tables — Producer Cheat Sheet

Based on the KDE analysis of hit songs in each of the top 3 genres, here are the optimal feature ranges:

🎤 Pop

Feature	Sweet Spot	Notes
Danceability	0.65–0.80	High but not extreme — steady groove
Energy	0.60–0.85	Energetic, radio-ready feel
Loudness (scaled)	85–90	Loud, compressed mastering
Speechiness	< 0.10	Melody-first — minimal spoken word
Tempo	95–130 BPM	Standard pop range
Acousticness	0.05–0.25	Mostly produced, slight organic touch
Instrumentalness	~0.00	Vocals are essential
Liveness	< 0.15	Studio production only
Valence	0.40–0.75	Leans happy and upbeat
Duration	3.0–3.8 min	Short, replayable tracks

💃 Latin

Feature	Sweet Spot	Notes
Danceability	0.75–0.85	Highest of all genres — dance is mandatory
Energy	0.60–0.85	High energy, rhythm-driven
Loudness (scaled)	85–90	Same loud standard as pop
Speechiness	< 0.10	Singing over speaking
Tempo	95–105 or 170–200 BPM	Bimodal — slow reggaeton and fast salsa/cumbia
Acousticness	0.10–0.35	Slightly more organic than pop
Instrumentalness	~0.00	Always vocal
Liveness	< 0.15	Studio recordings dominate
Valence	0.50–0.80	Happy, feel-good energy
Duration	3.0–4.0 min	Streaming-friendly length

🎶 R&B

Feature	Sweet Spot	Notes
Danceability	0.60–0.80	Wider range — groove matters but isn't everything
Energy	0.50–0.75	Slightly lower — allows smooth, mellow tracks
Loudness (scaled)	80–90	A touch softer than pop/Latin
Speechiness	< 0.15	Slightly higher tolerance (rap-influenced R&B)
Tempo	95–125 BPM	No second peak — steady mid-tempo
Acousticness	0.10–0.45	Most acoustic-friendly genre — organic sound welcome
Instrumentalness	~0.00	Vocals remain essential
Liveness	< 0.15	Studio quality standard
Valence	0.25–0.70	Widest emotional range — moody and happy both work
Duration	3.0–4.2 min	Slightly longer than pop is acceptable

Bottom line: High energy, loud mastering, vocals, and 3–4 minutes are universal. The real genre fingerprint lives in tempo (Latin's bimodal peaks), danceability (Latin demands the most), acousticness (R&B allows the most), and valence (R&B allows the darkest mood).

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👤 Author

Noam Fuchs — Data Science Assignment #1, March 2026