A full end-to-end data analytics project built using MySQL and Power BI, analyzing 114,000 Spotify tracks across 114 genres to uncover music trends, audio patterns, and popularity insights.
| Page | Description |
|---|---|
 |
Overview — KPIs, top artists, genre treemap |
 |
Genre Analysis — Popularity, audio features by genre |
 |
Track Deep Dive — Top tracks, tempo, valence vs energy |
 |
Track Intelligence Hub — Search & filter any song |
This project takes raw Spotify track data, loads it into a MySQL database, connects it to Power BI, and builds a 4-page interactive dashboard that answers:
- Which artists and genres are most popular?
- What audio features make a song a hit?
- How do genres differ in energy, danceability, and mood?
- How can we search and explore any track instantly?
flowchart TD
subgraph SRC["📦 Source"]
A["Kaggle Dataset<br/>Spotify Tracks Dataset<br/>114,000 rows × 21 cols"]
end
subgraph DB["🗄️ MySQL 8.0"]
B["dataset.csv"]
C["create_table.sql<br/>(schema definition)"]
D["load_data.sql<br/>(bulk load via<br/>secure file path)"]
E[("spotify_db<br/>Spotify Tracks table")]
end
subgraph ETL["🔄 Power Query Transformations"]
F["duration_ms → duration_min"]
G["explicit: 0/1 → Clean/Explicit"]
H["popularity_tier:<br/>Low / Medium / High"]
I["Trim whitespace<br/>(artists, track_name, album_name)"]
J["Remove duplicate track_id"]
K["Fix numeric data types"]
end
subgraph DAX["📐 DAX Measures"]
L["Total Tracks, Avg Popularity,<br/>Explicit %, Total Genres,<br/>Avg Duration Min"]
end
subgraph BI["📊 Power BI Dashboard (Spotify.pbix)"]
M["Page 1: Music Overview<br/>KPIs, Top Artists, Genre Treemap"]
N["Page 2: Genre Analysis<br/>Popularity & Audio Features by Genre"]
O["Page 3: Track & Artist Deep Dive<br/>Top Tracks, Tempo, Valence vs Energy"]
P["Page 4: Track Intelligence Hub<br/>Search & Filter any Track"]
end
A --> B
C -->|1. creates schema| E
B --> D
D -->|2. inserts data into schema| E
E -->|MySQL Connector/NET| F
F --> G --> H --> I --> J --> K
K --> L
L --> M
L --> N
L --> O
L --> P
- Source: Spotify Tracks Dataset (Kaggle)
- File: dataset.csv
- Size: 114,000 rows × 21 columns
- Key columns:
track_name,artists,track_genre,popularity,danceability,energy,tempo,valence,explicit,duration_ms
| Tool | Purpose |
|---|---|
| MySQL 8.0 | Database storage and querying |
| MySQL Workbench | Database management |
| Power BI Desktop | Dashboard creation |
| Power Query | Data transformation |
| DAX | Calculated measures |
git clone https://github.com/vedant-kawale-27/spotify-track-intelligence-powerbi-mysql.git
cd Spotify-Track-IntelligenceOpen create_table.sql in MySQL Workbench and run the query.
Copy dataset.csv to your MySQL secure file path: 'C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/'
Open load_data.sql in MySQL Workbench and run the query.
- Install MySQL Connector/NET from https://dev.mysql.com/downloads/connector/net/
- Open Power BI Desktop
- Home → Get Data → MySQL Database
- Server:
localhost| Database:spotify_db - Select: Database → Enter: User name & Password → Connect
- Click Transform Data
Open Spotify.pbix in Power BI Desktop — all visuals load automatically.
- Converted
duration_ms→duration_min(milliseconds to minutes) - Replaced
explicitvalues:0→Clean,1→Explicit - Added
popularity_tiercolumn: Low / Medium / High - Trimmed whitespace from
artists,track_name,album_name - Removed duplicate
track_identries - Changed data types for all numeric columns
Total Tracks = COUNTROWS('Spotify Tracks')
Avg Popularity = ROUND(AVERAGE('Spotify Tracks'[popularity]), 2)
Explicit Tracks = COUNTROWS(FILTER('Spotify Tracks', 'Spotify Tracks'[explicit] = "Explicit"))
Explicit % = ROUND(DIVIDE([Explicit Tracks], [Total Tracks]) * 100, 1)
Total Genres = DISTINCTCOUNT('Spotify Tracks'[track_genre])
Avg Duration Min = ROUND(AVERAGE('Spotify Tracks'[duration_min]), 2)
- 5 KPI Cards: Total Tracks, Avg Popularity, Explicit %, Total Genres, Avg Duration
- Top 10 Artists by Track Count (Bar Chart)
- Popularity Distribution: Low / Medium / High (Column Chart)
- Explicit vs Clean split (Donut Chart)
- Tracks by Genre (Treemap)
- Avg Popularity by Genre (Horizontal Bar Chart)
- Energy vs Popularity by Genre (Scatter Plot)
- Audio Features by Genre: Danceability, Energy, Valence, Acousticness (Clustered Bar)
- Genre Slicer (Dropdown)
- Top 10 Tracks by Popularity (Table with conditional formatting)
- Danceability vs Popularity (Scatter Plot)
- Avg Tempo by Genre (Column Chart)
- Valence vs Energy mood map (Scatter Plot)
- Slicers: Genre, Explicit, Popularity Tier
- Search by Track Name (Text Slicer)
- Search by Artist (Text Slicer)
- Filter by Genre (List Slicer)
- Explicit Filter (Tile Slicer)
- Popularity Range (Between Slider)
- Results Table: track_name, artists, genre, popularity, explicit, duration_min
- Detail Cards: Avg Danceability, Avg Energy, Avg Tempo, Avg Duration
Spotify-Track-Intelligence/
│
├── dataset/
│ └── dataset.csv
│
├── database/
│ ├── create_table.sql
│ └── load_data.sql
│
├── dashboard/
│ └── Spotify.pbix
| └── Spotify.pdf
│
├── assets/
│ ├── Spotify_page-0001.jpg
│ ├── Spotify_page-0002.jpg
│ ├── Spotify_page-0003.jpg
│ └── Spotify_page-0004.jpg
│
├── README.md
|
└── LICENSE
- Pop-film, K-pop, and Chill are the most popular genres on average
- The Beatles, George Jones, and Stevie Wonder have the most tracks in the dataset
- Only 8.6% of tracks are explicit — most content is clean
- Songs with higher danceability tend to have moderately higher popularity
- Grunge and Sertanejo genres have the highest average tempo
Vedant Kawal
- Built with MySQL + Power BI
- Dataset: Spotify Tracks (Kaggle)
- Tools: MySQL Workbench 8.0, Power BI Desktop
This project is open source under the MIT License.