A comprehensive SQL-based data analysis project exploring Spotify music data with exploratory data analysis (EDA) and queries categorized by difficulty levels (Easy, Medium, and Advanced).

• Project Overview
• Features
• Database Schema
• Project Structure
• Technologies Used
• Getting Started
• Query Categories
• Analysis Highlights
• Dataset

This project demonstrates SQL skills by analyzing Spotify music data including:

• Audio features (danceability, energy, tempo, etc.)
• Streaming statistics (Spotify vs YouTube)
• Artist and album analytics
• Track performance metrics

The analysis is organized into three difficulty levels to showcase progressive SQL techniques from basic queries to advanced window functions.

• Exploratory Data Analysis (EDA): Initial data exploration and cleaning
• Easy Queries: Basic SELECT, WHERE, GROUP BY operations
• Medium Queries: Aggregations, conditional logic, and joins
• Advanced Queries: Window functions, CTEs, subqueries, and complex analytics
• Data Cleaning: Removal of invalid records (zero duration tracks)

The spotify table contains the following columns:

Spotify_Data_Analysis_SQL/
│
├── spotify_data_analysis.sql    # Main SQL script with all queries
├── cleaned_data.csv             # Dataset file
├── spotify-piano-and-headphones-psvd4nwl5u4foka8.jpg  # Project image
└── README.md                     # Project documentation

• SQL (PostgreSQL/MySQL compatible)
• CSV data format
• SQL features used:
  • Basic SQL operations (SELECT, WHERE, GROUP BY, ORDER BY)
  • Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
  • Window functions (DENSE_RANK, SUM OVER)
  • Common Table Expressions (CTEs)
  • Subqueries
  • Conditional logic (CASE statements)
  • Data type handling

• SQL database system (PostgreSQL)
• CSV file import capability
• SQL client/interface (pgAdmin)

1. Clone the repository

   git clone https://github.com/Sd7070/Spotify_Data_Analysis_SQL.git
   cd Spotify_Data_Analysis_SQL

2. Import the dataset

   • Import cleaned_data.csv into your database
   • Create the table structure using the CREATE TABLE statement in the SQL file
   • Load the CSV data into the spotify table

3. Run the SQL queries

   • Open spotify_data_analysis.sql in your SQL client
   • Execute queries section by section
   • Or run the entire script

-- Create the table (from spotify_data_analysis.sql)
CREATE TABLE spotify (
    artist VARCHAR(255),
    track VARCHAR(255),
    -- ... (see full schema in SQL file)
);

1. High-Streaming Tracks: Find tracks with more than 1 billion streams
2. Album-Artist Mapping: List all albums with their respective artists
3. Licensed Content Comments: Calculate total comments for licensed tracks
4. Single Albums: Find all tracks belonging to single-type albums
5. Artist Track Count: Count total tracks by each artist

Skills Demonstrated: Basic SELECT, WHERE, GROUP BY, aggregate functions

6. Album Danceability: Calculate average danceability per album
7. Top Energy Tracks: Find top 5 tracks with highest energy values
8. Official Video Metrics: List tracks with views and likes for official videos
9. Album Views: Calculate total views for tracks in each album
10. Platform Comparison: Find tracks streamed more on Spotify than YouTube

Skills Demonstrated: Aggregations, GROUP BY with multiple columns, conditional aggregation (CASE statements)

11. Top Tracks per Artist: Find top 3 most-viewed tracks for each artist using window functions
12. Above-Average Liveness: Find tracks with liveness above the dataset average using subqueries
13. Energy Range Analysis: Calculate energy difference (max-min) per album using CTEs
14. Energy-Liveness Ratio: Find tracks where energy-to-liveness ratio > 1.2
15. Cumulative Likes: Calculate cumulative sum of likes ordered by views using window functions

Skills Demonstrated: Window functions (DENSE_RANK, SUM OVER), CTEs (WITH clauses), subqueries, advanced analytics

• Data Exploration: Comprehensive EDA including data quality checks and cleaning
• Performance Metrics: Analysis of streaming, views, likes, and comments
• Audio Feature Analysis: Insights into musical characteristics (energy, danceability, liveness)
• Platform Analytics: Comparison between Spotify and YouTube performance
• Ranking Analysis: Top-performing tracks and artists using advanced ranking functions
• Statistical Analysis: Average calculations, cumulative sums, and comparative metrics

• File: cleaned_data.csv
• Format: CSV with headers
• Content: Spotify music data with audio features and streaming metrics
• Data Quality: Pre-cleaned dataset (zero-duration tracks removed)

• All queries are compatible with PostgreSQL, MySQL, and most SQL databases
• Some syntax may need minor adjustments based on your database system
• The dataset should be imported before running the analysis queries
• Queries are organized sequentially and can be run independently

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

This project is open source and available for educational purposes.

⭐ If you find this project helpful, please consider giving it a star!