This project focuses on analyzing a Music Store Database using SQL to understand customer purchasing behavior, sales trends, music preferences, and regional performance.
The objective of this project is to transform raw business data into meaningful insights by solving real-world business questions using SQL queries, database relationships, and analytical techniques.
- Analyze customer purchasing behavior
- Identify top-performing customers and countries
- Understand music genre preferences across regions
- Discover sales trends and customer spending patterns
- Generate meaningful business insights using SQL
- SQL
- MySQL
- Relational Database Design
- Data Analysis
- JOIN Operations
- GROUP BY & HAVING
- Aggregate Functions (
SUM,COUNT,AVG) - ORDER BY & LIMIT
- Subqueries
- Common Table Expressions (CTEs)
- Window Functions (
RANK()) - Data Aggregation Techniques
- Who is the senior-most employee based on job title?
- Which countries have the highest number of invoices?
- What are the top invoice values?
- Which city generates the highest revenue?
- Who is the best customer based on total spending?
- Who are the Rock music listeners?
- Which artists have written the most Rock music?
- Which songs are longer than the average duration?
- How much amount is spent by each customer on artists?
- What is the most popular music genre for each country?
- Who is the top customer in each country based on spending?
- Rock music is the most popular genre across the majority of countries.
- Countries like the USA, Canada, and Brazil show strong customer activity and purchasing behavior.
- High-value customers contribute significantly to overall sales revenue.
- Customer spending behavior differs across countries and artists.
- Some regions show unique music preferences, highlighting regional variations in taste.
- Managing multiple tables and creating accurate JOIN relationships was challenging.
- Understanding database relationships and selecting correct columns took time.
- Implementing CTEs and Window Functions (
RANK()) was initially difficult but useful for solving advanced queries. - Writing long SQL queries and debugging errors improved problem-solving skills.
music-store-sql-analysis/
│── dataset/
│── screenshots/
│── Music_Store_SQL_Project.sql
│── Music_Store_Presentation.pdf
│── README.md
- Database Schema Diagram
- Best Customer Analysis
- Rock Music Listener Analysis
- Most Popular Genre by Country
- Top Customer by Country
- Project Challenges & Learnings
This project improved my understanding of:
- SQL Query Writing
- Database Relationships
- Business Data Analysis
- Query Optimization
- Problem Solving with SQL
- Turning Raw Data into Actionable Insights
Special thanks to:
Deepraj Vadhwane sir — for continuous guidance and support throughout this project.
Leelavardhan Raj sir — for mentorship, encouragement, and practical learning support.
Sujit Kumar Padhan B.Tech CSE Student | SQL | Data Analytics | Database Management
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