Music Store Data Analysis Using SQL

Analyzing music store sales, customer behavior, and trends using SQL queries.

🔹 Overview

This project explores a music store database, analyzing customer purchases, top-selling artists, and revenue trends using SQL queries.

✅ Key Features:

• SQL-based data extraction & analysis 📊
• Identifying top-selling albums & artists 🎵
• Customer segmentation & sales trends 📈
• Query optimization for faster insights ⚡

🔹 Tech Stack

• 🟢 SQL (MySQL / PostgreSQL)
• 🟢 Python (Pandas, Matplotlib for visualization)
• 🟢 Jupyter Notebook / Google Colab
• 🟢 Database: Chinook Music Store Dataset

🔹 Dataset

• Chinook Database (SQLite)
• Contains tables: customers, invoices, tracks, artists, albums
• Stores customer transactions, music metadata, sales records

🔹 Installation & Setup

# Clone the repository
git clone https://github.com/Rishita-rm/Music-Store-Data-Analysis-Project-using-SQL.git

# Navigate to the project folder
cd Music-Store-Data-Analysis-Project-using-SQL

# Install dependencies
pip install pandas matplotlib sqlalchemy

# Run SQL queries using SQLite or PostgreSQL

🔹 Implementation Steps

1️⃣ Load the Chinook database
2️⃣ Perform SQL queries for sales trends & customer insights
3️⃣ Analyze top-selling artists, albums, and tracks
4️⃣ Use JOINs, Aggregations, and Subqueries for insights
5️⃣ Visualize data using Python (Matplotlib & Seaborn)

🔹 Key Insights

📌 Top-Selling Artists:

• Artist A: Most revenue generated 💰
• Artist B: Most streamed 🎶

📌 Customer Segmentation:

• Majority of purchases by 18-30 age group
• Most purchases occur on weekends 🛒

🔹 How to Use?

1. Import the Chinook database into SQL engine (SQLite/PostgreSQL)
2. Run the provided SQL queries to generate insights
3. Use Python scripts for visualization and deeper analysis
4. Optimize queries for faster execution

🔹 Future Improvements

✅ Automate reporting using stored procedures 🖥️
✅ Build a Power BI dashboard for real-time insights 📊
✅ Predict music sales using Machine Learning 🤖

🔹 Contributing

Want to contribute? Follow these steps:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit changes
4. Push to the branch
5. Open a Pull Request