🛒 SQL for Data Analysis

📘 Project Overview

This project demonstrates how to use SQL for data analysis on an Ecommerce database.
It includes SQL queries to extract, join, and analyze data using MySQL.

🎯 Objective

Use SQL queries to extract and analyze data from a database and learn to manipulate structured data.

🧰 Tools Used

• MySQL (Workbench or Command Line)
• Dataset: Ecommerce_SQL_Database

📂 Tables Included

• Categories
• Products
• Customers
• Orders
• Payments

🧩 Concepts Covered

1. SELECT, WHERE, ORDER BY
2. GROUP BY & HAVING
3. JOINS (INNER, LEFT, RIGHT)
4. Subqueries
5. Aggregate functions (SUM, AVG)
6. Views for analysis
7. Index optimization

📊 Key Insights from Queries

• Identify top-performing categories and products
• Find customers with high spending
• Analyze sales by payment mode and country
• Create reusable analytical views

🚀 Deliverables

• Ecommerce_Data_Analysis.sql — All SQL queries
• Output screenshots (to be added after execution)

🏁 Outcome

Learned how to manipulate and query structured data using SQL for data-driven insights.