SQL Operations on E-commerce Dataset (MySQL Workbench Project)

This project showcases a series of SQL operations performed on an E-commerce dataset using MySQL Workbench.
The goal was to explore, analyze, and optimize data to derive actionable insights.

All tasks were executed using SQL queries, and screenshots of query results are included in this repository for reference.

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Tasks Performed

Basic SQL Queries

• Used SELECT, WHERE, ORDER BY, and GROUP BY clauses to explore data.
• Analyzed customer behavior, product categories, and order priorities.

JOINS (INNER, LEFT, RIGHT)

• Joined the orders table with a hypothetical customers table.
• Demonstrated INNER JOIN, LEFT JOIN, and RIGHT JOIN to show different join results and use cases.

Subqueries

• Used subqueries to extract insights such as:
  • Customers with total sales above average.
  • Orders with high profits or discounts.

Aggregate Functions

• Applied SUM(), AVG(), and COUNT() functions to analyze:
  • Sales performance
  • Average profits
  • Number of orders

Views for Analysis

• Created reusable SQL views like:
  • HighPriorityOrders
  • ProductCategorySalesSummary
• Made recurring analysis faster and cleaner.

Indexes for Optimization

• Created indexes on frequently used columns:
  • Customer_Id, Product_Category, Order_Priority, Payment_method
• Demonstrated how indexing improves query performance.
• Included example of multi-column indexes and indexing strategies.

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Dataset Attributes Used

• Order_Date, Time, Aging
• Customer_Id, Gender, Device_Type, Customer_Login_type
• Product_Category, Product, Sales, Quantity, Discount, Profit, Shipping_Cost
• Order_Priority, Payment_method

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Screenshots

Screenshots of all SQL tasks and output results (taken from MySQL Workbench) are included in the /screenshots folder for reference and transparency.

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Note

This is a beginner to intermediate-level project designed to showcase SQL skills on real-world e-commerce-style data.