SQLDatabase

SQL Portfolio Project — Superstore Order Analysis

This project explores and analyzes a retail order dataset using SQL. The database, df_orders, contains transactional sales data including prices, profits, categories, segments, and regions.

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Project Objectives

• Analyze revenue and profit across regions
• Discover the most profitable product segments and cities
• Understand customer behavior by segment
• Identify monthly revenue trends
• Use SQL to cleanly and clearly extract business insights

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Dataset Overview

Column	Description
Order Id	Unique order identifier
Order Date	Date of purchase
Ship Mode	Shipping method
Segment	Customer segment (e.g., Consumer)
Region	Geographical region
Category	Product category
Sub Category	Product sub-type
Product Id	SKU/Product code
Sale Price	Final price paid after discount
Discount	Discount percentage
Profit	Revenue profit from the sale

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Insights

1. Sales & Profit by Region

select region, round(sum(sale_price), 2) as total_sales, round(sum(profit), 2) as total_profit from df_orders group by region order by total_sales desc;

2. Top 5 Most Profitable Cities

select city, state, round(sum(profit)) as total_profit from df_orders group by city, state order by total_profit limit 5;

3. Average Discount by Category

select category, round(avg(discount), 2) as avg_discount from df_orders group by category;

4. Monthly Sales Trend

select date_format(order_date, '%Y-%m') as month, round(sum(sale_price), 2) as monthly_sales from df_orders group by month order by month;

5. Customer Segment Performance

select segment, count(order_id) as total_orders, round(sum(sale_price), 2) as total_sales, round(sum(profit), 2) as total_profit from df_orders group by segment;