Superstore Sales Analysis and Visualization

1. Objective

This project analyzes Superstore sales data to uncover insights into sales performance across different regions, months, product categories, and customer segments.

Tools Used: SQL for data extraction, Python (Pandas) for data manipulation, and Matplotlib and Seaborn for visualization.

2. Dataset

The "Superstore Sales" dataset is a comprehensive and versatile collection of data, containing information about 9,000 orders.

• Order ID
• Order Date
• Ship Mode (Standard Class, First Class, Second Class, Same Day)
• Customer ID
• Customer Name
• Segment (Consumer, Corporate, Home Office)
• State
• City
• Region (Geographic region with values: West, East, Central, South)
• Category
• Sub-category
• Postal Code
• Product ID
• Product Name
• Sales
• Quantity
• Discount
• Profit

Data Source: dataset-link

3. Data Analysis

Data analysis notebook can be found here: data-analysis.ipynb

SQL Script can be found here: data-extraction.sql