# 📊 Superstore Sales SQL Analysis

## 📌 Project Overview

This project analyzes Superstore sales data using Python, Pandas, SQLite, and SQL.

The objective is to identify important business trends, sales performance, profitable regions, top products, and customer behavior.

## 🎯 Objectives

- Analyze total sales and profit

- Identify top-performing products

- Compare sales across regions

- Analyze category performance

- Calculate monthly sales trends

- Identify top customers

- Analyze profit by region

- Calculate average order value

## 🛠️ Technologies Used

- Python

- Pandas

- NumPy

- SQLite

- SQL

- Matplotlib

- Seaborn

- Jupyter Notebook

## 📂 Project Structure

Superstore-SQL-Analysis/

│

├── data/

│   ├── superstore\_sales.csv

│   └── superstore.db

│

├── notebooks/

│   └── superstore\_sql\_analysis.ipynb

│

├── sql/

│   └── sales\_analysis\_queries.sql

│

├── visuals/

│   ├── monthly\_sales.png

│   ├── sales\_by\_category.png

│   ├── sales\_by\_region.png

│   ├── top\_products.png

│   └── profit\_by\_region.png

│

├── README.md

├── requirements.txt

└── .gitignore