📊 Retail Sales SQL Analysis

A complete SQL project analyzing retail sales data using MySQL Workbench. This project demonstrates database creation, data importing, SQL querying, and business insight generation from retail transaction data.

---

🎯 Project Objectives

• Create and manage a MySQL database
• Import retail sales data from CSV
• Perform revenue analysis
• Identify top customers
• Analyze monthly sales trends
• Generate business insights using SQL

---

🛠️ Tools Used

• 🐬 MySQL Workbench
• 📝 SQL
• 💻 VS Code
• 🌐 GitHub
• 📊 Kaggle Dataset

---

📂 Dataset Information

Dataset: Retail Sales Dataset

Author: Mohammad Talib

Source: Kaggle

Dataset Features

• Transaction ID
• Sale Date
• Customer Name
• City
• Product Category
• Product Name
• Quantity
• Unit Price
• Total Amount

---

🗄️ Database Creation

Create Database

CREATE DATABASE retail_sales_db;
USE retail_sales_db;

📸 Database Created

---

🧱 Table Structure

Create Table

CREATE TABLE retail_sales (
    transaction_id INT PRIMARY KEY,
    sale_date DATE,
    customer_name VARCHAR(100),
    city VARCHAR(50),
    product_category VARCHAR(50),
    product_name VARCHAR(100),
    quantity INT,
    unit_price DECIMAL(10,2),
    total_amount DECIMAL(10,2)
);

📸 Table Structure

---

📥 Data Import

The CSV dataset was imported into MySQL Workbench and loaded into the retail_sales table.

📸 Imported Data

---

🔍 SQL Analysis Queries

1️⃣ Monthly Revenue Trend

SELECT
    MONTH(sale_date) AS month,
    SUM(total_amount) AS revenue
FROM retail_sales
GROUP BY month
ORDER BY month;

📸 Output

---

2️⃣ Revenue by Product Category

SELECT
    product_category,
    SUM(total_amount) AS revenue
FROM retail_sales
GROUP BY product_category
ORDER BY revenue DESC;

📸 Output

---

3️⃣ Top 5 Customers by Spending

SELECT
    customer_name,
    SUM(total_amount) AS spending
FROM retail_sales
GROUP BY customer_name
ORDER BY spending DESC
LIMIT 5;

📸 Output

---

📈 Key Insights

💰 Revenue Analysis

• Product categories contributed differently to overall revenue.
• Revenue analysis helps identify high-performing product groups.

📅 Monthly Trends

• Revenue varied across different months.
• Monthly analysis helps understand sales patterns and seasonality.

👥 Customer Analysis

• Top customers generated significantly higher spending compared to others.
• Identifying high-value customers supports targeted business strategies.

---

💡 SQL Concepts Used

• CREATE DATABASE

• Data Import

• CREATE TABLE

• SELECT

• WHERE

• GROUP BY

• ORDER BY

• LIMIT

• COUNT()

• SUM()

• Aggregate Functions

• Business Analytics

---

📁 Project Structure

Retail-Sales-SQL-Analysis/
│
├── retail_sales_dataset.csv
├── SQL_Queries.sql
├── README.md
│
└── screenshots/
    ├── database_created.png
    ├── table_structure.png
    ├── data_imported.png
    ├── monthly_revenue.png
    ├── revenue_by_category.png
    └── top_customers.png

---

🚀 How to Run

1. Clone the Repository

git clone https://github.com/Unnati22p/Retail-Sales-SQL-Analysis.git

2. Open MySQL Workbench

3. Create Database and Table

Run the SQL commands from:

SQL_Queries.sql

4. Import Dataset

Import:

retail_sales_dataset.csv

into the retail_sales table.

5. Execute Queries

Run the SQL queries to generate insights and results.

---

⭐ Project Outcome

This project demonstrates practical SQL skills used in real-world data analysis, including database creation, data management, revenue analysis, customer analysis, and business reporting.

---

👨‍💻 Author

Unnati Patil

Aspiring Data Analyst | SQL | Python | Data Analytics

---

⭐ If you found this project useful, feel free to star the repository!