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📊 End-to-End Retail Sales Analytics

📌 Project Overview

This project demonstrates a full-cycle data analysis workflow—from raw data extraction to actionable business insights. Using a real-world Superstore Retail Dataset, I engineered a pipeline to load data into a MySQL database, performed complex SQL analysis, and visualized the results using Python (Matplotlib/Seaborn).

The goal was to answer critical business questions regarding sales trends, profit margins, and regional performance.

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🛠️ Tech Stack

• Language: Python 3.x
• Database: MySQL
• Libraries: Pandas, SQLAlchemy, PyMySQL, Matplotlib, Seaborn
• Tools: VS Code, Jupyter Notebook

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📂 Project Structure

Retail-Sales-SQL-Project/
│
├── data/
│   └── superstore_sales.csv     # Raw dataset
├── scripts/
│   ├── data_loader.py           # ETL Script: CSV -> MySQL
│   ├── analysis.sql             # 20+ Analytical Queries
│   └── dashboard.py             # Python Visualization Script
├── images/
│   └── (Screenshots of charts)  # Generated visual insights
├── README.md                    # Project Documentation
├── requirements.txt             # Dependencies
└── SQL_SCENARIOS.md             # Detailed breakdown of SQL queries

📈 Dashboard Insights Here are the key visualizations generated by the dashboard.py script.

1. Monthly Sales Trend

Visualizing revenue growth over time to identify seasonality.

2. Sales by Category

Comparing performance across major product categories.

3. Profit Distribution by Region

Understanding which regions are most profitable.

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🚀 How to Run

1. Clone the repository from GitHub:

git clone https://github.com/sanaurrehmanarain/Retail-Sales-Analytics-SQL.git

2. Install dependencies:

pip install -r requirements.txt

3. Setup Database:

   -Create a MySQL database named sales_db.

   -Update database credentials in data_loader.py.

4. Run the ETL Script:

python data_loader.py

5. Visualize Data:

python dashboard.py

📧 Contact

For any questions or feedback, feel free to reach out via LinkedIn.