README.md

E-commerce SQL Data Analysis Project

📌 Overview

This project analyzes e-commerce sales data to extract actionable insights and generate professional visualizations. It showcases data cleaning, exploratory data analysis (EDA), and key trends in sales, revenue, and product performance.

🛠️ Technologies Used

• Python 3.8+
• Pandas
• MySQL
• SQL
• python-dotenv
• Seaborn
• Matplotlib

🔄 Project Workflow

1. Data Cleaning

   • Removed missing values
   • Filtered out negative quantities (returns)
   • Created a revenue column

2. Data Loading

   • Loaded cleaned data into MySQL database

3. Data Analysis

   • Executed advanced SQL queries to analyze:
     • Revenue
     • Customers
     • Products
     • Sales trends

4. Insights Generation

   • Extracted actionable business insights

📁 Project Structure

ecommerce-sql-analysis/
├── data/
│   ├── data.csv
│   └── clean_data.csv
├── assets/
│   ├── revenue_by_country.png
│   ├── top10_products_quantity.png
│   ├── profit_vs_revenue.png
│   ├── sales_over_time.png
│   └── revenue_distribution.png
├── src/
│   ├── clean_data.py
│   ├── load_data.py
│   └── db_connection.py
├── sql/
│   └── queries.sql
├── insights/
│   └── insights.md
├── schema.sql
└── README.md

🚀 How to Run

1. Install dependencies:

pip install -r requirements.txt

2. Create MySQL database:

CREATE DATABASE ecommerce_analysis;

3. Run cleaning script:

python src/clean_data.py

4. Load data:

python src/load_data.py

5. Run SQL queries in MySQL

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📊 Visualizations

1️⃣ Total Revenue by Country

2️⃣ Top 10 Products by Quantity Sold

3️⃣ Profit vs Revenue Scatter

4️⃣ Revenue Over Time

5️⃣ Revenue Distribution per Invoice

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📈 Key Analysis Areas

• Revenue analysis
• Customer segmentation
• Product performance
• Sales trends
• Geographic insights

💡 Key Features

• Real-world dataset
• End-to-end data pipeline
• Advanced SQL queries
• Business insights generation

🎯 Purpose

This project demonstrates practical data analysis skills for real-world freelance and business scenarios.

📝 Notes

• X-axis labels and figure sizes are optimized for readability.
• All figures are saved as high-resolution PNGs.
• Ready to showcase on GitHub, Upwork portfolio, or presentations.

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⭐ Support

If you like this project, feel free to star the repository!