Welcome to my E-commerce Sales Analysis project! This notebook combines the power of SQL for data querying and Python for data manipulation and visualization. It's a hands-on exploration of how sales data can reveal business insights over time.
This project answers key questions like:
- 📅 How do monthly sales trend across years?
- 📈 What is the cumulative sales trajectory month by month?
- 🔁 How is the Year-over-Year (YoY) growth looking?
By writing SQL queries and visualizing the results in Python, I turned raw transactional data into clear, actionable insights.
Python+SQL_Ecommerce.ipynb– The main Jupyter notebookrequirements.txt– List of Python libraries usedREADME.md– You’re reading it!
| Tool | Purpose |
|---|---|
| MySQL | Writing queries for data analysis |
| SQLAlchemy | Connecting MySQL to Python |
| Pandas | Data manipulation |
| Seaborn | Data visualization (line/bar plots) |
| Matplotlib | Plot styling and customization |
| Jupyter | Interactive notebook environment |
- Month-by-month sales performance per year
- Cumulative sales tracking to monitor business growth
- YoY growth to compare sales performance annually
- Clean visualizations that speak louder than rows of numbers
- Clone this repo:
git clone https://github.com/Nikhil3107jaiswal/Python-SQL_Ecommerce_analysis.git