This project demonstrates data analysis using Python and SQL on an e-commerce dataset. The goal is to extract key business insights by leveraging MySQL queries and advanced Python libraries like Pandas, Matplotlib, and Seaborn.
SQL-Python Integration: Connected MySQL database with Python using mysql.connector.
Data Extraction & Cleaning: Queried raw data and structured it for analysis.
Business Insights:
Total number of orders placed in different years.
Sales contribution of different product categories.
Customer distribution across cities.
Revenue trends over time.
Visualizations:
Sales distribution by product categories.
Yearly order trends.
Customer location heatmaps.
SQL: MySQL for data extraction
Python: Pandas, NumPy, Matplotlib, Seaborn for analysis & visualization
Some key plots generated in this project:
Sales by Product Category 📈
Customer Distribution Across Cities 🗺️
Yearly Order Trends 📊
Writing efficient SQL queries for business insights.
Using Python for data wrangling & visualization.
Understanding real-world e-commerce sales trends.
Clone this repository:
git clone https://github.com/your-username/python-sql-analysis.git cd python-sql-analysis
Install required libraries:
pip install pandas matplotlib seaborn mysql-connector-python
Connect to your MySQL database by updating credentials in python_query.ipynb.
Run the Jupyter Notebook to execute SQL queries and generate insights.
Combines SQL & Python, making it ideal for data analysis roles.
Showcases business-focused insights, useful for e-commerce analytics.
Demonstrates practical database handling, crucial for real-world applications.
