The project begins by connecting to a SQLite database containing e-commerce tables such as:
customersordersorder_itemsproductspaymentssellers
Using SQL, the project performs:
- Table exploration
- Joins across multiple tables
- Data aggregation
- Category-wise and state-wise summaries
- Payment-level analysis
Example SQL tasks include:
- Total sales by product category
- Number of customers by state
- Total payment value per order
- Most ordered product categories
These queries help extract only the required data, reducing the workload on Python.
After SQL extraction, the data is loaded into Pandas DataFrames for:
- Data cleaning
- Column renaming
- Type conversions
- Sorting and grouping
- Preparing the final dataset for visualization
Pandas ensures smooth transitions between raw SQL output and readable analytics tables.
Visual analytics are created for better insight interpretation. Some visuals include:
- Bar chart of customer count by state
- Bar chart of sales by product category
- Payment distribution charts
- Analysis of order-item relationships
These visualizations give stakeholders a quick understanding of:
- Which states have the most customers
- Which product categories generate the highest revenue
- How payments contribute to overall sales
Each chart helps answer real business questions.
The notebook identifies patterns such as:
- Top-performing product categories
- Regions with maximum customer concentration
- Payment values and methods used
- Sales contribution by category
These insights can be used for:
- Marketing strategy
- Inventory planning
- Product recommendations
- Regional expansion opportunities
The analysis uses multiple relational tables, each representing an important part of the e-commerce business flow:
| Table | Description |
|---|---|
| customers | Customer information including customer location |
| orders | Order-level data such as order ID and order status |
| order_items | Individual product items associated with each order |
| products | Product details including product category |
| payments | Payment information for each order |
| sellers | Information about sellers in the marketplace |
Using SQL, these tables are joined and analyzed collectively.
This project demonstrates:
- Efficient data extraction from relational databases
- Strong SQL query design
- Professional Python data analysis practices
- Visualization & insight generation
- End-to-end analytics workflow used in real companies
It showcases the skills required for:
- Data Analyst
- Business Analyst
- Data Science foundations
- SQL + Python analytics roles
This E-commerce analysis project helps you practice:
- SQL joins, group by, aggregations
- Converting SQL results into Pandas DataFrames
- Creating charts with Matplotlib
- Extracting business insights
- Working with multi-table datasets
The skills demonstrated here are the same used in real analytic dashboards and BI systems.
This project provides a complete analysis pipeline using SQL and Python to answer critical business questions in the e-commerce domain. It highlights how structured data can be transformed into powerful insights that help in business decision-making, optimization, and understanding customer habits.