A comprehensive sales dashboard application built with React and Flask to analyze e-commerce data from the UCI Machine Learning Repository's Online Retail dataset.
This project provides a full-stack dashboard solution for analyzing e-commerce sales data. It features a React frontend for visualization and a Flask backend with SQL database for data management. Users can view sales metrics, geographic data, and even perform custom SQL queries for deeper analysis.
- Interactive Dashboard: View key metrics like total sales, profits, and profit margins
- Visual Data Analysis: Charts showing monthly sales trends
- Geographic Visualization: World map displaying sales distribution by country
- Top Performers: Tables showing top countries and products by sales and profits
- SQL Query Tool: Custom SQL query interface with results visualization
The application follows a containerized microservices architecture:
-
Frontend Container: React for the UI
- Consumes data from the backend API
- Visualizes data using Recharts and Leaflet
-
Backend Container: Flask with SqLite database
- Provides RESTful API endpoints
- Processes data and handles SQL queries
- Manages the database layer
This project uses the Online Retail dataset from the UCI Machine Learning Repository:
- Source: UCI Machine Learning Repository
- Description: This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
| Endpoint | Method | Description |
|---|---|---|
/api/metrics |
GET | Returns overall metrics like total sales and profits |
/api/monthly-data |
GET | Returns monthly sales and profit trends |
/api/countries-data |
GET | Returns sales and profit data by country |
/api/products-data |
GET | Returns sales and profit data by product |
/api/map-data |
GET | Returns geojson data with sales data |
/api/query-data |
POST | Executes custom SQL queries |
The application uses a relational database with the following tables:
- Region: Geographic data
- Customer: Customer data
- Product: Product details including description and pricing
- Invoice: Transactions data
