A comprehensive E-commerce Dashboard built with Streamlit, Pandas, NumPy, and Plotly. This interactive web application generates a synthetic dataset to simulate e-commerce sales data, offering insights into revenue trends, customer segments, product performance, and payment methods through dynamic visualizations and filters.
The E-commerce Dashboard is designed to help business analysts and e-commerce managers explore and analyze sales data. It generates a synthetic dataset of e-commerce transactions and provides interactive visualizations, filters, and data export options. The dashboard includes key performance indicators (KPIs), revenue trends, category performance, and raw data exploration, all accessible through a user-friendly interface.
- Interactive Visualizations: Includes line charts, bar charts, pie charts, and data tables powered by Plotly.
- Dynamic Filters: Filter data by date range, category, customer segment, payment method, and minimum rating using sidebar controls.
- Key Performance Indicators (KPIs): Displays total revenue, total orders, average order value, total customers, and average rating.
- Data Export: Download filtered data, summary statistics, and aggregated data as CSV files with timestamps.
- Data Summary: View statistical summaries and top-performing products.
- Raw Data Access: Toggle to display the full filtered dataset.
Below are screenshots of the E-commerce Dashboard:
Displays key performance indicators and sidebar filters.
Shows monthly revenue trends over time.
Illustrates revenue by customer segment.
Displays revenue distribution by payment method.
Includes statistical summaries, top products, and export options.
To run the dashboard locally, follow these steps:
-
Clone the Repository:
git clone https://github.com/AvazAsgarov/streamlit-e-commerce-dashboard.git cd streamlit-e-commerce-dashboard -
Install Dependencies: Ensure you have Python 3.8+ installed, then install the required packages:
pip install -r requirements.txt
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Run the Application: Launch the Streamlit app:
streamlit run e_commerce_streamlit_app.py
The dashboard will open in your default web browser.
- Access the Live App: Visit E-Commerce Dashboard to explore the dashboard online.
- Adjust Filters: Use the sidebar to set a date range, select categories, customer segments, payment methods, and minimum ratings.
- Apply Filters: Click the "Apply Filters" button to update the dashboard with your selections.
- Explore Sections:
- KPIs: View real-time performance metrics.
- Analytics: Analyze revenue trends, category performance, customer segments, and payment methods.
- Data Summary: Check statistical summaries and top products.
- Raw Data: Toggle to view the filtered dataset.
- Export Data: Download CSV files for filtered data, summary statistics, or aggregated data.
The dashboard generates a synthetic dataset with the following features:
- Order Details: Order ID, order date, quantity, unit price, total amount, discount percent, discount amount, final amount, shipping cost.
- Product Information: Category, product name.
- Customer Data: Customer ID, customer segment.
- Transaction Details: Payment method, rating.
- Temporal Data: Generated over the past year from the current date.
The dataset uses realistic distributions (e.g., uniform for prices, random for dates) and includes calculated fields like final amount and discounts.
This dashboard is deployed using Streamlit Community Cloud. Access the live app at E-Commerce Dashboard. The deployment is managed directly from this GitHub repository, requiring only the Python script (e_commerce_dashboard.py) and requirements.txt.
For any questions or suggestions, please open an issue in this repository or connect with me on LinkedIn: Avaz Asgarov.