This project analyzes e-commerce transaction data using Python and advanced data visualization libraries like Plotly, Seaborn, and Altair. It presents interactive dashboards and rich insights into revenue trends, customer segments, product performance, and shipping patterns.
- Interactive choropleth map of revenue by country
- Time series analysis of daily revenue
- B2B vs B2C segment breakdown (pie chart)
- Revenue ranking by product category and top-performing cities
- Correlation matrix of financial indicators
- Clean, annotated notebook structured for storytelling
This project uses a modified version of public e-commerce sales datasets sourced from Kaggle. Columns include:
- Order Date, Ship Date
- Product & Category
- Customer Segment (B2B/B2C)
- Quantity, Amount, Revenue
- Geographic data (City, Country)
- Python (Pandas, NumPy)
- Visualization: Plotly, Seaborn, Altair, Matplotlib
- Notebook: Google Colab
- Version Control: Git & GitHub
| Visualization | Library |
|---|---|
| Choropleth by Country | Plotly |
| Category Revenue Bars | Plotly |
| Revenue Trend Line | Plotly |
| B2B vs B2C Pie Chart | Plotly |
| Top 10 Cities Bar Chart | Plotly |
| Correlation Heatmap | Seaborn |
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Clone the repo:
git clone https://github.com/Indra1806/Ecommerce-Sales-Dashboard.git
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Open the .ipynb file in Google Colab or Jupyter Notebook.
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Upload or mount the dataset.
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Run cells top to bottom and interact with the charts!
Built by Indrasena Reddy 📍 Based in India | Passionate about data storytelling, dashboards, and making insights accessible.
💬 Want to contribute or collaborate on a live dashboard interface or deploy this project online? Fork it and reach out!