An interactive analytics dashboard built using Streamlit and Plotly, designed to evaluate and explore retail sales, profit, discount trends, and regional performance using the SuperStore dataset.
This dashboard was created specifically to explore the SuperStore dataset through interactive diagnostics, visual analytics, and scenario simulations. It serves as a modular, data-driven interface to analyze performance across sales, profit, discount behavior, and regional contributions.
The project highlights:
- Building custom analytics dashboards with real business context
- Creating repeatable evaluation workflows using filters, visual tools, and simulations
- Delivering insight-driven interfaces adaptable to decision-making settings
Filters:
Use the sidebar to filter by Region, Category, Sales Range, and Order Date to customize the data view.
Guided Tour:
Enable the "📖 Guided Tour" checkbox in the sidebar for a step-by-step walkthrough of all dashboard sections.
Special Features:
- 📊 Dual-Axis chart comparing Sales vs Profit by Category
- 🎛️ What-If Simulator to predict profit under different scenarios
- 🧾 Export filtered data to CSV or Excel
- 🌲 Interactive Treemap, 🔗 Correlation Heatmap, and 📏 dynamic Benchmark Comparisons
All visualizations are fully interactive and respond in real-time to your filters.
| Feature | Description |
|---|---|
| 📖 Guided Tour | Step-by-step assistant walking through each section |
| 🔍 Interactive Filters | Region, Category, Sales Range, Date filtering |
| 📈 KPI Metrics | Sales, Profit, Order Count, Profit Margin |
| 📊 Dual-Axis Chart | Compare Sales vs Profit across Categories |
| 🌍 Regional + City Drilldown | Explore performance at geographic levels |
| 🌲 Treemap View | Hierarchical sales view by Category & Sub-Category |
| 🎛️ What-If Simulator | Adjust variables to predict profit outcomes |
| 🧾 Raw Data + Export | Custom column views with CSV/Excel export |
| 📏 Benchmarking | Compare real performance vs dynamic industry targets |
| 📈 Trendline Analysis | Visualize discount impact using OLS regression |
| 🔗 Correlation Heatmap | Understand relationships across metrics |
- Python / Streamlit
- Plotly Express
- Pandas, NumPy
- Scikit-learn
- Statsmodels
- Matplotlib, XlsxWriter
git clone https://github.com/ritunjaym/retail-analytics-dashboard.git
cd retail-analytics-dashboard
pip install -r requirements.txt
streamlit run app.py