Interactive sales analysis dashboard built with Python, Plotly, and Dash.
Includes KPIs, filters, and dynamic visualizations.
🎬 Dashboard Demo
This project is a complete end-to-end Sales Analysis and Interactive Dashboard built with
Python, Pandas, Plotly, and Dash, using the Sample Superstore dataset (2014–2017).
It includes:
- Data cleaning and preprocessing
- Exploratory data analysis (EDA)
- Visualizations in Seaborn and Plotly
- Interactive dashboard with live KPIs
- Source: Superstore Dataset (Kaggle)
sales_analysis_dashboard/ ├─ dash/ │ └─ app.py ├─ data/ │ └─ Sample - Superstore.csv ├─ notebooks/ │ └─ sales_analysis.ipynb ├─ plots/ │ ├─ sales_by_category_plotly.png │ ├─ sales_by_category_seaborn.png │ ├─ sales_by_region_plotly.png │ ├─ sales_by_region_seaborn.png │ ├─ monthly_sales_trend_plotly.png │ ├─ monthly_sales_trend_seaborn.png │ ├─ profit_vs_discount_plotly.png │ ├─ profit_vs_discount_seaborn.png │ ├─ top_10_cities_by_sales_plotly.png │ └─ top_10_cities_by_sales_seaborn.png ├─ dashboard/ │ ├─ supersales_dashboard_static.png │ ├─ supersales_dashboard_static_light.png │ └─ superstore_sales_dashboard.gif ├─ requirements.txt ├─ .gitignore └─ README.md
pip install -r requirements.txt
jupyter notebook notebooks/sales_analysis.ipynb
▶️ Run the Dashboard Locally
python dash/app.py
Then open your browser and go to
👉 http://127.0.0.1:8050📊 Dashboard KPIs
| KPI | Value |
|---|---|
| 💰 Total Sales | $2,297,201 |
| 💵 Total Profit | $286,397 |
| 📈 Profit Margin | 12.5% |
| 📦 Orders | 9,994 |
| 💸 Average Discount | 15.6% |
💡 Key Insights
Technology category has the highest profit margin (~17.4%). Furniture performs the worst (~2.5% margin). West region drives the most profit (~14.9%). Discounts show a strong negative correlation with profit. Sales trend increases steadily through 2017, with Q4 peaks.
🎨 Dashboard Features
✨ Modern dark theme with teal-accented palette 📦 Dynamic KPIs cards for instant overview 🎚️ Filters for Category, Region, and Month Range 📊 Interactive Plotly charts (hover, zoom, filter) 📈 Real-time recalculation of metrics and visuals 📁 Exported static charts with Seaborn and Plotly
🧠 Tech Stack
| Area | Tools |
|---|---|
| Data Handling | pandas, numpy |
| Visualization | plotly, dash, seaborn, matplotlib |
| Notebook | jupyter, pyarrow |
| Dashboard | Dash Core Components, Dash HTML Components |
| Theme | Custom dark + teal palette |
📸 Visuals
| Library | Example |
|---|---|
| Plotly | Interactive visuals (used in Dash app) |
| Seaborn | Static comparisons and EDA |
| Matplotlib | Trend validation & complementary plots |
🧩 Next Steps
✅ Forecasting models (Prophet / ARIMA) ✅ Customer segmentation analysis ✅ Dynamic filters by state and product type ✅ Deployment to cloud (Render / AWS / Hugging Face Spaces)
🛠️ Installation Requirements
Dependencies listed in requirements.txt: pandas>=2.0 numpy>=1.24 plotly>=5.20 dash>=2.14 seaborn>=0.13 matplotlib>=3.8 pyarrow>=15.0
🧑💻 Author
Mónica Venzor 📍 Data Analyst Jr | SQL | Excel | Power BI | Python | Data Visualization | Machine Learning Enthusiast 🔗LinkedIn | — GitHub
📜 License
This project is licensed under the MIT License — free for educational and personal use.