An interactive sales analytics dashboard built with Python, Pandas, Plotly, and Streamlit — deployed live on Streamlit Cloud.
🔗 Live Dashboard: https://niksm2003-superstore-sales.streamlit.app
This project performs end-to-end exploratory data analysis (EDA) on the Kaggle Superstore Sales dataset (9,994 orders, 4 regions, 3 product categories, 2014–2017) and presents the findings as an interactive, filterable dashboard.
The dashboard allows users to filter by Region, Product Category, and Year — all charts and KPIs update dynamically based on the selected filters.
Real-time KPI cards showing:
- Total Revenue
- Net Profit and Profit Ratio
- Total Orders
- Average Profit Margin
- Revenue vs Profit comparison across Furniture, Office Supplies, Technology
- Revenue breakdown across all 17 Sub-Categories
- Revenue and Profit by Region (East, West, Central, South)
- Revenue share by Region (donut chart)
- Revenue by Customer Segment (Consumer, Corporate, Home Office)
- Monthly Revenue and Profit trend across 2014–2017
- Identifies seasonal peaks and growth patterns
- Top 10 most profitable products
- Scatter plot showing the relationship between discount rate and profit
- Key finding: orders with discounts above 30% result in losses 80%+ of the time
- Revenue by Shipping Mode (Standard, Second Class, First Class, Same Day)
- Average fulfilment time by Shipping Mode
- Technology is the highest revenue category ($836K) but Office Supplies has the strongest profit margins
- The West region leads in total revenue; the East region delivers the highest profit
- Phones and Chairs are the top two sub-categories by revenue ($330K and $328K respectively)
- Heavy discounting (>30%) is the primary driver of margin erosion — particularly in the Furniture category
- Standard Class shipping accounts for the majority of orders but Same Day shipping has the fastest fulfilment time
- Source: Kaggle Superstore Sales Dataset
- Download: https://www.kaggle.com/datasets/vivek468/superstore-dataset-final
- Size: 9,994 orders · 21 features
- Period: January 2014 – December 2017
- Regions: East, West, Central, South
- Categories: Furniture, Office Supplies, Technology
| Tool | Purpose |
|---|---|
| Python | Core programming language |
| Pandas | Data loading, cleaning, aggregation |
| Plotly | Interactive charts and visualisations |
| Streamlit | Dashboard framework and deployment |
| Streamlit Cloud | Free hosting and deployment |
superstore-eda/ │ ├── app.py # Main Streamlit dashboard application │ # - Top filter bar (Region, Category, Year) │ # - KPI metric cards │ # - Category performance charts │ # - Regional analysis (bar + donut charts) │ # - Monthly sales trend (line chart) │ # - Product performance (bar chart) │ # - Discount impact analysis (scatter plot) │ # - Shipping analysis (bar charts) │ ├── Sample - Superstore.csv # Dataset (9,994 orders, 21 features) ├── requirements.txt # Python dependencies └── README.md # Project documentation
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Clone this repository git clone https://github.com/Niksm2003/superstore-eda.git
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Navigate to the project folder cd superstore-eda
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Install dependencies pip install -r requirements.txt
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Run the dashboard streamlit run app.py
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Open your browser at
http://localhost:8501
streamlit pandas plotly openpyxl
🔗 https://niksm2003-superstore-sales.streamlit.app
Built by Nikhil Mishra · github.com/Niksm2003