Superstore Sales Analysis & Interactive Power BI Dashboard
To analyze retail sales data and uncover key revenue drivers, loss-making areas, and customer behavior patterns in order to support data-driven business decisions.
Source: Sample Superstore Dataset (Kaggle)
Size: ~10,000 retail transactions
Key Features: Sales, Profit, Discount, Category, Sub-Category, Region, Segment, Order Date
Python: Pandas, NumPy, Matplotlib
Jupyter Notebook for data analysis and EDA
Power BI for interactive dashboards
Git & GitHub for version control and project hosting
Which regions and categories generate the highest sales and profit?
Which products show high sales but low or negative profitability?
How do discount levels impact profit margins?
Which customer segments contribute the most to overall revenue?
How do sales and profit trends change over time?
The Technology category generates the highest overall profit.
Higher discount levels are strongly associated with reduced profitability.
Several sub-categories exhibit high sales volumes but consistent losses.
Corporate and Consumer segments are the primary contributors to revenue.
Reassess discount strategies for products and sub-categories with persistent losses.
Prioritize marketing and inventory planning for high-margin categories and segments.
Review pricing, sourcing, or operational costs for consistently unprofitable products.
The Power BI dashboard provides an interactive analysis of sales performance, profitability, and customer insights, enabling stakeholders to explore trends across regions, categories, and time periods.
superstore-sales-analysis/ ├── data/ ├── notebooks/ ├── dashboard/ ├── README.md └── requirements.txt