Tools: Python · Pandas · Matplotlib · Seaborn · Power BI Dataset: Superstore Sales Dataset (Kaggle) — 9,994 records Status: ✅ Complete
A retail company wants to understand which products, regions, and categories drive revenue. This analysis answers:
- Which categories generate the most sales?
- Which regions perform best?
- What seasonal patterns exist?
- Which products drive the most revenue?
- Data Cleaning — fixed date formats, handled missing values
- EDA — Python (Pandas, Matplotlib, Seaborn)
- Dashboard — Power BI interactive dashboard
- Technology leads all categories with $836K in total sales
- West region generates the highest sales at $725K
- November is the peak sales month — strong year-end surge
- Canon imageCLASS 2200 is the top revenue product
- Average shipping time is 3.96 days across all orders
| File | Description |
|---|---|
notebooks/analysis.ipynb |
Full Python analysis |
outputs/chart1_category.png |
Sales by category |
outputs/chart2_monthly_trend.png |
Monthly sales trend |
outputs/chart3_regional.png |
Regional performance |
outputs/chart4_top_products.png |
Top 10 products |
outputs/chart5_subcat_sales.png |
Sales by sub-category |
outputs/dashboard_screenshot.png |
Power BI dashboard |
- Focus inventory on Technology — highest revenue category
- Prioritise West & East regions — contribute 60%+ of total sales
- Prepare for Q4 surge — November peak is consistent year on year