This project analyzes retail sales data from a Superstore to uncover performance trends, high-profit categories, and growth opportunities. Using SQL for business-driven queries and Python for visual analytics, the analysis highlights actionable insights across product lines, customer segments, and geographic regions.
Handled the full analytics pipeline: SQL-based data extraction, EDA in Python, metric definitions, visualization creation, and business insights summarized in a PPT for stakeholders.
- Analyze sales and profit trends across segments, categories, and regions
- Perform SQL-based subgroup analysis to classify customers and products
- Visualize key metrics to support strategic retail decisions
- SQL: Business logic, segmentation, profitability analysis
- Python: Data cleaning & visualization
- Libraries: pandas, matplotlib, seaborn
- Presentation: Microsoft PowerPoint
- Canon imageCLASS 2200 is the top-selling product (₹61,000+)
- West region achieves the highest total sales; South underperforms
- Copiers and Phones are highly profitable, but Tables show losses
- Consumers contribute the highest revenue
- High discounts ≠ high sales or high profit — discounting strategy needs refinement
Cleaned_Superstore.csv– Cleaned and preprocessed datasetSuperstore_sales_data_analysis.ipynb– Python notebook with insights and graphsCustomer_And_Products.sql– SQL queries for business analysisSuperstore_Sales_Analysis_ppt.pdf– Executive summary presentation
This project provides a clear breakdown of what drives Superstore sales and profitability.
By combining SQL logic with visual analytics, we identified:
- Top-performing products and segments
- Regions that require strategic improvement
- The impact of discounting strategies
- Opportunities for better inventory planning and customer targeting
These insights can help businesses improve decision-making, optimize marketing strategies, and enhance profitability.