Superstore Sales Analysis

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, we extract actionable insights across product lines, customer segments, and geographic regions.

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🔍 Objectives

• Analyze sales and profit trends across segments, categories, and regions
• Perform SQL-based subgroup analysis to classify products and customers
• Visualize key metrics to support strategic retail decisions

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🛠 Tools Used

• SQL – Business queries, segment analysis, profitability logic
• Python – Data cleaning & visualization
• Libraries: pandas, matplotlib, seaborn
• PowerPoint – Final presentation and summary

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📌 Key Insights

• Canon imageCLASS 2200 is the top-selling product (₹61,000+)
• West region leads in total sales; South lags behind
• Copiers and Phones are highly profitable; Tables incur losses
• Consumers drive the most revenue among segments
• High discounts don't always lead to high sales or profit

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📁 Files Included

• Cleaned_Superstore.csv – Cleaned and preprocessed dataset
• Superstore_sales_data_analysis.ipynb – Python notebook with insights and graphs
• Customer_And_Products.sql – SQL queries for business analysis
• Superstore_Sales_Analysis_ppt.pdf – Presentation slide deck

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✅ Conclusion

This project provides a clear breakdown of what drives Superstore sales and profitability. By combining SQL logic with data visualization, we identified top-performing products, regions needing improvement, and the impact of discounting strategies. These findings can support smarter inventory planning, customer targeting, and promotional strategies.