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Project title:
Sales Data Analysis & Visualization
Description:
Analyzed a sales dataset using Python and Pandas to clean, transform, and explore business data. Identified sales trends, top-performing products, customer patterns, and key performance insights. Created visualizations and summary reports to present the findings in a clear and easy-to-understand format.
The network for creativity
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Manufacturing ERP & Production Intelligence Dashboard
The dark plant nav next to the light data canvas keeps the menus from fighting the charts. Putting the packing trend beside stock balance makes the floor numbers readable at a glance.
📊 Superstore Sales Analysis — Complete Business Analytics Project
Turned 10,194 rows of raw retail data into a 3-dashboard Power BI system covering Sales, Customers, and Product Performance.
🔹 Main Sales Dashboard — ₹2.33M revenue tracked, 12.56% profit margin, regional & monthly trend breakdowns
🔹 Customer Analysis Dashboard — 800 unique customers segmented, top revenue drivers, repeat customer tracking
🔹 Product Performance Dashboard — 39K units sold, best-sellers vs. loss-making products flagged by category
Built end-to-end with Excel → MySQL → Power BI, this project turns raw transactional data into decisions: what's selling, who's buying, and where the money's leaking.
Tools: Power BI | Excel | SQL | Data Analysis
51 interactive visuals | 4 dashboards | Real business KPIs
📁 Full project + dataset on GitHub — link in profile
From data to business growth. 🚀
#DataAnalytics #PowerBI #SQL #BusinessIntelligence #DataDrivenDecisions
If you have ever been handed a dataset and wanted to know the integrity of the content, and any key information sitting inside it, you needed a descriptive analysis.
This is a descriptive analysis of Canada Structures, the federal building-footprint product. A file like this is assembled from several sources and file types, and that mix can leave artifacts. The work was looking for those artifacts so the file can be trusted before anyone treats stored height or type as national fields.
National count is 13,760,754, matching the spec. Every Nunavut footprint stores height as zero (11,068). 66.5% of footprints have a positive stored height. 82.4% carry no building type. Shape, size, and province together score 0.42 at guessing source class, against 0.32 from province alone.
