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Amazon Sales Performance Dashboard: SQL to Excel Pipeline
This project showcases an end-to-end data analytics pipeline designed to clean, structure, and visualize massive retail transactional data.
The Raw Data: Processed over 113,000 messy e-commerce records tracking sales, logistics, and customer details.
The SQL Cleanup: Cleaned the database by removing duplicate transactions to protect metric accuracy, standardizing product categories, and handling missing values.
The Staging Step: Created structured relational views to ensure fast, lag-free dashboard performance.
The Interactive Excel Dashboard: Developed a premium, dark-themed interactive Excel interface tracking $75.40M in Revenue, 113.7K orders, top states (like Maharashtra), and critical metrics like the 4.95% cancellation rate. Slicers allow users to instantly filter by product size, month, and order status.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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For high ticket specifically, zero friction can work against you at the wrong step. capture & routing should be instant but the moment a real buyer expects a human usually right after qualifying, that is where automation should hand off not close the loop entirely
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Security Support Information System (SSIS)
The Security Support Information System (SSIS) is an end-to-end Business Intelligence solution developed to address operational visibility and decision-making challenges in the private security industry.
The project involved identifying a real business problem, cleaning and transforming operational datasets using Python (Pandas), designing a relational MySQL database, implementing SQL scripts, and developing interactive Power BI dashboards.
The final solution delivers Executive, Workforce, Operations, and Commercial Analytics dashboards featuring KPI reporting, DAX calculations, and interactive visualizations to help managers make informed, data-driven decisions.
Tools Used: Python (Pandas), MySQL, SQL, Microsoft Power BI, DAX, Excel.
