Analyzed 10,000 rows of raw retail sales data and built a complete analytics system with 11 professional charts and an automated PDF report that runs with a single click. The biggest challenge was identifying why profit margins were inconsistent across different product categories and regions despite strong overall sales numbers. Using Python and Pandas I cleaned and transformed the raw data, removed errors and inconsistencies and built a complete analysis pipeline from scratch. KEY FINDING: Discounts above 40% were generating negative profit across hundreds of transactions. Capping discounts at 20% identified a $50,000 annual profit recovery opportunity the business did not know existed. WHAT WAS DELIVERED: 1. Complete data cleaning and preparation 2. 11 professional charts covering sales 3. trends, profit margins, regional 4. performance and top products 5. Master dashboard image showing all 6. key metrics in one view Automated PDF report generator that 7. runs on any new sales data instantly 8 Business insights written in plain English TOOLS USED: Python, Pandas, Matplotlib, Seaborn, FPDF, Microsoft Excel, SQL PORTFOLIO: github.com/gurleensainitamber15/ data-analytics-portfolio