A Python-based analytics engine that transforms raw transactional data into actionable business insights using a relational SQLite database.
- Database: SQLite (Relational Schema Design)
- Language: Python (Data Orchestration)
- Libraries: Pandas (Analysis), Matplotlib (Visualization)
- Engineered a Relational Schema: Designed a 4-table database system (Categories, Products, Sales, Customers) with enforced referential integrity.
- Complex Data Extraction: Developed multi-join SQL queries to bridge disparate tables and calculate real-time business metrics.
- Growth Analytics: Implemented time-series analysis to visualize daily revenue trends and identify sales seasonality.
- The "Whale" Report: Identified top-tier customers by joining multi-table data to calculate lifetime spend.
- Proactive Automation: Built a background monitoring loop that alerts management when high-velocity products need reordering.
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High-Value Customers: Identified "Whale" customers using a monetary spend analysis (The "Whales" report).
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Inventory Performance: Automated a "Top-Sellers" report to highlight products moving > 3 units per week.
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Category Dominance: Mapped revenue by department, identifying Electronics as the primary growth driver.
