🛒 E-Commerce Analytics & Automation Pipeline

Database: SQLite (Relational Schema Design)
Language: Python (Data Orchestration)
Libraries: Pandas (Analysis), Matplotlib (Visualization)

📌 Project Overview

A Python-based analytics engine that transforms raw transactional data into actionable business insights using a relational SQLite database.

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.

High-Value Customers: Identified "Whale" customers using a monetary spend analysis (The "Whales" report).
Inventory Performance: Automated a "Top-Sellers" report to highlight products moving > 3 units per week.
Category Dominance: Mapped revenue by department, identifying Electronics as the primary growth driver.

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
E-commerce_Project.py		E-commerce_Project.py
README.md		README.md
retail_warehouse.db		retail_warehouse.db
revenue_trends.png		revenue_trends.png
top_sellers.png		top_sellers.png