Dave-s-Portfolio
# Grocery Sales Analysis (SQL)
📌 Context
This project analyzes grocery sales data to uncover purchasing trends, highlight top-performing categories, and identify opportunities for inventory optimization.
🛠️ Techniques Used
- SQL: Data cleaning, filtering, aggregation
- Visual storytelling: Screenshots of key queries with annotations
📊 Process
- Data Cleaning
- Removed duplicate transaction IDs
- Standardized product category names
- Filtering
- Focused on transactions within the last 6 months
- Segmented by product category and store location
- Aggregation
- Calculated total sales per category
- Identified top 5 products by revenue
📷 Script Screenshots
Below are key SQL queries used in this project:
Data cleaning: Removing duplicate transaction IDs and standardizing product categories
Filtering: Selecting transactions from the last 6 months and segmenting by location
Aggregation: Calculating total sales per category and identifying top products
Final results showing sales breakdown by category and top revenue drivers
📈 Results
- Top Categories: Fresh produce and dairy accounted for 55% of total sales
- Revenue Drivers: Top 5 products contributed 30% of overall revenue
- Inventory Insight: Seasonal spikes in produce sales suggest opportunities for dynamic stocking
🎯 Teaching Takeaway
This case study shows how cleaning, filtering, and aggregating grocery sales data can reveal actionable insights for inventory planning and category management.
🔗 Links
- SQL Script:
grocery_sales_analysis.sql