This project is a SQL-based exploratory data analysis (EDA) of a Zepto-like quick commerce dataset. The goal is to extract meaningful business insights related to pricing, revenue, product availability, and category performance using SQL queries.
The project simulates real-world retail analytics problems commonly faced in e-commerce and quick commerce platforms.
- Analyze product pricing efficiency across categories
- Identify top revenue-generating categories
- Evaluate stock availability and out-of-stock trends
- Understand discount impact on pricing
- Build business-oriented SQL insights from raw data
The dataset contains product-level information including:
- Product category
- Discounted selling price
- Quantity
- Weight in grams
- Stock availability (in stock / out of stock)
- Discount percentage
- Which categories provide the best price efficiency per gram?
- Which categories generate the highest estimated revenue?
- Which categories have the highest out-of-stock ratio?
- How does discount percentage affect product pricing?
- Which categories are most valuable for platform growth?
- PostgreSQL / SQL
- VS Code
- Git & GitHub
- Certain categories contribute significantly higher revenue compared to others
- High discount products are not always the most cost-efficient
- Some categories show consistently high out-of-stock ratios
- Price efficiency varies widely across product categories
Zepto-SQL-project/
│
├── data
├── sql
├── README.md
├── insights.md
- Clone the repository
- Import dataset into PostgreSQL
- Run SQL queries from 'sql'
- Explore insights and modify queries
- Add Power BI / Tableau dashboard
- Include advanced SQL analytics (window functions, ranking)
- Build automated reporting insights