Zepto is a fast-growing grocery delivery app in India known for its promise of delivering groceries and daily essentials to customers within minutes. With a strong focus on speed, convenience, and efficiency, Zepto leverages a network of micro-warehouses and real-time inventory systems to fulfill orders rapidly. The platform offers a wide range of products including fruits, vegetables, packaged foods, personal care items, and more—making it a go-to solution for modern urban grocery shopping.
📊 Zepto PostgreSQL Data Project This project showcases SQL-based data analysis and cleaning on a Zepto product dataset that was web scraped and sourced from Kaggle. The dataset contains detailed product information such as SKU ID, category, name, pricing, availability, weight, and stock status.
I used PostgreSQL to perform data exploration, cleaning, and analysis to uncover valuable business insights, such as stock availability, revenue estimation, pricing strategies, and discount patterns.
🗂️ Table Schema
✅ Key Steps & Highlights 🔍 Data Exploration Counted total rows and viewed a sample of products.
Checked for null values across all columns.
Extracted distinct product categories.
Analyzed stock availability (in-stock vs. out-of-stock).
Identified duplicate product names with multiple SKUs.
🧹 Data Cleaning Removed entries with MRP = 0 or discountedSellingPrice = 0.
Converted prices from paise to rupees for clarity.
📈 Insights & Business Questions Answered Q1: Top 10 best-value products based on highest discount percent.
Q2: High-priced products that are out of stock.
Q3: Estimated revenue generated per product category.
Q4: Products priced over ₹500 with discounts less than 10%.
Q5: Top 5 categories with the highest average discount percentage.
Q6: Categorized products by weight into low, medium, and bulk.
Q7: Total inventory weight grouped by category.
📌 Tools Used PostgreSQL
Dataset scraped from Zepto and hosted on Kaggle 💡 Takeaway This project demonstrates how raw product data from an e-commerce platform like Zepto can be transformed into actionable insights using SQL. It showcases real-world data challenges like cleaning and preprocessing, followed by extracting KPIs relevant to business decisions in inventory and pricing.
This project was completed as part of my continuous learning process to strengthen my skills in SQL, data analysis, and real-world business problem solving. ❤️