🛒 Zepto Q-commerce SQL Analysis

Project Overview
This project simulates a real-world data analyst workflow using Zepto’s Q-commerce inventory dataset. The goal is to explore, clean, and analyze inventory data with SQL and generate actionable business insights.

Purpose

• Build a strong portfolio project for data analyst roles
• Learn SQL hands-on with real e-commerce data
• Prepare for interviews in retail, e-commerce, or product analytics

Dataset Highlights

• Source: Kaggle, scraped from Zepto’s official product listings
• Key columns:
  • SKU_ID: Unique identifier for each product
  • Name: Product name
  • Category: Product category (Fruits, Snacks, Beverages, etc.)
  • MRP: Maximum Retail Price in ₹
  • Discount_Percent: Discount applied on MRP
  • Discounted_Sp: Price after discount in ₹
  • Availability: Units available
  • weightINGms: Product weight in grams
  • OutOfStock: Stock availability (True/False)
  • Quantity: Units per package

Workflow

1. Database & Table Creation – Created SQL table with proper data types
2. Data Import – Imported CSV into PostgreSQL using pgAdmin
3. Data Exploration – Checked dataset structure, null values, categories, stock counts, and duplicates
4. Data Cleaning – Removed invalid rows and converted prices from paise to rupees
5. Analysis & Insights –
   • Top products by discount percentage
   • High-MRP products out of stock
   • Estimated revenue per category
   • Price-per-gram analysis for value products
   • Grouped products by weight

Technologies Used

• SQL (PostgreSQL)
• pgAdmin
• CSV data handling

Key Learnings

• Handling real-world e-commerce data
• Writing business-focused SQL queries
• Extracting insights to support business decisions