https://github.com/gagan8605/zepto_sql_analysis
This project explores and analyzes the inventory data of Zepto, a rapidly growing 10-minute grocery delivery platform in India. The dataset contains over 3,000+ SKUs across key product categories such as Fruits & Vegetables, Dairy, Beverages, Packaged Foods, and more. The analysis was performed using PostgreSQL, covering both data cleaning and bus
https://github.com/gagan8605/zepto_sql_analysis
cleaning-data data-analysis database-management postgresql sql
Last synced: 3 months ago
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This project explores and analyzes the inventory data of Zepto, a rapidly growing 10-minute grocery delivery platform in India. The dataset contains over 3,000+ SKUs across key product categories such as Fruits & Vegetables, Dairy, Beverages, Packaged Foods, and more. The analysis was performed using PostgreSQL, covering both data cleaning and bus
- Host: GitHub
- URL: https://github.com/gagan8605/zepto_sql_analysis
- Owner: gagan8605
- Created: 2025-06-25T14:29:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-25T14:52:10.000Z (4 months ago)
- Last Synced: 2025-06-25T15:43:42.250Z (4 months ago)
- Topics: cleaning-data, data-analysis, database-management, postgresql, sql
- Homepage:
- Size: 80.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🛒 Zepto Inventory & Pricing Analysis (SQL Project)
This project involves analyzing the inventory and pricing dataset of **Zepto**, a rapidly expanding 10-minute grocery delivery service in India. The dataset includes over **3,000+ SKUs (Stock Keeping Units)** across categories like **Fruits & Vegetables, Dairy, Beverages, Packaged Foods**, and more.
Using **PostgreSQL**, we performed data cleaning, exploration, and multiple business intelligence queries to derive actionable insights about product pricing, stock levels, and category-wise trends.
---
## 📦 Dataset Overview
- **Total SKUs**: 3,104
- **Columns**: `sku_id`, `category`, `name`, `mrp`, `discountPercent`, `availableQuantity`, `discountedSellPrice`, `weightInGms`, `outOfStock`, `quantity`
---
## 🧹 Data Cleaning & Exploration
- ✅ **Null Check**: No null values found across critical columns.
- 🔄 **Zero Price Removal**: Removed 21 SKUs with `mrp` or `discountedSellPrice` equal to 0.
- 💱 **Unit Conversion**: Prices converted from *paise* to *rupees*.
- 🏷️ **Unique Categories**: 11
- 📉 **Stock Status**:
- In Stock: 2,612
- Out of Stock: 471 (≈15.16%)
- 🔁 **Duplicate Product Names**: 112 names mapped to multiple SKUs.
---
## 📊 Business Analysis Queries & Insights
### 🔟 Top 10 Best Discounted Products
- Products with up to **60%+ discounts** across personal care and packaged items.
### 🚫 High MRP & Out-of-Stock Products
- 59 products with `MRP > ₹300` were unavailable.
- Highest observed MRP: ₹999.
### 💰 Estimated Revenue by Category
| Category | Revenue (₹) |
|---------------------|-------------|
| Packaged Foods | 1,14,230 |
| Beverages | 91,890 |
| Fruits & Vegetables | 85,140 |
| Dairy | 77,500 |
| Personal Care | 61,380 |
### 🧾 Premium Products with Low Discounts
- 48 products with `MRP > ₹500` and `<10%` discount, typically premium goods.
### 📉 Top 5 Categories by Avg. Discount
| Category | Avg. Discount (%) |
|-------------------|-------------------|
| Personal Care | 26.4 |
| Beverages | 23.1 |
| Snacks | 21.7 |
| Cleaning Supplies | 20.9 |
| Dairy | 18.3 |
### ⚖️ Best Value Products (₹ per gram)
- Price efficiency calculated for products above 100g.
- Best value product: ₹0.07/g (e.g. rice, sugar, detergent).
### 🏷️ Product Weight Category
| Weight Category | Count |
|------------------|-------|
| Low (<1kg) | 1,876 |
| Medium (1–5kg) | 845 |
| Bulk (>5kg) | 215 |
### 🏋️ Total Inventory Weight by Category
| Category | Total Weight (kg) |
|---------------------|-------------------|
| Packaged Foods | 7,140 |
| Fruits & Vegetables | 5,410 |
| Dairy | 3,860 |
| Beverages | 2,970 |
| Personal Care | 2,115 |
---
## 🛠 Tech Stack
- **Database**: PostgreSQL 16
- **Tools**: Git, GitHub, Git Bash
- **Language**: SQL (PostgreSQL dialect)