https://github.com/s-narasimman/zepto_inventory_sql_data_analysis
This project focuses on data cleaning, exploration, and analysis of product information from the Zepto dataset using SQL. It provides actionable insights into pricing, stock availability, discounts, and category-level performance.
https://github.com/s-narasimman/zepto_inventory_sql_data_analysis
aggregation categorization csv data-analysis data-cleaning kaggle postgresql sql zepto
Last synced: 27 days ago
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This project focuses on data cleaning, exploration, and analysis of product information from the Zepto dataset using SQL. It provides actionable insights into pricing, stock availability, discounts, and category-level performance.
- Host: GitHub
- URL: https://github.com/s-narasimman/zepto_inventory_sql_data_analysis
- Owner: S-Narasimman
- License: mit
- Created: 2025-10-21T08:03:34.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-10-21T08:23:05.000Z (8 months ago)
- Last Synced: 2025-10-21T10:24:18.722Z (8 months ago)
- Topics: aggregation, categorization, csv, data-analysis, data-cleaning, kaggle, postgresql, sql, zepto
- Homepage:
- Size: 83 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🛒 Zepto Data Analysis Using SQL
This project focuses on **data cleaning, exploration, and analysis** of product information from the **Zepto** dataset using SQL.
It provides actionable insights into **pricing**, **stock availability**, **discount strategies**, and **category-level performance**.
📂 **Dataset Source:** The dataset was obtained from **Kaggle**, contributed by **Palvinder**.
---
## 📊 Key Objectives
* Explore and clean raw product data to ensure accuracy and consistency.
* Analyze discount trends, pricing strategies, and stock status.
* Derive insights on product performance, revenue, and value metrics.
---
## 🧩 SQL Operations Performed
### 1️⃣ Table Creation & Data Exploration
* Created the `zepto` table with detailed product-level fields.
* Verified null values, duplicates, and anomalies.
* Checked product availability (in-stock vs out-of-stock).
### 2️⃣ Data Cleaning
* Removed invalid records where MRP = 0.
* Converted price data from **paise to rupees** for consistency.
### 3️⃣ Data Analysis & Insights
| Query | Description |
| :----- | :----------------------------------------------------------- |
| **Q1** | Top 10 best-value products based on discount percentage. |
| **Q2** | High-MRP products that are out of stock. |
| **Q3** | Estimated revenue generated by each category. |
| **Q4** | Premium products (MRP > ₹500) with minimal discounts (<10%). |
| **Q5** | Top 5 categories offering the highest average discounts. |
| **Q6** | Price-per-gram calculation to determine best-value items. |
| **Q7** | Weight-based classification: *Low*, *Medium*, *Bulk*. |
| **Q8** | Total inventory weight per category. |
---
## 💡 Key Insights
* **High discounts** highlight best-value products that attract customers.
* **Premium items (>₹500)** typically offer **lower discounts**, maintaining brand value.
* **Bulk and medium-weight** items dominate total inventory weight.
* **High-MRP products** going out of stock indicate **strong customer demand**.
---
## 🧠 Tech Stack
* **Language:** SQL
* **Database:** PostgreSQL
* **Focus Areas:**
* Data Cleaning
* Aggregation
* Analytical Querying
* Business Insight Generation
---
## 🧾 Example Queries
```sql
-- Q1: Top 10 Best-Value Products
SELECT DISTINCT name, mrp, discountPercent
FROM zepto
ORDER BY discountPercent DESC
LIMIT 10;
-- Q3: Estimated Revenue by Category
SELECT category,
SUM(discountedSellingPrice * availableQuantity) AS total_revenue
FROM zepto
GROUP BY category
ORDER BY total_revenue;
```
## 🧑💻 Author
**Narasimman S**
📍 Chennai | Data Analyst | SQL & Data Science Enthusiast
---