🛒 Zepto Inventory Data Analysis using PostgreSQL

📌 Project Overview

This project analyzes Zepto’s inventory dataset using PostgreSQL to generate actionable business insights related to product pricing, discount strategy, and inventory management.

The entire workflow including data cleaning, exploratory data analysis, and business insight generation was performed using SQL in a single integrated SQL file.

This project demonstrates practical SQL skills required for Data Analyst and Business Analyst roles.

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🎯 Business Objectives

• Understand product distribution across categories
• Analyze pricing and discount patterns
• Identify top revenue-generating products
• Evaluate inventory availability and stock shortages
• Compare product prices within categories

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🗂 Dataset Information

Dataset Name: Zepto Inventory Dataset

Key Columns:

• name – Product Name
• category – Product Category
• mrp – Maximum Retail Price
• discountedSellingPrice – Selling Price
• discountPercent – Discount %
• availableQuantity – Available Stock
• weightInGms – Product Weight
• outOfStock – Stock Status

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🧹 Data Cleaning

The following data cleaning steps were performed:

• Removed duplicate records
• Checked and handled null values
• Corrected pricing inconsistencies
• Standardized category values
• Created derived metrics for analysis

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📊 Analysis Performed

Exploratory Data Analysis

• Product count by category
• Price distribution analysis
• Discount analysis
• Inventory distribution

Business Insight Analysis

• Top revenue generating products
• Revenue contribution by category
• High demand products using stock status
• Profit margin analysis

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🚀 SQL Techniques Used

Aggregate Functions

• SUM()
• AVG()
• COUNT()

Window Functions

• RANK()
• Running Total using SUM() OVER()

Conditional Logic

• CASE statements

Derived Columns

• Revenue calculation
• Profit calculation
• Price per gram

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📈 Key Business Insights

• A small number of products contribute significantly to total revenue
• Premium products generate higher revenue per unit
• Some categories show frequent stock shortages indicating high demand
• Discount strategy varies significantly across categories

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🛠 Tools Used

• PostgreSQL
• SQL
• pgAdmin
• GitHub

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📁 Project Structure

zepto-sql-project/

│── zepto_v2.csv
│── Zepto_inventory_data_analysis.sql
│── README.md

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💼 Skills Demonstrated

• SQL for Data Analysis
• Data Cleaning using SQL
• Business Insight Generation
• Window Functions (RANK, Running Total)
• Analytical Thinking

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💼 Business Value

This project shows how SQL can be used to:

• Support business decision-making
• Identify revenue opportunities
• Analyze pricing strategies
• Improve inventory management

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👨‍💻 Author

Sreejit Kumar Paul

MBA Business Analytics

Software Engineer | Aspiring Data Analyst

📍 Kolkata, India

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