Zepto SQL Data Analysis

Project Overview

This project analyzes Zepto's e-commerce inventory dataset using PostgreSQL to extract meaningful business insights. The analysis focuses on product pricing, discounts, inventory availability, category-wise performance, and stock optimization using advanced SQL queries.

Objective

Analyze large-scale e-commerce data.
Perform data cleaning and preprocessing.
Solve business problems using SQL.
Generate actionable insights for decision-making.

Technologies Used

PostgreSQL
SQL
pgAdmin 4
Git & GitHub

Key Features

Cleaned and validated raw inventory data.
Performed Exploratory Data Analysis (EDA).
Used Joins, CTEs, Window Functions, Aggregate Functions, and Subqueries.
Analyzed product categories, discounts, stock levels, and pricing trends.
Generated business-focused insights using optimized SQL queries.

Business Insights

Identified high-value and low-performing product categories.
Analyzed discount strategies across different products.
Detected inventory shortages and overstocked items.
Compared pricing trends to support business decisions.

Skills Demonstrated

SQL Query Writing
Database Management
Data Cleaning
Exploratory Data Analysis (EDA)
Business Analytics
Problem Solving

Outcome

Successfully built an end-to-end SQL analytics project that demonstrates strong SQL proficiency, analytical thinking, and the ability to derive business insights from real-world e-commerce data.