📌 Project Overview

This project analyzes an e-commerce dataset using SQL and Python to uncover insights on customer behavior, sales trends, product performance, and seller contributions. Queries range from basic to advanced, helping businesses improve decision-making, customer retention, and revenue growth. This project analyzes an E-commerce platform’s dataset to extract insights useful for business decision-making. The dataset includes information about customers, orders, products, sellers, and payments. Dataset Link: https://www.kaggle.com/datasets/devarajv88/target-dataset?select=products.csv

The analysis is structured into three levels of SQL queries: Basic, Intermediate, and Advanced.

📂 Dataset Schema (Typical Tables)

customers → customer details (ID, city, state)

orders → order details (ID, customer ID, order date, status)

order_items → items per order (order ID, product ID, seller ID, price, quantity)

products → product details (ID, category, name)

payments → payment details (order ID, type, installments, amount)

sellers → seller details (ID, state, city)

⚙️ Tools & Technologies

MySQL

Python (Pandas, Matplotlib, Seaborn) for data visualization

Power BI for dashboards

🚀 Key Outcomes

Understanding customer distribution and behavior

Identifying top-performing products and sellers

Analyzing revenue growth and customer loyalty

Providing actionable insights for marketing and operations