📊 Project Overview – Retail Sales Analysis The Retail Sales Analysis Project demonstrates the practical use of SQL in analyzing transactional data from a retail business. By leveraging SQL queries, this project simulates how a data analyst explores, cleans, and interprets raw sales data to uncover meaningful business insights. The dataset includes customer demographics (age, gender), transaction details (date, time, quantity, price), and sales metrics (total sales, COGS). Through a combination of exploratory data analysis (EDA) and business-driven queries, we analyze customer behavior, sales trends, and product performance across multiple categories.
🔹 Objectives
Database Setup → Create and populate a retail sales database (Retail Sales data). Data Cleaning → Identify and handle missing/null values to improve data quality. Exploratory Data Analysis (EDA) → Understand dataset distributions (sales volume, date range, categories). Business Analysis → Answer key retail questions using SQL queries, such as:
What are the top-selling products and categories? Who are the most valuable customers? How do monthly and shift-based sales trends change over time? What percentage of transactions represent high-value sales?
🔹 Key Features
SQL queries for filtering, aggregation and window functions. Business insights into customer demographics, sales trends, and product performance. Queries written with best practices (readable formatting, CTEs for clarity). Organized project workflow: setup → cleaning → EDA → business insights.
🔹 Business Value
This project reflects how SQL is used in real-world analytics to:
Improve decision-making by identifying peak seasons and high-value customers. Support marketing strategies by analyzing demographics and product demand. Enhance inventory planning by spotting top-performing categories.