🛍 Retail Sales Database — Design & Analytics

This project focuses on converting an Amazon retail sales dataset into a normalized relational database and performing analytical SQL queries to uncover business insights.

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Project Objectives

• Convert cleaned retail dataset into a relational database schema
• Normalize data into 3rd Normal Form (3NF)
• Execute analytical SQL queries for sales insights
• Generate ER diagram & supporting visuals

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🗂 Folder Contents

File Name	Description
Amazon Sale Report.csv	Original raw dataset
Cleaned_Amazon_Sale_Report.csv	Cleaned and preprocessed dataset (used for database loading)
sales.db	Final SQLite database containing normalized 3NF tables
sql_queries.ipynb	Jupyter Notebook with: ✔ Table creation scripts ✔ Data insertion ✔ Analytical SQL queries ✔ Validation outputs
ER Diagram.png	Entity Relationship Diagram of the database
Query1.jpg	Screenshot: Top-selling products by month
Query2.jpg	Screenshot: Sales by region (JOIN + GROUP BY)
Query3.jpg	Screenshot: Customers with spend > ₹5000

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Database Design (3NF)

Normalized tables created:

• Customers
• Products
• Orders
• OrderDetails

Ensures: ✔ No duplicate customer/product details
✔ Referential integrity enforced via Primary/Foreign Keys
✔ Scalable & efficient analytical querying

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ER Diagram

Full ERD available in ER Diagram.png

Tables & relationships:

Customers 1 ────∞ Orders 1 ────∞ OrderDetails ∞──── 1 Products

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Analytical SQL Queries

✔ Top-selling product by month
✔ Sales by region using JOIN + GROUP BY
✔ Customers with total spend > threshold using HAVING clause

Query Results

Top Selling Products

Sales by Region

High Value Customers

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Tools & Technologies Used

Component	Technology
Database	SQLite
Querying	SQL
Processing	Python + Pandas
Visualization	Built-in Jupyter screenshot capture

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Key Insights

• Product sales volumes peak in specific months for top categories
• Maharashtra & Karnataka lead in total order revenue
• High-value repeat customers generate majority of profit

These insights can support targeted promotions and stock planning.

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How to Review the Project

1. Open sql_queries.ipynb to view database creation and query execution
2. Explore sales.db using any SQLite viewer (DBeaver, DB Browser)
3. View ER Diagram.png and screenshots for query evidence

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