📊 SQL Exploratory Data Analysis (EDA) Project

📌 Overview

SQL-powered EDA on a relational database to drive business decisions. Features complex queries (CTEs, window functions, joins), customer RFM & cohort analysis, sales trends, YoY growth, and top performers.

💡 Key Objectives

• Clean and prepare raw data entirely using SQL techniques
• Uncover actionable insights on sales performance, customer behavior, and product trends from a relational database
• Leverage SQL features including aggregations, joins, window functions, and CTEs

🏗️ Database Schema

The dataset consists of multiple relational tables, including:

• Customers: Contains customer information (ID, Name, Location, etc.).
• Products: Stores product details (ID, Name, Category, Price, Cost etc.).
• Sales: Links orders and products to track revenue and quantity sold.

🔍 Key Analyses

1️⃣ Sales Analysis

• Total revenue, average order value, and monthly revenue trends.
• Year over year sales performance.
• Contributions of products and categories to total revenue.

2️⃣ Customer Insights

• Customer retention and churn analysis.
• customer segmentation by age and spending.
• Purchase frequency and order trends.

3️⃣ Product Performance

• Products and catefories performance ranking.
• Stock and inventory turnover analysis.
• Product category-wise sales breakdown.

🛠️ SQL Techniques Used

• Aggregate Functions (SUM, AVG, COUNT, MAX, MIN)
• Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN)
• Window Functions (ROW_NUMBER(), RANK(), DENSE_RANK(), LAG())
• Common Table Expressions (CTEs)
• Subqueries and Nested Queries
• Group By & Having Clauses
• Case Statements for Conditional Analysis

📂 Project Structure

📦 SQL-EDA-Project
 ┣ 📜 queries.sql  # Collection of SQL queries for EDA
 ┣ 📜 dataset.csv  # Sample dataset (if applicable)
 ┣ 📜 README.md    # Project documentation

🚀 How to Run

1. Clone the repository

   git clone https://github.com/LightR-039/SQL_EDA_project_datawarehouseanalysis.git
   cd     SQL_EDA_project_datawarehouseanalysis

2. Load the dataset into your SQL Server Management Studio OR Run this file from scripts → scripts/00_init_database.sql
3. Run the SQL queries from scripts to explore the dataset and extract insights.

🔗 Future Enhancements

• Integrate with Power BI/Tableau for interactive visualizations.
• Automate SQL queries using Python scripts.
• Expand analysis with predictive analytics using machine learning.

🤝 Contributing

Feel free to fork the repository and submit pull requests for improvements! Suggestions and collaborations are welcome. 🚀

📜 License

This project is licensed under the MIT License.

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