https://github.com/sinhasomya100/task-3

SQL for Data Analysis Use SQL queries to extract and analyze data from a database
https://github.com/sinhasomya100/task-3

excel-csv joins kaggle-dataset mysql mysql-database query

Last synced: 9 months ago
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SQL for Data Analysis Use SQL queries to extract and analyze data from a database

• Host: GitHub
• URL: https://github.com/sinhasomya100/task-3
• Owner: sinhasomya100
• Created: 2025-04-10T12:34:00.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-04-13T08:02:51.000Z (9 months ago)
• Last Synced: 2025-04-13T08:40:52.999Z (9 months ago)
• Topics: excel-csv, joins, kaggle-dataset, mysql, mysql-database, query
• Homepage:
• Size: 1000 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
 🛒 eCommerce Sales Data Analysis using MySQL

Welcome to my SQL-based data analysis project where I dive deep into **sales trends, customer behavior, product performance, and order insights** using a self-created **eCommerce database**. This project was developed as part of my Data Analyst internship to simulate real-world retail data problems and solve them through SQL.

---

📦 Dataset: `ecommerce_big`

This project revolves around a mock **eCommerce platform** database that closely resembles platforms like **Amazon**, **Flipkart**, or **Shopify**. The database includes 4 core tables:

| Table Name     | Description                                      |

|----------------|--------------------------------------------------|

| `customers`    | Contains customer details like name and email    |

| `products`     | Product catalog with names, categories, and price|

| `orders`       | Transaction data including order dates and IDs   |

| `order_items`  | Line items of each order: quantity & product info|

📷 Screenshots showing the structure of these tables can be found in the `screenshots/` folder.

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⚙️ Technologies Used

- **MySQL Workbench** – SQL Querying and database creation  

- **SQL (DDL + DML + Queries)** – Data definition and data manipulation  

- **Windows 11** – Local development environment

---

 🎯 Objectives

The core aim of this project was to:

- Understand how relational databases work in an eCommerce context

- Perform data extraction, transformation, and analysis via SQL

- Gain practical experience with real-world business scenarios

---

 🔍 Key Analytical Tasks

Each SQL file addresses a key eCommerce metric or analysis scenario, such as:

- 📈 **Top-selling products**

- 👥 **Repeat customers**

- 💸 **Total revenue by date/category**

- 📦 **Average items per order**

- 🕵️‍♂️ **Customer insights**

These were done using advanced SQL techniques like:

- `JOINs`

- `GROUP BY`

- `HAVING`, `WHERE`

- Subqueries & Nested Selects

- Aggregate functions (`SUM`, `COUNT`, `AVG`)

---

🧠 Business Insights Extracted

A few real-world learnings simulated from this dataset:

1. **High-value customers** contribute to a large chunk of sales — supporting Pareto’s 80/20 rule.

2. Certain products consistently outperform in terms of both units sold and revenue — useful for inventory planning.

3. **Order quantity patterns** indicate bulk purchasing behavior during specific periods (e.g., festive seasons).

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 📝 Files in this Repo

| File Name              | Description                                     |

|------------------------|-------------------------------------------------|

| `task3_queries.sql`    | All SQL queries written during the analysis     |

| `README.md`            | Project overview and context                    |

| `screenshots/`         | MySQL Workbench screenshots for submission      |

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📌 Notes

- The database was manually built using raw SQL (DDL + DML) to simulate a real-world eCommerce schema.

- All screenshots are included to validate the structure and execution of each query.

- The project assumes **one order can have multiple items** (i.e., 1-to-many relationship between `orders` and `order_items`).

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👨‍💻 Author

Somya Sinha

Aspiring Data Analyst | SQL Enthusiast | Excel & Power BI Learner

🔗 www.linkedin.com/in/somyasinha100 

📧 somyasinha615@gmail.com

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> 💬 “Data is the new oil. SQL is the engine that refines it.”  

> – Inspired by real-world analysts building smarter ecommerce systems