{"id":27376734,"url":"https://github.com/sinhasomya100/task-3","last_synced_at":"2025-04-13T12:53:25.286Z","repository":{"id":287197631,"uuid":"963929492","full_name":"sinhasomya100/Task-3","owner":"sinhasomya100","description":"SQL for Data Analysis Use SQL queries to extract and analyze data from a database","archived":false,"fork":false,"pushed_at":"2025-04-13T08:02:51.000Z","size":1025,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-13T08:40:52.999Z","etag":null,"topics":["excel-csv","joins","kaggle-dataset","mysql","mysql-database","query"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sinhasomya100.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-10T12:34:00.000Z","updated_at":"2025-04-13T08:02:54.000Z","dependencies_parsed_at":"2025-04-13T08:40:54.534Z","dependency_job_id":null,"html_url":"https://github.com/sinhasomya100/Task-3","commit_stats":null,"previous_names":["sinhasomya100/sql-for-data-analysis","sinhasomya100/task-3"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sinhasomya100%2FTask-3","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sinhasomya100%2FTask-3/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sinhasomya100%2FTask-3/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sinhasomya100%2FTask-3/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sinhasomya100","download_url":"https://codeload.github.com/sinhasomya100/Task-3/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248717259,"owners_count":21150388,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["excel-csv","joins","kaggle-dataset","mysql","mysql-database","query"],"created_at":"2025-04-13T12:53:24.524Z","updated_at":"2025-04-13T12:53:25.279Z","avatar_url":"https://github.com/sinhasomya100.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":" 🛒 eCommerce Sales Data Analysis using MySQL\n\nWelcome 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.\n\n---\n\n📦 Dataset: `ecommerce_big`\n\nThis project revolves around a mock **eCommerce platform** database that closely resembles platforms like **Amazon**, **Flipkart**, or **Shopify**. The database includes 4 core tables:\n\n| Table Name     | Description                                      |\n|----------------|--------------------------------------------------|\n| `customers`    | Contains customer details like name and email    |\n| `products`     | Product catalog with names, categories, and price|\n| `orders`       | Transaction data including order dates and IDs   |\n| `order_items`  | Line items of each order: quantity \u0026 product info|\n\n📷 Screenshots showing the structure of these tables can be found in the `screenshots/` folder.\n\n---\n\n⚙️ Technologies Used\n\n- **MySQL Workbench** – SQL Querying and database creation  \n- **SQL (DDL + DML + Queries)** – Data definition and data manipulation  \n- **Windows 11** – Local development environment\n\n---\n\n 🎯 Objectives\n\nThe core aim of this project was to:\n- Understand how relational databases work in an eCommerce context\n- Perform data extraction, transformation, and analysis via SQL\n- Gain practical experience with real-world business scenarios\n\n---\n\n 🔍 Key Analytical Tasks\n\nEach SQL file addresses a key eCommerce metric or analysis scenario, such as:\n\n- 📈 **Top-selling products**\n- 👥 **Repeat customers**\n- 💸 **Total revenue by date/category**\n- 📦 **Average items per order**\n- 🕵️‍♂️ **Customer insights**\n\nThese were done using advanced SQL techniques like:\n- `JOINs`\n- `GROUP BY`\n- `HAVING`, `WHERE`\n- Subqueries \u0026 Nested Selects\n- Aggregate functions (`SUM`, `COUNT`, `AVG`)\n\n---\n\n🧠 Business Insights Extracted\n\nA few real-world learnings simulated from this dataset:\n\n1. **High-value customers** contribute to a large chunk of sales — supporting Pareto’s 80/20 rule.\n2. Certain products consistently outperform in terms of both units sold and revenue — useful for inventory planning.\n3. **Order quantity patterns** indicate bulk purchasing behavior during specific periods (e.g., festive seasons).\n\n---\n\n 📝 Files in this Repo\n\n| File Name              | Description                                     |\n|------------------------|-------------------------------------------------|\n| `task3_queries.sql`    | All SQL queries written during the analysis     |\n| `README.md`            | Project overview and context                    |\n| `screenshots/`         | MySQL Workbench screenshots for submission      |\n\n---\n\n📌 Notes\n\n- The database was manually built using raw SQL (DDL + DML) to simulate a real-world eCommerce schema.\n- All screenshots are included to validate the structure and execution of each query.\n- The project assumes **one order can have multiple items** (i.e., 1-to-many relationship between `orders` and `order_items`).\n\n---\n\n👨‍💻 Author\n\nSomya Sinha\nAspiring Data Analyst | SQL Enthusiast | Excel \u0026 Power BI Learner\n\n🔗 www.linkedin.com/in/somyasinha100 \n📧 somyasinha615@gmail.com\n\n---\n\n\u003e 💬 “Data is the new oil. SQL is the engine that refines it.”  \n\u003e – Inspired by real-world analysts building smarter ecommerce systems\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinhasomya100%2Ftask-3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsinhasomya100%2Ftask-3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinhasomya100%2Ftask-3/lists"}