This project involves creating a comprehensive SQL database for a fashion store using SQLite. The database is designed to simulate real-world operations of a fashion company, including detailed information about employees, fashion designs, clients, and orders.
Key Components:
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Data Generation:
- Utilized Python with numpy and Faker libraries to generate realistic, synthetic data.
- Created four dataframes: employees, designs, clients, and orders, each containing relevant attributes.
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Database Schema:
- Employee Table: Contains details such as employee ID, names, birth dates, department, hire dates, and addresses.
- Design Table: Includes design ID, names, and the employee ID of the creator.
- Client Table: Holds client IDs, names, contact information, and membership types.
- Order Table: Details order IDs, design IDs, client IDs, order dates, client ratings, and a compound key for unique identification.
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Data Types:
- Included various data types: nominal, ordinal, interval, and ratio data to ensure a diverse and realistic dataset.
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Database Implementation:
- Imported CSV files into SQLite DB Browser.
- Set up primary keys, foreign keys, unique constraints, and not-null values to maintain data integrity and establish relationships between tables.
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Ethical Considerations:
- Ensured data privacy by using only synthetic data.
- Represented various demographics and departments fairly.
- Created the dataset for educational purposes only.
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Example Queries:
- Demonstrated complex SQL queries, including joins and selections, to showcase the functionality and relationships within the database.
This project highlights the ability to create, manage, and query a realistic database, demonstrating advanced SQL and data management skills.