100mcg is a synthetic dataset that mirrors the workings of an online healthcare and pharmacy platform. It includes everything from medicine listings and user purchases to prescriptions and customer reviews—making it a great playground for testing ideas in healthtech. Whether you’re building recommendation systems, analyzing buying patterns, or simulating prescription flows, 100mcg offers realistic data to explore.
The dataset is perfect for data scientists, developers, and analysts alike. You can model product demand, predict user needs based on health behavior, or test backend processes like order fulfillment and inventory tracking. It's also a great resource for practicing SQL, experimenting with pricing strategies, or understanding what drives user decisions in a digital pharmacy setting.
Highlights:
- Simulates a full pharmacy and healthcare platform with product listings, user profiles, prescriptions, and transactions.
- Great for building models for purchase prediction, product recommendation, and dynamic pricing strategies.
- Supports backend testing for inventory management, order processing, and prescription handling systems.
- Provides insights into user behavior, medication preferences, seasonal demand, and the impact of promotions.
- Perfect for SQL practice, data cleaning, cohort analysis, and A/B testing of promotional campaigns.
The 100mcg schema is designed for an online pharmacy system that manages users, medicines, prescriptions, orders, and payments. It facilitates the categorization of medicines, prescription management, order processing, and customer reviews. The schema also supports discounts, shopping cart functionality, and notifications to improve user experience.
Key tables in the dataset include:
- Users: Stores user data, including login, contact details, and role (customer, admin, or doctor). Links to orders, prescriptions, and reviews.
- Medicines: Contains information about medicines, including name, description, price, stock, and dosage form (tablet, capsule, syrup, etc.).
- Categories: Defines categories for grouping medicines (e.g., "Painkillers," "Antibiotics") with a unique name and description.
- Medicine Categories: Many-to-many relationship linking medicines to categories, allowing a medicine to belong to multiple categories.
- Prescriptions: Holds user prescriptions issued by doctors, including references to the user, doctor, and an image of the prescription.
- Orders: Tracks customer orders, including total amount, status (pending, completed), and shipping and billing addresses.
- Order Items: Stores details of each item in an order, including the medicine, quantity, unit price, and total price.
- Payments: Records payment transactions for orders, including payment method, status (pending, completed), and transaction ID.
- Reviews: Stores customer reviews for medicines, including ratings (1-5 stars) and text feedback, linked to medicines and users.
- Cart: Tracks items added to a user’s cart, including medicine ID, quantity, and the time it was added.
- Discounts: Defines discount codes users can apply, including percentage and validity period (start and end dates).
- Notifications: Stores notifications sent to users about order status, prescriptions, and platform updates, with read/unread status.
The 100mcg dataset was created to reflect the structure and operations of an online pharmacy and healthcare product platform. It models key elements such as medicine listings, prescription uploads, user orders, cart activity, product reviews, and payment handling. AI simulations were used to mimic realistic behaviors from customers, doctors, and order fulfillment systems.
Prescription flows, stock management, dosage types, and categorical classifications are all included — without any real patient data, prescriptions, or medical products. This ensures ethical use while allowing developers and analysts to test e-health applications, pharmacy backend systems, and customer support tools in a fully synthetic environment.
100mcg presents a healthcare-focused dataset simulating prescription orders, medicine reviews, and user purchases. Built for health tech simulations and analysis, the dataset ensures compatibility with industry-standard file types, traditional SQL databases, and enterprise-level cloud platforms.
- Available file formats: CSV, JSON, Excel
- Available databases: MySQL, PostgreSQL, SQL Server
- Cloud database access: Snowflake
