100mcg: Your
Real-World
Healthcare &
Pharmacy
Data Playground
100mcg is a ready-to-use synthetic dataset that mimics the experience of a real online pharmacy platform. Ideal for recommendation engines, prescription flow testing, dynamic pricing, and healthcare behavior analysis.
Overview
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.
Full Pharmacy Lifecycle
Simulates a complete pharmacy and healthcare experience, including user profiles, product listings, prescriptions, cart management, orders, and fulfillment.
Built for Development & Testing
Excellent for building and testing purchase prediction models, product recommendation systems, dynamic pricing strategies, and demand forecasting pipelines.
Backend Feature Testing
Useful for developers working on inventory management, order processing, prescription handling, and customer support flows.
Rich Transaction Data
Supports use cases in user behavior analysis, medication preference modeling, seasonal demand tracking, and promotional impact studies.
Analytics & Research
Suitable for SQL practice, data cleaning, cohort analysis, ETL development, and A/B testing simulations of promotional campaigns.
How it Works
AI-Generated & Fully Synthetic
The 100mcg dataset was created using AI simulations to reflect the structure and operations of an online pharmacy, mimicking realistic behaviors from customers, doctors, and order fulfillment systems — with zero real patient data.
Realistic Simulation with Privacy
It models prescription flows, stock management, dosage types, and categorical classifications — without any real prescriptions or medical products, ensuring ethical use across all healthtech applications.
High-Quality & Safe for Use
Built for developers and analysts to test e-health applications, pharmacy backend systems, and customer support tools in a fully synthetic environment — 100% privacy-compliant and ready to use.
Dataset Schema
A comprehensive relational model representing a modern online pharmacy platform engineered for deep analysis and complex querying.
Users
Stores user data including login, contact details, and role (customer, admin, or doctor).
Medicines
Contains medicine name, description, price, stock, and dosage form (tablet, capsule, syrup, etc.).
Categories
Defines categories for grouping medicines such as Painkillers and Antibiotics.
Medicine Categories
Many-to-many relationship linking medicines to categories, allowing multiple associations.
Prescriptions
Holds user prescriptions issued by doctors, including references to the user, doctor, and prescription image.
Orders
Tracks customer orders including total amount, status, and shipping and billing addresses.
Order Items
Stores details of each item in an order including medicine, quantity, unit price, and total price.
Payments
Records payment transactions for orders including method, status, and transaction ID.
Reviews
Stores customer reviews for medicines including ratings (1–5 stars) and text feedback.
Cart
Tracks items added to a user's cart including medicine ID, quantity, and time added.
Discounts
Defines discount codes users can apply, including percentage and validity period.
Notifications
Stores notifications about order status, prescriptions, and platform updates with read/unread status.
- CSV
- JSON
- Excel
- MySQL
- PostgreSQL
- SQL Server
- Snowflake