Data

What’s in This Dataset This dataset contains 3,648 realistic synthetic bank transactions spanning one year, with data for five distinct accounts. Each transaction includes essential fields such as date, account_id, amount, merchant_name, category, running_balance, and transaction_type. The transactions are structured to mimic real-world spending behavior, including regular purchases, recurring bills, and occasional large expenses. The dataset also includes a merchant_name column with realistic names like “Starbucks,” “Amazon,” and “Walmart,” along with category labels like “Food & Dining,” “Shopping,” “Utilities,” and “Transportation....

What’s in This Dataset This dataset contains 10,000 realistic, HIPAA-safe synthetic patient records in standard CSV format. Each record includes a full set of healthcare-related fields such as patient demographics (age, gender, address), ICD-10 diagnosis codes, prescribed medications, insurance provider details, and billing amounts. The dataset is designed to mimic real-world patient data without compromising privacy, making it suitable for testing and development purposes. The CSV includes columns like patient_id, age, gender, diagnosis_code, medication_name, insurance_provider, billing_amount, and admission_date....

What’s in This Dataset This dataset includes 500 realistic synthetic MLS property listings in CSV format, designed to mimic real estate data you’d find in a professional listing system. Each record contains a unique MLS-style listing ID, full property address, price, square footage, number of bedrooms and bathrooms, lot size, listing date, and agent information. The fields are carefully structured to reflect typical real estate listing details, including columns like mls_id, address, price, beds, baths, sqft, lot_size, list_date, and agent_name....

What’s in This Dataset This dataset includes 5,000 realistic fake Shopify order records in CSV format, designed to mirror real-world export data from Shopify. Each record contains essential fields like order_id, customer_email, customer_name, product_name, sku, price, discount_amount, shipping_address, order_status, and order_date. The structure matches actual Shopify CSV exports, making it ideal for testing tools and systems that expect real data formats. With columns covering everything from transactional details to customer demographics, this dataset offers a complete snapshot of e-commerce activity for development and analysis....

synthetic patient records are essential for testing healthcare applications, but manually creating them is a tedious task. You end up copying data, fudging dates, or using outdated tools that don’t meet industry standards. The process is slow, error-prone, and often fails to maintain privacy compliance, especially when working with HIPAA-safe datasets. The Manual Way (And Why It Breaks) Creating synthetic patient records manually means copying and pasting from templates or writing out data by hand....

MLS property listings are often scattered across multiple sources, and manually compiling them into a usable dataset can be tedious and error-prone. If you’re working on real estate data projects, you’ve probably encountered the time-consuming task of gathering and structuring property information from various public or private feeds. That’s where automation tools like the 500 Synthetic MLS Property Listings come in. The Manual Way (And Why It Breaks) Manually collecting MLS property listings involves a lot of clicking, copying, and pasting....

The manual process of creating employee records for testing HR systems, database seeding, or payroll applications is tedious and error-prone. You end up copying and pasting data, guessing realistic salary ranges, or worse — reusing the same names over and over. A proper employee record generator can automate this and make your development workflow more efficient. The Manual Way (And Why It Breaks) Manually creating employee data is a time sink....

Synthetic patient data is essential for healthcare software testing, but manually crafting it is tedious and error-prone. Without proper tools, developers often fall back to copying real records or creating fake data by hand — both of which risk privacy violations and inaccurate simulations. The result? Flawed applications that fail in real-world use. The Manual Way (And Why It Breaks) Creating healthcare datasets by hand is a painstaking process. You start with basic demographics like names, dates of birth, and addresses — but then you need to build realistic medical histories....

Usage Download CSV: oddshop.work/downloads/synthetic-patient-records-10k.zip Requirements Python 3.8+. Install dependencies: pip install -r requirements.txt Download Buy for $39 → Buy once, download immediately. ZIP includes the full script, README, and usage examples. License Personal & Commercial Use. You may use this tool in your own personal and commercial projects. Redistribution or resale of the source code is not permitted.

Usage Download CSV: oddshop.work/downloads/synthetic-mls-listings-500.zip Requirements Python 3.8+. Install dependencies: pip install -r requirements.txt Download Buy for $29 → Buy once, download immediately. ZIP includes the full script, README, and usage examples. License Personal & Commercial Use. You may use this tool in your own personal and commercial projects. Redistribution or resale of the source code is not permitted.

Generates synthetic employee records for testing applications, database seeding, and development environments. Creates realistic data including personal information, job details, and organizational structure. Perfect for HR systems, payroll applications, and employee management software testing. Features Generate thousands of realistic employee records with proper names and demographics Create structured data with departments, job titles, salary ranges, and reporting chains Export to CSV, JSON, or Excel formats with customizable field mappings Control data distribution patterns for realistic organizational hierarchies Include realistic employee attributes like hire dates, contact info, and location data Usage python generate_employees....

Generates realistic fake real estate property listing records as CSV for app testing. Creates property data with MLS-style IDs, addresses, prices, square footage, bedrooms, bathrooms, and agent info. Features Generate MLS-style listing IDs and dates Property addresses with city and state Bedrooms, bathrooms, and square footage Listing price and days on market Agent name, brokerage, and contact info Configurable record count via –records argument Usage python fake-real-estate-listing-generator.py --records 500 --output listings....