Sample Generated Datasets
Explore realistic synthetic data generated using advanced AI techniques. Each dataset demonstrates different field types, industry contexts, and data patterns while maintaining uniqueness and avoiding duplicates.
E-Commerce Customer Data
E-CommerceRealistic customer profiles for online retail analysis
python main.py --fields "customer_id:string:uuid,name:string,email:string,age:integer:18:70,total_spent:float:25:2500,category_preference:categorical:electronics|clothing|books|home"...Healthcare Patient Records
HealthcareAnonymized patient data for medical research and training
python main.py --fields "patient_id:string:uuid,age:integer:18:85,diagnosis:categorical:flu|covid|diabetes|hypertension|allergies,visit_cost:float:50:800,insurance_type:categorical:private|medicare|medicaid"...Financial Transaction Data
FinanceSynthetic transaction records for financial modeling
python main.py --fields "transaction_id:string:uuid,account_holder:string,amount:float:10:5000,transaction_type:categorical:deposit|withdrawal|transfer|payment,risk_score:float:0:1"...About This Tool
This Python CLI application generates synthetic datasets using OpenAI's advanced language models. It features intelligent duplicate detection using fuzzy matching algorithms, configurable batch processing for optimal performance, and industry-specific context generation for realistic data patterns. The tool supports multiple output formats (JSON, CSV, Parquet) and can generate up to 50,000 records with sophisticated uniqueness controls and memory management.