synthetic-data-planner

What problem does it solve?

This Skill helps plan the generation of synthetic data when real-world data is insufficient, restricted, or needs augmentation for AI training and testing.

Core Features & Use Cases

• Generation Strategy: Defines methods like rule-based, statistical, ML-based, or LLM-based.
• Quality Requirements: Specifies volume, feature distributions, and relationships to maintain.
• Validation Approach: Outlines statistical fidelity, utility, and privacy testing.
• Use Case: Planning to train a fraud detection model but have limited examples of fraudulent transactions. This Skill helps design a strategy to generate realistic synthetic fraudulent data to augment your real dataset.

Quick Start

Use the synthetic-data-planner to create a plan for generating synthetic data for customer churn prediction.