Synthetic data modeling

In addition to faker, there are several Python libraries and tools that can generate reliable synthetic data, depending on the type (tabular, time-series, image, text, etc.) and the level of realism or constraints you need. Here’s a breakdown by use case:





1. General Tabular Data



  • SDV (Synthetic Data Vault)
  • State-of-the-art for realistic synthetic tabular data.
  • Uses statistical modeling or deep learning (GANs, CTGAN, TVAE).
  • Ideal for privacy-preserving data generation.

  • scikit-learn’s make_classification, make_regression, make_blobs
  • Useful for ML model prototyping.
  • Generates labeled synthetic data for classification/regression problems.

  • [Synthpop (via RPy2 for Python)]
  • If you want to use R’s synthpop from Python.
  • Great for statistical privacy-preserving synthetic data.






2. Time-Series Data



  • Timesynth
  • For generating synthetic time series data.
  • Supports autoregressive signals, harmonic signals, and noise.

  • Sktime
  • Has utilities for time series generation, often used in research.







3. Image / Vision Data








4. Text / NLP Data








5. Domain-Specific Libraries



  • Pydbgen
  • Like Faker, but tailored for fake databases.


  • DataSynthesizer
  • Generates synthetic data with differential privacy guarantees.


  • Mimesis
  • Similar to Faker, supports multiple locales and categories.






Would you like suggestions based on a specific type of data or project goal (e.g., simulating customers, financial records, health data)?


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