Project Title: Synthetic Data Generation

Objective:

To create artificial data that mimics real-world data distributions while preserving privacy, enabling data augmentation, or enhancing model training when real data is limited or sensitive.

Key Components:

Understanding the Use Case:

Determine the purpose of synthetic data: privacy protection, data augmentation, imbalanced class handling, or simulation of rare events.

Identify the type of data to generate: tabular, image, text, or time series.

Data Analysis:

Analyze the real dataset to understand feature distributions, correlations, and class imbalances.

Perform preprocessing like encoding, scaling, and missing value treatment.

Generation Techniques:

Statistical Methods: Sampling from estimated distributions (e.g., Gaussian, multinomial).

Machine Learning Models:

SMOTE for oversampling minority classes.

GANs (Generative Adversarial Networks) for realistic image or text generation.

VAEs (Variational Autoencoders) for continuous data generation.

CTGAN / TVAE for tabular data (via SDV or other libraries).

Evaluation of Synthetic Data:

Statistical Similarity: Compare distribution of real vs. synthetic data.

Model Utility: Train models on synthetic data and evaluate on real data.

Privacy Checks: Ensure synthetic data does not leak sensitive information.

Packaging & Reuse:

Create reusable scripts or modules for generating and evaluating synthetic data.

Use Docker or virtual environments for reproducibility.

Outcome:

A synthetic dataset that maintains utility and realism without compromising privacy, often accompanied by a tool or API for automated generation.