The spectrum of synthetic data generation: A comprehensive review

Synthetic data, which is the data produced to mimic the characteristics of actual data without revealing any confidential information, is a much safer option than original data, especially when it comes to extreme instances such as personal data, financial data, or military intelligence. There are substantial dangers connected with the use of real-life data such as assault with the intent to commit identity theft, fraud, and hacking, but because synthetic data (SD) reproduces some of the elements of real data, without infringing on anyone’s privacy, suffers from these risks. The project concentrates on the cutting-edge fields of Language Learning Models (LLM) and Deep Learning (DL) to generate synthetic data that mimics real-world data in its intricacy. Advances in LSTM networks and Generative Adversarial Networks (GAN) produce plausible and useful data in sequence forms for natural language processing and machine learning (ML) augmentation respectively. Applications of this technology include, but are not limited to, the use of augmented datasets to improve medical diagnosis, advanced finance fraud detection systems, and designing fictitious consumers in order to enhance AI-based system recommendations. The project which is implemented with Python programming language and also takes advantage of some open source packages such as SymPy, Pydbgen, Synthetic Data Vault (SDV), and Scikit-learn offers a solution to data scarcity and quality problems in order to improve the performance of the AI models in various sectors.