Looking for data to train ML-models? Generate it Yourself: 3 P | Big Data Science

Looking for data to train ML-models? Generate it Yourself: 3 Python Packages for Generating Synthetic Data
Synthetic data is an artificially generated, not collected, topic learning dataset for training ML models or practicing analysis techniques. You can create them yourself using possible Python packages:
• Faker is a very simple and efficient Python package for creating their data. It's great when you need to load data into a database, create a use of XML documents, prepare for load testing, or anonymize data retrieved from involved services. https://github.com/joke2k/faker
• SDV (Synthetic Data Vault) is a synthetic data storage for creating synthetic data based on a given dataset. The generated data can be a single summary, pivot table, or time series, and have the same properties and statistics as the original dataset. SDV uses synthetic data with DL models. Even if the original dataset contains multiple data types and gaps, SDV handles them. https://sdv.dev/SDV/
• Gretel Synthetics - a source code package based on a recurrent neural network for generating structured and unstructured data. The batch approach treats a data set as text data and trains a model based on it. The model will then create synthetic data with text data. Gretel is based on RNN networks, it requires more computing power, so when working with it, it is better to use Google Colab, rather than load a personal computer. https://synthetics.docs.gretel.ai/en/stable/