Show HN: SyntheticRows – expand small datasets, with an honest quality score (syntheticrows.com)

SyntheticRows, a new free web application, has been launched to help machine learning practitioners generate realistic synthetic data from their own CSV files. This tool aims to address common challenges faced by data scientists, such as small dataset sizes and class imbalances, by augmenting existing datasets to improve model training effectiveness. Notably, it offers an "honest" three-metric realism scoring system and ML-readiness testing, enabling users to train on synthetic data and evaluate performance on real data, ensuring reliable outcomes without compromising data privacy. The significance of SyntheticRows lies in its focus on generating privacy-safe synthetic data while providing users with quality assurances. By allowing data scientists to automatically fix data quality issues and expand their datasets, it democratizes access to robust ML training resources, especially for those with limited data. This tool not only enhances the capability of smaller ML teams to compete but also pushes the boundaries for synthetic data applications in various domains.