Data quality highly affects machine learning (ML) model performance and data scientists spend considerable amount of time on data cleaning before model training. The objective of the project is to measure the impact of data augmentation when developing a ML model for one dataset, more specifically, comparing YData synthetic data augmentation strategy against SMOTE and undersampling strategies.

Contact: Fabiana Clemente ( fabiana.clemente@ydata.ai )