This project aims to train a model based on supervised data with minimal effort.
Aims are to:
-Clean out missing values, uninformative features. -Normalize data between different sources. -Automatically detect discrete vs continuous data types, handle appropriately. -Scale all features appropriately. -Select the best model automatically using cross-validation.