Categorical Features Encoding challenge--Kaggle

This workflow demonstrates use of Auto Categorical Features Embedding component. The component transforms categorical features into numeric features through three metods: count-encoding, rank of count-encodings and target-encodings.
Each one of the three methods transforms a column of categorical feature to one column of numeric values. This is unlike one-hot-encoding where each categorical column gets transformed to multiple numeric columns.
Dataset is imbalanced. F1 score as also cohen's kappa score are much better when modlling using this compoenet than without it.

External resources

• Kaggle Data Source