Unbalanced target variable in Orange, how do I balance it? So I want to perform a predictive model to predict churn.

I have 2 datasets, one with churn and the other without (so I can later perform predictions).

The issue is that I think my Confusion matrix is kinda bad since my target variable is highly unbalanced:



which mostly leads to this confussion matrix:



(Similar values for both logistic regression and decision tree).

This is my workflow:



Is there any way to balance the data? I can't find it in the Orange documentation.
For unbalanced classes use the Python Script widget, with imblearn add-on, you will need to code!

Link for thread in github