Being late to the party I could offer these points
- you might want to employ H2O.ai models included in knime at least for benchmarking. I wrote an article about how to use the auto-machine learning package (always to be taken with a grain of salt I know) H2O.ai AutoML in KNIME for classification problems
- then you could try and use automatic feature engineering techniques like vtreat or featuretools and see what that could do (keep in mind to separate test and training) H2O.ai AutoML (wrapped with R) with vtreat data preparation in KNIME for classification problems (with R vtreat) – KNIME Hub
- then you could try and employ a technique like label encoding (terms and conditions apply and you should be careful what categorical variables you would transform H2O.ai AutoML in KNIME for classification problems - #11 by mlauber71
Then like other KNIMEs suggested it does make sense to read about machine learning in general and avoid pitfalls.
If you have to deal with (highly) imbalanced data you might want to read the links about that in my collection.
It is although important to keep in mind what your data says and what business problem you want to solve and if your data will be able to say something about that - preferably in a reproducible way.