what are effects of working with categorical dataset I am working on classification problem where the dataset contains 90% of features as categorical. It is binary classification problem, and the class is heavily imbalanced. I performed Smote over sample and created a model. I also tried similar approach with undersampling. Both the method with logistic regression performs mediocre. I want to know how having too many categorical variables impacts the model and possible efficient way to approach the problem

feature1:1-3
feature2:0-1
feature3:0-3
feature4: 1-4
feature5: 0-2
feature6: 0-5
feature7: 1-4
feature8: continuous( max 10)
feature9 continuous( max 10)
class: 0-1
You need to distinguish a few problems here. How well can your data classify some outcome, what features should be used, how to deal with imbalanced classes.

Categorical features are not a problem per se. In the current stage, feature selection might be an issue. Thus, use logit with lasso or ridge to shrink features which are not too helpful (happens automatically). Also dummy/one-hot encoding would be worth a try (jointly with lasso).

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html