I’ve been working on a dataset, something that I’ve never worked on before.

I am in the process of building a classification model which can separate 2 classes using 2 continuous features & around 10-15 multiclass categorical features. The classes are heavily imbalanced (3:1 ratio) & I have over 500k observations.

I’ve tried a few methods like downsampling, class balancing along with a few algorithms like Logistic Regression, KNN, Random Forest, a few Gradient Boosting algorithms etc.. All these models are giving me poor results.

I am working locally & don’t have access to a cloud service, hence I’m not keen on using NNs or SVMs which tend to be more computationally expensive.

What else can I do?

Thanks