I am using this dataset: https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents
and so far I have successfully cleaned the dataset as well as reduced the size of the features and records.
I have some numeric features that I have standardized and there are some categorical features remaining that I have not touched. Now I need to perform clustering with Kmeans/Dbscan etc and compare their results, I read that since there are a lot of features (38) I can perform PCA on the numeric features and use the PCA components for clustering.
I also read that using PCA on categorical features is not very correct although it may produce a result. The way I want to do clustering is to remove the target feature('Severity') and do unsupervised learning on the remaining features to see how well it does to predicting the target feature.
Now for the question: Since I have so many features, even if I do PCA on the numeric ones and use the PCA components for clustering, will the result be okay? And what to do with categorical features? One hot encode them and put them in the PCA or don't use them for clustering at all?
All the examples I found used a small dataset with less than 10 features for clustering.