a newbie here. I am currently self-learning data science. I am working on a dataset that has both categorical and numerical (continuous and discrete) features (26 columns, 30244 rows). Target is numerical (1, 2, 3). I have several questions.
I still have not performed any encoding or scaling techniques. According to my knowledge, as my categorical data are unordered, I have to perform one hot encoding right? As it will increase the number of columns, I am hoping to do that after feature selection. Is that okay?
How can I perform feature selection for this dataset? (Because this has both numerical and categorical data) Should I first do one-hot encoding and then go for checking correlation or t-scores or something like that?
(I am currently focusing on EDA only. I don't have a model in my mind)
Any help is much appreciated. Thank you!