I have a dataset with 56 columns, 4 numeric and the target variable which is also numeric. I am trying to eliminate some of the categorical variables from my model and wanted to get some understanding of the methodologies to do that.
I am currently working in R, which is somewhat new to me.
Any help would be great!
Edit: I would like to eliminate some variables to increase my models performance. While the model is not performing poorly, I believe the model can perform better than it is now. Some of the variables I can take a look at and know right off the bat that this is something that I can remove. But, I would like to show proof that this is not an informative predictors.
Edit2: Here is information about my dataset.
Out of the columns that are categorical, there are roughly 5 variables that have levels greater than 2. My goal is essentially to remove variables that are the least informative to my model. My model is a regression model to predict the sale price of something. Using accuracy as a metric, I want to have the most accurate model and remove predictors (both numeric and categorical) that are not conducive to the predicting accuracy of my model.
In removing categorical variables, I would be removing the entire variable, not particular levels.