Dimensionality Reduction Techniques Ordinal Variables on different scales I have a data set that is a mix between Ordinal variables and numerical variables.
The problem is that the ordinal variables are on different scales, such as 0-2, 1-4. The data set has 35 variables. I want to perform some type of data reduction on the data set but am unsure of what to do given the different scales.
Techniques such as:

*

*Random Forest

*Principal Component Analysis

However, I am not sure if it's possible due to the different scales?
I am using pandas.

The categorical variables don't have any scaling as such, in your case, the categorical variables are binned into different groups, calculate the value counts of these categories, and you can further bin smaller categories into one group, which can reduce the size of input features, or else, it would add more sparse input features to your data and also leads to dimensionality curse problem.
If you feel that all the 35 variables are important then you can try one of the following strategies like

*

*Principal component analysis

*t-SNE

*UMAP etc.

