Attempting dimensionality reduction on the Census-Income (KDD) Data Set.
The dataset is a mixed dataset with continuous and categorical features.
PCA works fine for continuous variables, reduced down to 2 PCA variables.
But having difficulties with categorical variables and cont/cat together.
There are 2 options that are not working out.
- multiple correspondence analysis (MCA - for categorical variables)
[0.022592513816454877, 0.022074753604733162, 0.01779832534650477, 0.01644525961669972, 0.013596646212654651]
- factor analysis of mixed data (FAMD - for continuous and categorical variables).
Will add these later but they are also very low - not picking up variance in data
Any ideas/tips ?