How can we apply PCA to reduce dimensionality of a heterogenous dataset?
Yes, Since machines cannot quantify qualitative (or) sometimes called categorical data. We manually have to quantify data when processed along with quantitative data. One way of converting these categorical data is One hot encoding, By performing one hot encoding, you convert categorical data into numerical data, something that a machine can understand
There are some cases where one hot encoding fails. When the number of categories are high, you will get highly sparse features which makes no sense Ex: Countries as categorical labels. In these types of situations you can calculate distance of all countries from a certain point on the earth Ex: Calculating the distance of each country from equator. I'm just a beginner in DS.