I have a training test with 3000 samples, 100 features per sample.
For each sample, there is also a label column with the sample "price" - continuous variable with infinite values possible (think of price of a house, for example)
My end goal is to build a model, based on the training set, which predicts a new sample "price".
In order to do so, I would first like to perform some dimensionality reduction, such as PCA, but I want to perform it so it would give me as a result, manipulated data set, with samples containing only the most affecting features in regard to the price label.
e.g. get transformed data set, with only X features that actually have an impact on the specific "price" column value
How can I achieve that in R?