I am trying to do linear regression to predict the time a user spends listening to music using the following dataset:
My end goal is to know which characteristics or columns lead to higher listening. (Sum is the total listening time)
I was thinking of using PCA before linear regression because there were so many columns. However, as I googled that, I came across PCR and I'm not sure what the difference is. Does PCA even increase the results from Linear Regression? If yes, then is PCA + LR better or PCR better?
I am trying to do this using scikit learn's method for Linear Regression. It looks like it only takes in an X and Y data set for training. Does this mean I need to decrease my dimensionality of my input data matrix to 2 dimensions using PCA before I can use this method?
EDIT: Ended up using pandas and statsmodel to take in multiple inputs when doing linear regression in case that helps anyone else with the same question...However, my above questions still stand.