Automatic Linear/Multiple Regression in Python with 50+ columns
This is more of an EDA problem than a python problem. Look into some regression resources, specifically a correlation matrix. However, one possible solution could use itertools.combinations with a group size of 6. This will give you 15,890,700 different options for running a regression so unless you want to run greater than 15 million regressions you should do some EDA to find important features in your dataset.
I have a dataset with more than 50 columns and I'm trying to find a way in Python to make a simple linear regression between each combination of variables. The goal here is to find a starting point in furthering my analysis (i.e, I will dwelve deeper into those pairs that have a somewhat significant R Square).
I've put all my columns in a list of numpy arrays. How could I go about making a simple linear regression between each combination, and for that combination, print the R square? Is there a possibility to try also a multiple linear regression, with up to 5-6 variables, again with each combination?
Each array has ~200 rows, so code efficiency in terms of speed would not be a big issue for this personal project.