I have roughly 15 variables / attributes characterizing 6k customers in my data set. As they are categorical I have transformed them into 1 attribute for each possible value (1-out-of-K coding). An example could be Region with values "A", "B" and "C", which is transformed into 3 variables:
Region_C. The same goes for other variables such as the
Sales Channel. After this transformation I now have around 70 attributes.
I would like to examine if there are any significant 2-way interactions between the different variables with regards to a response variable (concerning
customer quality) using logistic regression. For instance, it is interesting to see if there is an interaction between
Sales Channel 1. However, there are very many possible interactions and therefore I would like to start by removing some variables, which have very few observations connected to them. An example could be that only 3 customers come from
More specifically, I would start by removing all attributes that have 5 observations or less connected to them (out of 6k observations). However, I cannot find out how to do that. Thus I have the following questions:
Does my thinking make sense? Or should I approach the issue in another way?
How do I remove all attributes in a dataset which has fewer than 5 observations connected to them? The values of the variables are always 0 or 1 as the customer is either from
Region A(=1) or not from
After removing these variables there should be fewer interactions. However, it would still be quite a large amount. I would therefore also like to only examine interactions with 5 observations or more. I am thinking this could be done using a formula in the logistic regression, but can you help me how I would find the right variables for the formula?