I am very interested in R and prediction models. I already used different models like ols, flexible ols, lasso, Regression trees and so on for american household data which was already perfectly prepared.
Now I am trying to do a more complex prediction of the amount of sold quantity of 5 different products. I got data on the amount of Sales channels and Dates (weekstart). Availability of products in sales channels per weekstart and product price. Also a Logical variable which indicates if there was promotion in a certain week, for product x and ad spending for TV and radio.
SO what I did so far was factorizing certain variables like the product names,Logical variable, weekstarts, Sales channels(A-F).So I can perform a linear Regression including variable declaration that R can work with. I did that by simply using factor(). Sidenote: I have no NA or missing values as well as completly out of range values like a price of a billion Dollar or something.
Now after creating test and Train data, using this Code to make a prediction seems to simple:
fmla=QuantitySold ~ (Channel + WeekStart + Price + isPromoPeriod + TV + Online + StoresAvailability) basic= QuantitySold ~ (Channel + WeekStart + Price + isPromoPeriod + TV + Online + StoresAvailability) regbasic= lm(fmla,data=train) regbasic #estimated coefficients summary(regbasic) #number of regressors $df
this gives me a model which has 184 (since there are a lot of weeks) regressors and 233undefined coefficients because of singularity.
I know that a simple linear Regression does not hold for the complex combination of Promotion(true/fals), Channels, Prices and weeks..
Long Story short: My Question is if this even is the right start for a task like this?