This is my first question here so I hope I am asking correctly. I have done a few weeks worth of searching and haven't been able to find much, however I'm not a statistician so I probably wouldn't know if I came across good info.
I have a dataset that describes a food. The variables are as follows:
- Categorical variables (describing country, region, cultivar, and process method)
- Continuous (Altitude of growth)
- Discrete (A bunch of scores between 1:10 for different flavour descriptors.
My aim is to create a predictive model to predict the scores based on the categorical variables and the altitude. Firstly, is this possible and secondly, how should I approach this?
I have been using R and trying out a few ways I thought it would work however I haven't had any luck with anything I try.
- I imported my data and split it 80/20 into test and training sets
- Perform PCA on the scores and use the 90% component as my response variable in MLR
- Use a few different methods for model selection (This is pretty much where I am stuck)
I feel like I am approaching this problem in completely the wrong way and am hoping someone may give me some pointers or guide me in the right direction?
My train of thought for the PCA was because the scores otherwise would all be weighted equally when one may be more important than another. I have been making sure to dummy code my categorical variables, but I still end up with atleast one singularity at one level of a variable.
Any help would be greatly appreciated!