I work at a University and have a project to be able to score applicants based on their likelihood to enroll (convert), using their answers to application form questions.
The applications contain name, DOB, date & time of application, country, gender, course selected, English proficiency, funding availability and some other similar fields. There are also various free-text fields, but I think these will complicate things too much to begin with.
My initial thought is to use a regression model to do this, using R. But I am a complete noob - I studied regression at uni 10 years ago....
I have had a search around and I think once I know I'm on the right path I will be able to figure the process out but I am unsure where to begin, and do not want to start by going down the wrong avenue. My main concerns are:
- Is a regression model the correct approach? If not what is?
- Are categorical fields a problem - as opposed to continuous fields?
- There is additional information which is only available for some applicants - can this be included, or do we need to use the same information for all applicants?