# Data Science Noob - Customer Scoring based on conversion probability

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?

Your question could be closed for too broad, but let's give a try. You want the enrolment probability, this sounds like a logistic regression for me. Neither categorical nor continuous data type should present a problem. You may model the additional information, applications who don't have one simply be assigned a NA category. You can include the NA category in your model.

I recommend you read the book Applied Predictive Modelling, I think it has a section on credit-card applications, which is close to what you are doing. You should try to learn from it.

• Thanks, I did think it may be too broad, but it is the broader details I'm struggling with. I will check the book out. Jan 22 '17 at 12:43

• Not a problem, you need to make the categorical fields factor using as.factor() in R.