I have around
30,000 data points and for those data points I have some numerical fields like
customer_number_of_previous_purchases, etc and also some categorical string fields such as
customer_ethnicity, etc. I have lots of different fields like the above. Finally I also have an output field
did_customer_make_purchase which is a binary yes or no depending on if they made a purchase or not.
I have around 30,000 data points.
I am wanting to figure out the best way of using a model to make use of the above information so I can essentially plug in things like
customer_ethnicity, etc and it tell me whether it thinks a new customer will make a purchase or not (this can either be a yes or no or better a probability of that occurring).
I don't know what type of model is good for this kind of scenario. Could someone give me some ideas of which types of models may work good for this scenario please?
I have used neural networks before (maybe too fancy for this use case?), and know python so implementation should be fine but I am struggling with what model to investigate. Should I try to classify customers into
non purchase using some classification algorithm or is there something better?