predict the probability of buying a product I have a huge dataset with 3 variables Company_ID, Area_code, Product_ID each one of them is a categorical variable of levels 1500,50,15 respectively,where Product_ID is the product the Company_ID is using.Each company can be located in more than one area_code and can be using more than one product.Lets call this train data.

Given a combination of Company_ID, Area_code, Product_ID ,
example- comp_025, area_012, p_10 that is not present in train data,i have to predict the probability that a company Company_ID located in area Area_Code uses the product Product_ID. So that i can recommend that product to company.

I am a beginner and having difficulty in coming up with a model. Any input is highly appreciated.

Thank you.
Seems like Naive Bayes of e1071 package could help.

Given the model product_id ~ comp_id + area_id it will construct conditional probabilities that may reveal some pattern in your data. For specifics, however, some sample data is nescessary.