# How to predict the dealer whether pick up the goods next month?

Here, I want to predict dealers(about 600) whether pick up the goods(about 30) next month. As you see, there are about 18,000 possibilities and it's difficult to predict.

By the way, now I have nearly three years'data(2017,2018 and 2019), and now I just use the common object in the last several months as the prediction.

For example, if dealer A purchases the goods item 1 in September, October and November, then I predict he will purchases item 1 in December. However, this is not exactly accurate, and the precision is about 0.6 while the recall is about 0.3.

So any other way to predict it?

• What algorithm are you currently using to predict that? If you are using regression, how are you performing it? Dec 18 '19 at 6:29
• Now I don't use any algorithm, just use some rules to get prediction. Dec 18 '19 at 7:31
• My suggestion would be to look into logistic regression. It seems quite appropriate for your task. Dec 18 '19 at 7:43

Sure.

Question you should answer is it multiclass or mutlilabel.

In any case I would really advise you to take a look at this list of similiar problems/competitions. Since your question is open-ended I could write 10 pages of approaches. Yours best bet is to invest some time, find similiar problem and look what other people did and dig it up.

• Thanks, actually I think my problem is more difficult, because it's similar to time series prediction, and there are not many samples (only three years' data). Dec 19 '19 at 2:53