I have created a new experiment in Azure Machine Learning and added two datasets by manually uploading csvs.
- One is from a customer of which I'd like to predict which products he will order next.
- The second dataset has the same type of data, only then from all other customers as a reference for learning.
I have productid, amount, and orderdate and orderid for grouping and putting it on a timeframe. The customer (dataset one) is always several months behind with ordering the latest products. therefor I added the dataset two with all other customers as a reference.
Also because the reference can tell which products are more popular (ordered more and by several customers) so perhaps I should add a customerid column to the dataset.
I know how to start and get the data in, and I do know that it is common to split the data for training, feed it to the train the model with an Ilearnerdotnet type and give the output to the score model and evaluate the model.
The current experiment I do not know how to choose a classification type and how this can give an output for the next three months of order. I have read some tutorials, but I just need someone who can give me some pointers.