I have created a new experiment in Azure Machine Learning and added two datasets by manually uploading csv's.
- 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 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 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 model with a Ilearnerdotnet type and give the output to the score model and evaluate the model.
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.
edit I have added the customerid to the dataset so that I have just one set now which I should split to focus on a specific customer. edit2 found these templates. will look into it https://stackoverflow.com/a/36552849/169714
