It's a project that I'm working on. Here are the steps I took:
I want to make a recommendation service based on the customer data. I first used a collaborative filtering method to get the recommended products. Then I am trying to use that result as a target variable of my random forest model.
- My data: Columns A, B, C, D, E, F. Each row indicates each customer.
- Do unsupervised learning(collaborative filtering) to make a recommendation. Only used column A (which has information about the products they have bought) Choose one recommendation product for each customer, and make that into a categorical column
- Merge that column with the original data, and take out column A
- My data: Columns B, C, D, E, F, NEW_COLUMN
- Use supervised learning to get important features that explains the NEW_COLUMN
- I am trying to use supervised learning on the NEW_COLUMN because I want to get some useful predictions that could be used to explain the reason for the NEW_COLUMN(the recommended products)
This is the path that I'm taking. However, is it okay to do predictive modeling with the data that I made using predictive modeling?
Also, if there are any references, it would be a great help!