# Assign more importance to recent records during training

My goal is to build a classification model in order to predict if a customer will buy a product or not (binary classification).

Since in the last months (let's say 3-4) I know that the advertising of the company is changed a bit, I want to put more emphasis on the newer records.

I know that it is possible to specify the sample_weights parameter in most of the classification algorithms, but I don't know how to properly build these weights.

Basically, I have the date in which the customers have (or have not) bought the product, but I want to understand if there is a specific way to build the weights using this information.

In addition, I would also know if someone knows some references or applications for this application (time-related sampling weights).