How do you deal with predicting purchases where the purchases are extremely imbalanced and the data is extremely sparse.

Dataset has 300 million rows. Only 1 million have purchases. So the dataset is extremely sparse.

I’m taking the one million purchases and taking a random sample of one million non purchases and training my model on that.

Is this approach feasible? Are there any other approaches people would recommend. Any papers on this?

Trying to predict conversions on an ads platform.

data datascience dataset deal random sample training