You are building a model for a digital creative platform to predict whether a newly uploaded asset will receive strong user engagement in its first week. The goal is to help rank and surface promising content earlier, and you can use common machine learning frameworks and libraries to train and evaluate the model.
How would you approach this problem end to end, including which frameworks or libraries you would choose, how you would represent the data, which model families you would try first, and how you would evaluate and compare them before deployment?