How to transform time series data to apply supervised learning algorithms to it? Apologies in advance for what may be a very basic question.

I have a dataset consisting of marketing calls to different clients, which include the timestamp for the call.
My goal is to train a model to predict if whether a customer will answer a call or ignore it based on the call time, as well as other features like caller id, etc.

The issue is that the outcome of the call is clearly time dependent, that is, the order of the data points per client matters for the prediction of the dependent variable at a given time.

My question is, how can I transform my features, so that I can use standard classification algorithms like Logistic Regression/Random Forest to classify a new data point? Are these algorithms effective for these scenarios? If so, how  should I proceed to take the previous data points into consideration?

I have read that time series data can be converted to a supervised learning problem, by including lead and lag columns of the dependent variable. But since my test data will not have those columns, I am confused to how they might help me.

Thank you very much.
You need to transform your timestamp column into several numerical/categorical columns representing that specific timestamp. After converting that timestamp column into features, you can simply throw supervised algorithms on the problem.

You could leverage for example the fast.ai function add_datepart that you can find here: