Once you have featurized the data, you can build a machine learning model with your library of choice (iirc, in that notebook it is scikit-learn). See the “compiling an ML model” section of the notebook. You should be able to do something similar for your own use case. Depending on the property you are trying to predict, the results may not be accurate on your first try and might require some hyperparamter adjustment during validation.
If you are asking how to predict on new data once you have compiled your model, you need to run your new samples through essentially the same workflow. You can reuse the same objects you used to featurize/select features/train/etc. Just make sure the generated features of your new samples are the same as the ones used to train your ML model. I.e., if you did
mymodel.fit(X, y) to train, you’d use
mymodel.predict(X_new) to predict on new data.
I might recommend automatminer which wlll do a lot of these tedious steps for you!