You finished processing data and want to continue the model development lifecycle to identify factors driving sales conversions. What is the next step?

The correct next step after data processing, before model development, is to delineate data for training and testing. This crucial step, often called data splitting, ensures that the model is developed and evaluated on independent datasets. Training data is used to teach the model patterns, while testing data (unseen during training) is used to objectively assess its generalization ability and prevent overfitting. Without this separation, evaluating the model's true performance is impossible. Running predictions on fresh customer input data is premature; the model hasn't been built or validated yet. Monitoring model performance is also premature, as it requires a deployed, trained model. Testing and evaluating the model comes after it has been trained, using the previously delineated test set.