Which step comes directly after preparing and selecting the dataset in prediction building?

After preparing and selecting the dataset in prediction building, the next logical step is to train the model. This process involves using the selected dataset to teach the model how to make predictions based on the patterns it can learn from the data. During training, algorithms adjust parameters to minimize errors and improve accuracy, effectively allowing the model to learn relationships within the data.

Although gathering additional data, validating the model, and making predictions are important steps in the overall prediction building process, they occur at different stages. Gathering additional data might be necessary if the initial dataset is insufficient, but it precedes the training phase. Validating the model takes place after training, where you assess the model's performance on unseen data to ensure that it generalizes well. Making predictions is the final step, which relies on a trained and validated model to yield results based on new inputs. Thus, training the model is the immediate and essential next step following dataset preparation and selection.