Which of the following are components of a typical data-mining project? (Select two)

The selection of building a model on historical data as a component of a typical data-mining project is indeed foundational. This step involves using historical datasets to train algorithms to recognize patterns and make predictions based on past outcomes. By building a model, data scientists pick up relevant variables, establish relationships between them, and prepare themselves for making informed predictions on future data. This model serves as the crux of many data-mining projects, where the aim is to derive insights and actionable strategies from known datasets.

Another valid component that is often included in such a project is the application of a model to future cases. Once the model has been developed using historical data, it needs to be deployed effectively to make predictions or decisions based on new incoming data. This step ensures that the investment in building the model translates into practical usage that can inform business strategy or operations, leading to improved decision-making.

Other proposed components, such as building IT infrastructure, while important for supporting a data-mining project, are typically more auxiliary and do not directly pertain to the core analytical tasks. Writing a project plan inspired by business objectives is crucial for guiding the project, but it is a preliminary step rather than a core data-mining task. Therefore, the focus remains squarely on the