Having explored machine learning concepts, specific algorithm types like regression and classification, and essential data preparation techniques, we now focus on integrating these elements. This chapter demonstrates the practical workflow for building a machine learning model using standard tools.

You will see how to:

• Load and prepare data using a library.
• Choose a suitable algorithm for a given task.
• Train a model on the prepared data.
• Use the trained model to generate predictions for new inputs.
• Evaluate the model's effectiveness using appropriate metrics.

We will use a common library, such as Scikit-learn, to illustrate these steps efficiently. The goal is to connect the concepts learned previously into a coherent, end-to-end process, moving from foundational knowledge to practical application.