In this section, you're going to apply your knowledge of binary classification to the Cleveland CAD Dataset. This is a real-life dataset describing patients with cardiovascular disease in a hospital.

You'll build a new training pipeline to process the data, use a binary classification algorithm to train a machine learning model, and then evaluate the predictions generated by the fully trained model.

Finally, we'll explore the Accuracy, Precision, Recall and AUC metrics and I'll share some tips with you on how you can improve the predictive quality of your model.