Most real-world machine learning problems use supervised learning. In supervised learning, the model will learn from a labeled training dataset. A label is a target variable that we want to predict. It is an extra piece of information that helps in making decisions or predictions, for example, which loan application is safe or risky, whether a patient suffers from a disease or not, house prices, and credit eligibility scores. These labels act as a supervisor or teacher for the learning process. Supervised learning algorithms can be of two types: classification or regression. A classification problem has a categorical target variable, such as a loan application status as safe or risky, whether a patient suffers from a "disease" or "not disease," or whether a customer is "potential" or "not potential...