There are many uses for Classification. We will discuss some of them:

Predicting disease

A doctor or hospital might have a historical dataset of BEHAVIORAL and physiological attributes of a set of patients. They could use this dataset to train a model on this historical data (and evaluate its success and ethical implications before applying it) and then leverage it to predict whether a patient has heart disease or not. This is an example of binary classification (healthy heart, unhealthy heart) or multiclass classification (healthy heart, or one of several different diseases).

Classifying images

There are several applications from companies LIKE Apple, Google, or Facebook that can

predict who is in each photo by running a classification model that has been trained on

historical images of people in your past photos. Another COMMON use case is to classify images or LABEL the objects in images.

Predicting customer churn

A more business-oriented use case might be predicting customer churn—that is, which customers are likely to stop using a service. You can do this by training a binary classifier on past customers that have churned (and not churned) and using it to try and predict whether current customers will churn.

Buy or won’t buy

Companies often want to predict whether visitors of their website will purchase a given PRODUCT. They might use information about users browsing pattern or attributes such as location in order to drive this prediction.