Regression analysis is a classic example of supervised learning. It's a method that helps you in knowing the relationship between a dependent variable and many other independent variables in the dataset.
The regression models can be broadly classified into logistic and linear models. In the case of logistic regression, the dependent variable is binomial and our output will be a probability of the categorical outcome; a problem of this nature is generally called a classification problem. On the other hand, in linear regression, the dependent variable is continuous in nature and the problems of this nature are called regression problems.
Let's take one example for each classification and regression problem. A typical classification model would be predicting if a banking customer would default his loan using various other details about the customer, such as his demographic, historic, and other details, whereas when we predict how much money a particular...