Regression in machine learning consists of mathematical methods that allow data scientists to predict a continuous outcome (y) based on the value of one or more predictor variables (x).
Linear regression is one of the simplest and most widely used statistical models. This assumes that there is a linear relationship between the independent and dependent variables. This means that the change in the dependent variable is proportional to the change in the independent variables.
Polynomial regression is used to model nonlinear relationships between the dependent variable and the independent variables. It adds polynomial terms to the linear regression model to capture more complex relationships.