Support Vector Regression (SVR) is a machine learning algorithm that is used to solve regression problems. It is a variant of Support Vector Machines (SVM) that is used for classification problems. The goal of SVR is to find a hyperplane (line or surface) that best fits the data points while maximizing the margin, i.e., the distance between the hyperplane and the closest data points.

In Python, we can use the scikit-learn library to implement SVR.