from sklearn.svm import SVR

SVR stands for Support Vector Regression. It is a type of machine learning model used for regression analysis. It is based on the Support Vector Machine (SVM) algorithm, which is used for classification problems.

In SVR, the goal is to find a hyperplane that maximizes the margin around the training data points while still fitting the data as closely as possible. The hyperplane is used to predict the output values for new data points.

SVR can be used with various kernel functions, such as linear, polynomial, and radial basis function (RBF) kernels. The choice of kernel function depends on the nature of the data and the problem being solved.

SVR is commonly used in finance, economics, and other fields where predicting continuous values is important. It can also be used for time series forecasting, anomaly detection, and outlier detection.