Support Vector Regression is a Generalization of SVM into Regression problems. SVR’s are supervised learning algorithms which require a training data set including the target variable to develop an algorithm which can be used to build a model. As we already gone through the conceptual understanding of SVR algorithm. In this section we would try to implement SVR logic using Python commands/Packages.
Step 1: Import the Libraries. We would use three libraries for this analysis:
Use below command to import the libraries.
Step 2: Import the data set.
Step 3: Feature Scaling
Step 4: Fitting SVR to the data set
Step 5: Predicting a new result
Step 6: Visualizing the SVR results