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:

- numpy
- pyplot
- pandas

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

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