Support Vector Regression – Python

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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.

SVR1

Step 2: Import the data set.

SVR2

Step 3: Feature Scaling

SVR3

Step 4: Fitting SVR to the data set

SVR4

Step 5: Predicting a new result

SVR5

Step 6: Visualizing the SVR results

 

SVR6

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