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Support Vector Regression

Support Vector Regression(SVR) is a supervised learning algorithm that is based on Support Vector Machines(SVM). SVM uses support vectors or margin of tolerance to identify the categories in Classification problems called Support vector Classifiers and to predict continuous features in Regression problems called Support Vector Regressors.

For deeper understanding behind the mathematics of Support Vector Machines, use the following resources:



In this practice session, we will learn to code Support Vector Regression.

We will perform the following steps to build a simple Support Vector Regressor using the Beer dataset from How To Choose The Perfect Beer Hackathon.

Step 1. Data Preprocessing

  • Importing the libraries.
  • Importing the data set.
  • Dealing with categorical data.
  • Classifying dependent and independent variables.
  • Creating training and test sets.

Step 2. Support Vector Regression

  • Create a Support vector Regressor.
  • Training the regressor with training data.
  • predicting the salary for a test set.
  • Calculating the score from Root Mean Log Squared Error.


Click on Start/Continue Hackathon to go to the practice page.

Hackathon Reviews


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  1. Helped me in learning the concept of SVM


    Helped me in learning the concept of SVM, thank you very much

  2. Good course


    Simple and easy to understand.

  3. Succinct coding techniques


    SVM has two resolutions :
    1. Classification
    2. Regression

    And SVM uses kernel tuning to improve the performance of the algorithm.

    and at the end Root Mean Square Logarithmic Error technique is used to find out the score of the algorithm.

    It was really useful to cram over SVM.


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