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Support Vector Classification

Support Vector Classification(SVC) is a supervised learning algorithm used for classification. It is widely popular for its performance and efficiency in Machine Learning and is a class of Support Vector Machines. Support Vector Classifiers are linear as well as non-probabilistic. SVM also supports the kernel method called kernel SVM which allows us to tackle non-linearity.

For a deeper understanding of SVC, use the following resources:



In this practice session, we will learn to code the SVM Classifier. We will perform the following steps to build a simple classifier using the popular Iris dataset. You can find the dataset here.


Step 1. Data Preprocessing 

  • Importing the libraries.
  • Importing dataset (Dataset Link https://archive.ics.uci.edu/ml/datasets/iris).
  • Dealing with the categorical variable.
  • Classifying dependent and independent variables.
  • Splitting the data into a training set and test set.
  • Feature scaling.


Step 2. SVM Classification 

  • Create a Support Vector classifier.
  • Feed the training data to the classifier.
  • Predicting the species for the test set.
  • Using the confusion matrix to find accuracy.


Let’s go to the practice session to code the Support Vector Classification algorithm in Python.

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  1. Support Vector Classifier


    Good insight on SVM Classification algorithm.


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