Perform the following steps to train the SVM:

1. Load the e1071 package:

        > library(e1071)

2. Train the support vector machine using the svm function with trainset as the input dataset and use churn as the classification category:

        > model  =svm(churn~., data = trainset, kernel="radial", cost=1, 
        gamma = 1/ncol(trainset))

3. Finally, you can obtain overall information about the built model with summary:

        > summary(model)
        Output
        Call:
        svm(formula = churn ~ ., data = trainset, kernel = "radial",
        cost = 1, gamma = 1/ncol(trainset))
    
    
        Parameters:
        SVM-Type:  C-classification 
        SVM-Kernel:  radial 
            cost:  1 
            gamma:  0.05882353 
       
        Number of Support Vectors:  691
    
        ( 394 297 )
    
          
        Number of Classes:  2 
    
        Levels: 
        yes no