Preparing the training and testing datasets

Building a classification model requires a training dataset to train the classification model, and testing data is needed to then validate the prediction performance. In the following recipe, we will demonstrate how to split the telecom churn dataset into training and testing datasets, respectively.

Getting ready

In this recipe, we will use the telecom churn dataset as the input data source, and split the data into training and testing datasets.

How to do it...

Perform the following steps to split the churn dataset into training and testing datasets:

1. You can retrieve the churn dataset from the C50 package:

        > install.packages("C50")
        > library(C50)
        > data(churn)  

2. Use str to read the structure of the dataset:

        > str(churnTrain)

3. We can remove the state, area_code, and account_length attributes, which are not appropriate for classification features:

        > churnTrain = churnTrain[,! names(churnTrain) %in% c("state...