In this section, we are going to discuss how to build predictive models using machine learning algorithms in R. More specifically, we will learn how to build a predictive model using a random forest algorithm, as well as how to tune the random forest model, and evaluate the performance of the model. We will be mainly using the caTools, ROCR, and randomForest packages to evaluate, visualize, and build machine learning models that predict the likelihood of customer marketing engagement. For those readers who would like to use Python instead of R for this exercise, you can refer to the previous section.
For this exercise, we will be using one of the publicly available datasets from IBM, which can be found at this link: https://www.ibm.com/communities/analytics/watson-analytics-blog/marketing-customer-value-analysis/. You can...
