In this section, we are going to discuss how to build predictive models using machine learning algorithms in Python. More specifically, we will learn how to build a predictive model using the 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 pandas, matplotlib, and scikit-learn packages to analyze, visualize, and build machine learning models that predict the likelihood of customer marketing engagement. For those readers who would like to use R instead of Python for this exercise, you can skip to the next 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...
