Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data by EMC Education Services

Author:EMC Education Services [EMC Education Services]
Language: eng
Format: epub, pdf
Tags: Databases
Publisher: John Wiley & Sons
Published: 2015-01-26T22:00:00+00:00

FIGURE 7-2 Example of a decision stump

To illustrate how a decision tree works, consider the case of a bank that wants to market its term deposit products (such as Certificates of Deposit) to the appropriate customers. Given the demographics of clients and their reactions to previous campaign phone calls, the bank's goal is to predict which clients would subscribe to a term deposit. The dataset used here is based on the original dataset collected from a Portuguese bank on directed marketing campaigns as stated in the work by Moro et al. [6]. Figure 7-3 shows a subset of the modified bank marketing dataset. This dataset includes 2,000 instances randomly drawn from the original dataset, and each instance corresponds to a customer. To make the example simple, the subset only keeps the following categorical variables: (1) job, (2) marital status, (3) education level, (4) if the credit is in default, (5) if there is a housing loan, (6) if the customer currently has a personal loan, (7) contact type, (8) result of the previous marketing campaign contact (poutcome), and finally (9) if the client actually subscribed to the term deposit. Attributes (1) through (8) are input variables, and (9) is considered the outcome. The outcome subscribed is either yes (meaning the customer will subscribe to the term deposit) or no (meaning the customer won't subscribe). All the variables listed earlier are categorical.

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