Chapter 6 extends the formulation of data sets with symmetric binary, categorical, and continuous attributes. There are many applications that contain binary and nominal attributes. Figure 6.1: Internet survey data with categorical attributes shows symmetric binary attribute such as gender, a computer at home, chat online, shop online, and privacy concerns, and nominal attributes such as level of education and state. Also, Table 6.2: shows Internet survey data after binarizing categorical and symmetric attributes.

1. Search online and identify a data set with a survey information from any company or industry.
2. Use the identified dataset to develop a data set like a figure 6.1 data set for the symmetric binary attribute with different variables.
3. Using the dataset developed in #2, transform it into a binary category like Table 6.2 in chapter 6.
4. Describe the data set Table developed in #2 and #3 and explain the issues to consider when applying association analysis to the binarized data.