Know Thy Customer (KTC) is a financial consulting company that provides personalized financial advice to its clients. As a basis for developing this tailored advising, KTC would like to segment its customers into several representative groups based on key characteristics. Peyton Blake, the director of KTC's fledging analytics division, plans to establish the set of representative customer profiles based on 600 customer records in the file ktc. Each customer record contains data on age, annual income, marital status, number of children, level of education, and whether the customer rents or owns their home. KTC's market research staff has determined that these six characteristics should form the basis of the customer clustering. Peyton has invited a summer intern, Danny Riles, into the office so they can discuss how to proceed. As they review the data on the computer screen, Peyton's brow furrows as they realize that this task may not be trivial. The data contains both categorical variables (Marital, Education, and Home) and quantitative variables (Age, Income, and Children). The data is stored in Module 6 Homework Case 1.xlsx. To address these mixed data (observations containing both quantitative and categorical variables), derive a new set of a categorical variables based on the original variables according to the following rules. Using the variables Age, Income, Married, Children, EdLevel, and Own, apply k-means clustering various values of k to identify a recommended number of clusters. Characterize these clusters.