# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument.

Background Often, data are stored in a very compact but not human-friendly way. Think of how dishes in a menu can be stored in a restaurant database somewhere in the cloud. One way a dish object can be stored is this: { "name": "Margherita", "calories": 800, "price": 18.90, "is_vegetarian": "yes", "spicy_level": 2 } Obviously, this format is not user-readable, so the restaurant's frontend team produced many lines of code to render and display this content in a way that's more understandable to us, the restaurant users. We have a mini-version of the same task coming up. Given a list of similar complex objects (represented by Python dictionaries), display them in a user-friendly way that we'll define in these instructions. What is a "dish"? In this project, one dish item is a dictionary object that is guaranteed to have the following keys: "name": a string that stores the dish's name. "calories": an integer representing the calorie intake for one serving of the dish. "price": a float to…