Consider a scenario in which you are a senior marketing executive for a large tech company whose products are sold in stores internationally and online, and you have responsibility for approving investments in marketing campaigns. Your business analytics $($BA$)$ team has called a meeting with you to present the results of a model built to predict which customers are most likely to buy the tablets your company designs and manufactures $$ a product on which there is the widest profit margin. The BA team$$s goal is to inform your decisions around how to allocate marketing dollars on tablet campaigns planned for the summer months.

The BA team begins the meeting with a brief overview of the sample data used to build the model $$ namely, data on tablet purchases in one U$.$S$.$ store over a two hour span during a recent Black Friday sale. They explain that because some of the customers who purchased tablets during this window had a customer loyalty card, they had access to these shoppers$$ demographics, primary residence, purchasing history, and many other data elements that could be used as predictor variables in the model. The BA team then elaborates on the innovative machine learning algorithms deployed to build a robust model they claim can predict with $100\%$ accuracy the customers who purchased a tablet. Your peers, other executives who are far less technical than you, are beyond impressed with such a high accuracy rate and look to you to make what they see as an easy decision with respect to allocating marketing funds.

Using the knowledge you have gained throughout this course, what are the five biggest red flags that come to mind given both your role in the organization and what the BA team has just explained to you? You may list these in the form of a clarifying question you would ask the BA team or a statement about a potential shortcoming you have identified.