You are hired by Blue Moon Consulting $($BMC$)$ to conduct a data mining project to improve the targeting of a new financial planning service. The service has been quite successful so far, being marketed over the last $6$ months only via a very inexpensive word$-$of$-$mouth campaign, and BMC has already garnered a large customer base without any targeting. However, the CIO of BMC believes that very accurate targeting might cost$-$effectively expand your audience to consumers that word$-$of$-$mouth would not reach.

You are given an abridged version of BMC$\text{'}$s data science proposal $($found below$).$ Identify $3_{?}$ things that you believe are weaknesses$/$flaws in their plan and explain why $(3-5$ sentences each$).$ I encourage you to refer to the questions associated with the CRISP$-$DM process and the Appendices in the back of the book if you are struggling to come up with feedback.

$"$We will build a logistic regression $($LR$)$ model to predict service uptake for a consumer using data on BMC$\text{'}$s existing customers, including their demographics and their past usage of the service. We believe that logistic regression is the best choice of method because it is a tried$-$ardtrue statistical technique, and we can check if the results make sense. If ther do make sense, then we can have confidence that the model will be accurate in predicting service uptake. We will then apply the model to BMC$\text{'}$s large database of existing customers and target those whom the LR model prediets to be the most likely to use the service."