This example illustrates a common data mining method, decision tree, and demonstrates how it can be used to support business decisions.

Based on the decision tree in M$6$Example.jpg file, please describe the households that are likely to have no children. Submit the answer in a word file through the link of "Module $6$ Example".

Here is the background information you need:

The Marketing Department wants to do a targeted mailing promoting the Mythic World line of figurines. Previous research has shown that the buyers of the Mythic World line of products do not have any children living at home. Unfortunately, the list purchased for the Mythic World mailing does not include the statistic on the number of children living at home for each household, but it does include the following facts about each household:

$*$ Number of cars owned

$*$ Marital status

$*$ Whether the address is a rental property or occupied by the owner

The marketing department would like to find a way, using the three facts included in the mailing list data and the information known about current customers, to predict which households have no children living at home. The mailing will then be sent only to those households likely to have no children living at home.

Wholesale customers have customer numbers below $5000.$ Retail customers have customer numbers of $5000$ and above. Of course, we should only use retail customers as our data for this mining operation.

Hint: For simplicity, the decision tree is truncated to $4$ levels $($the right side can be further extended$)$ but the left side concerning this assignment is complete. There are two end nodes that have ranges covering $0$: $0\mathrm{.}2\backslash$pm $0\mathrm{.}7$ and $0\mathrm{.}2\backslash$pm $0\mathrm{.}5.$ Please describe both nod$($Caes$.$