Decision Tree : how to determine target in a model with no labels? I am studying classification algorithms using decision tree approach in Python.
I would have some questions on this topic, specifically regarding the target (y) in my dataset.
I have a dateset made by 20000 observations and a few fields:

Customer
recorded date
amount
status (if married or not)
children (if any children in the family)
nationality (if American or not)

And so on.
Most of these fields are binary (yes/no).
Based on this I would like to determine if this customer is trustworthy or not. As you can see, I have no label about trusting, but I have some initial information: for example the amount. If the amount is 0 or &lt; 0, the customer has no money so he/she can be considered not trusted.
Then, I could consider status: if he/she is married, then it could be considered trustworthy, as there could be another salary to take into account.
And so on.
My doubt are in splitting my dataset, as it asks about y variable.
What would it be in this case? I have no explicit target..
Use Clustering under unsupervised learning. That will categorize the customer based on similar parameters. You can define the number of cluster you need to form, in you case it is two(trustworthy and not). If there are more features it will be more helpful for the algorithm.
This might help.
https://towardsdatascience.com/an-introduction-to-clustering-algorithms-in-python-123438574097