Ranking Sample (n dimension) Data Points I have a dataset which has a bunch of columns(features). There are around 10 features for each row (data point). Think of each data point as a person and features, some attributes defining that person.
Now I want to rank these data points. Basically create a ranking method which looks at their features and comes up with a ranking score and says a particular data point (person) is higher than others and so on.
Is there any statistics method I can use that can help here?
Using some naive methods as taking average of all feature values for a datapoint and then comparing those averages against each datapoint doesn't sound very good.
Looking if there is a way this problem can be solved statistically or some ML method.

First of all, this is not a well defined problem. One cannot simply sort n-dimensional vectors in general without making assumptions. Meanings of your features will play a key role. For example,

Using some naive methods as taking average of all feature values for a
datapoint and then comparing those averages against each datapoint
doesn't sound very good.

It indeed sounds good enough if these features were just exam results of students.
How to combine the features into a single number for sorting is your choice because you own the data. This could be vector norms, weighted averages etc.
It's best to try different approaches and ask stakeholders' opinion (this maybe you as well) if the order makes sense or not.
