# Methodology for driving score(behavior)

I am an intern at mobility data company and a Master's candidate in Statistics. I am researching about driving score which is based on a driver's driving habit. We have trip data which contains the distance, quick acceleration(seconds), quick stop, steering angles, and so on. I have read some related articles and papers but some of them contain the skills that I cannot handle such as genetic programming.

I would like to know which ML skills could be used for this unsupervised learning problem(maybe?). Below is an example of our data set.

Driver ID | Trip time | Distance | Harsh acceleration | Quick Stop | ...

1              60 mins     1 mile       180 seconds         7 times  ...

2             30 mins      0.3 mile    10 seconds           2 times ...


My goal is making a driving score based on the dataset for each driver. The scale does not matter. It could be 0-100 scale or classifiers such as poor, bad, normal, good, perfect. The problem I'm struggling with is that I have to create the target value(driving score). I guess that unsupervised learning could be a hint but I am not pretty sure about it. I welcome any kind of advice or source! Thank you so much in advance.