How to interpret a specific feature importance?
You can use one of 2 approaches:

The 1st is unsupervised:

Use PCA algorithm to extract the feature vectors best representing the dataset variance. The PCA algorithm extract new features which each of them is a linear combination out of the other features (independent from the label) when the 1st feature it extracts is most important feature and last one is the least important. Then you can retrieve the weights of each "Beta" value at the most important feature. Here is an example for that:
https://stackoverflow.com/a/34692511/6677037

Another approach is the supervised:

using the labels, which you should use carefully and not choose the features based on the test set.
With these methods you can see the most important features using "Xi Square" , "mutual information gain" etc. Then you remove the least important features.
here is the easiest way to do that:
https://hub.packtpub.com/4-ways-implement-feature-selection-python-machine-learning/

good luck.