How to extract features insights to change classifier decision?

I don't know if my question is specific enough but there's what I mean.

Suppose we have high school grades of students who attended a Computer Science degree and whether or not they succeeded (given a certain criteria). I want to create an adviser, which given high school grades, point out which features (grades) doesn't fit (below a certain important range for example) to twist them to reach the objective (be Good in Computer Science Degree).

Is this possible ? What is the methodology to do that? Are there resources you can advise ?

Topic methodology supervised-learning classification machine-learning

Category Data Science

1
answered at

Your "adviser" can use the correlation between the explanatory variables and the explained variable. You can also use information provided by the p-value

More details here : https://towardsdatascience.com/feature-selection-correlation-and-p-value-da8921bfb3cf

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