# How can i quantify the infuence of the independent variables over the dependent variables in linear regression with more variables than observations?

I have a dataset that has 1216 columns and 104 observations. I want to somehow quantify numerically, how much each of the columns influence a change (drop or raise) in the value of the target variable, or at least, get to know which of the columns have the most influence over the target.

At a first attempt i have thought about simply interpreting the coefficients of a linear regression model, but since there are many more variables than observations, the model will achieve a perfect fit and i don't know if in this conditions the coefficients are meaningful to determine the influence of the variables over the target.

I have also thought about lasso and ridge regression, but these methods forces the coefficientes to be near 0, so how could i know in which measure a variable influences the target if the coefficients are 0 or near 0?

My question is, which approach should i use here? Which method would be more helpful to determine and quantify in a meaningful way how much a variable is influencing the target?

I'm using Python for this analysis so if you you could point or suggest a method that can be implemented in Python it would also be very helpful.

Thank you very much in advance.