Correlation feature selection followed by regression I have quarterly results data for a company with around 100 variables. Total 60 quarters results are available (total records 60).
sample data: (only few columns &amp; 10 rows)

I would like know following,

which ML algorithms / methods to be used to get the information about most important variable(s) affecting the price movement

how much each variable is contributing towards price movement (+ve or -ve)

predict, if a variable value is changed then how it'll affect the price movement


Thanks!
I think this is a case for linear regression with a lasso/ridge penalty. The lasso/ridge does „shrink“ features/variables, so that it is easy to see which features are important. Since you have 100 variables, you could opt for lasso, since lasso can also „automatically“ exclude features. Here is a lasso example in Python: https://datascience.stackexchange.com/a/53639/71442.