Correct me if I am wrong anywhere.

1. I understand PCA is used to determine what component(s) of a given dataset could be of more use than the other.
2. By #1, I understand, for a structured dataset (with n columns and k rows), PCA will help us determine what column(s) could be useful to predict the outcome.
3. Plotting the explained_variance_ will show us, graphically, what components are useful, that is to say, if the graph begins to straighten, we may discard the variables there.
4. Here's my graph. There are 31 variables (columns) and the graph starts to straighten out at 11-isih, I guess?

1. So from what I have understood about PCA, we may discard the variables from 11 and beyond and the model built using this would still manage to score well.

And here lies my confusion: In the X axis, what number correspond to what variables? Like, does 1 mean that first columns of the dataframe? Obviously not, right?