I am running a Random Forest on data which has multiple observations per Subject. Some change with time, most don't.

Subject time  Feature1  Feature2    Feature3  Feature4      Feature5 Feature6   Target
Ann     1     Yr1       Female      20        Special-Yes   3.6      Car    0   
Ann     2     Yr2       Female      21        Special-Yes   4.0      Car    0
Ann     3     Yr3       Female      22        Special-Yes   3.2      Bus    0
Bob     2     Yr1       Male        19        Special-No    2.6      Car    0
Bob     3     Yr2       Male        20        Special-No    2.7      Car    1
Cathy   2     Yr1       Female      24        Special-No    1.6      Bus    1
Diane   3     Yr1       Female      37        Special-Yes   6.6      Bus    1

Feature 3 is age, therefore, increases by 1 each year. Feature 2 and 4 never changes with time. Feature 5 changes over time. Feature 6 may or may not change.

I don't want to model the change in values over time for each subject

All I want to be able to predict if the subject belong to class 0 or 1.

What are the implications of dropping Time and Subject Name and running a Random Forest Model out the features and the target ? Is this valid ? Why not ? Can I consider each row to be independent after dropping time and Subject ?

I can see that there are image classification models that consider multiple images per Subject such as 3 different X-Rays per patient. However, target variable in these cases are consistent across the observations for each Subject, which is not in my data set.

Can I have multiple observations / records per Subject in a classification problem ? If yes, does each record have to be unique to run RF ?

I looked at this post. But couldn't find a convincing answer. My data set looks like this.