I am trying to identify what attributes are not relevant in my dataset to remove them before fitting a classifier.
The target is a categorical variable with three different values.
I also have a lot of numerical attributes.
For ANOVA, I used the following code:
grouped_test2=df[['room_type', 'price']].groupby(['room_type']) f_val, p_val = stats.f_oneway(grouped_test2.get_group('Entire home/apt')['price'], grouped_test2.get_group('Private room')['price'], grouped_test2.get_group('Shared room')['price'])
The independent variable is room_type, and the explanatory variable is price.
In this case, the f_val is equal to 1061.64 and p_val is equal to 0.
I read that 0 or values near 0 imply a relationship between the two variables but I am not sure about that?
What mean f_val is near enough to 0 to can say that the two variables are related?