In which situation should we consider a dataset as imbalanced? I'm facing a problem about making a classification on a dataset. The target variable is binary (with 2 classes, 0 and 1). I have 8,161 samples in the training dataset. And for each class, I have:

class 0: 6,008 samples, 73.6% of total numbers.
class 1: 2,153 samples, 26.4%

My questions are:

In this case, should I consider the dataset I used as an imbalanced dataset?

If it was, should I process the data before using RandomForest to make a prediction?

If it was not an imbalanced dataset, could somebody tell me in which situation (like what ratio for each class) I could consider a dataset as imbalanced?
I think you can speak of imbalanced targets if (in case of a binary classification problem) the classes are not represented in a 50:50 manner. This is almost always the case.
With about 25/75 in your case, I would see this as „imbalanced“. There are some strategies to deal with this problem, such as (re)sampling so that you achieve a 50:50 balanced sample (essentially you will lose observations in the majority class here). Alternatively you can use synthetic oversampling (SMOTE) and related rechniques.
However, some packages come with built-in options to deal with unbalanced targets, e.g. sklearn‘s random forest (option class_weight). Check the docs.