Overfitting problem in model I am making a project on prediction cars price given its features. I was able to scrape over 13000 examples. After cleaning and manipulating the data, I left with a little more than 11000 examples, I used 12 features of car, like mileage, year, brand and all other important stuff. After model selection, I decided to use Random forests to predict model. There are several questions, that got me interested: 

1) When tuning model, the best I can get on test set is mean absolute error more than 2200. R_2 score is 0.92
And training set error is 900-1000. R_2 score is 0.98
I couldn’t eliminate this overfitting, if it was, what I think it is. 
I tried grid search on bagging, forests even, on boosting (I know it reduces bias, not variance, but I was desperate) with different parameters, but the test error and train error with best parameters were always approximately same. 
I used simple estimators too, but they had too much bias in train and test.
I know that there is a way to get rid off this overfitting, if only I had more data, but this data is maximum I can get from the page I scraped. 
So the first questions is: are there situations when overfitting cannot be solved without proper and big dataset? Am I stuck in this situation?

2) And the second optional question is: 
Some of the features in data are categorical, so after creating dummy variables I had something like 120 features. And interesting thing is, that after scaling, which I needed for PCA, the variance I retained with arbitrary n_components was completely different, than variance I retained from data scaled just on continuous features, and not on dummy. I know that there is no meaning in scaling dummy variables, and it’s not handful. But after scaling dummy variables, in order to retain 99 percent of variance, I needed something like 110 features (originally 120), and after scaling just continuous features, to retain 99 percent of variance I needed less than half of the features (~55).
It’s strange for me, I cannot understand this behavior, is it okay at all?
Overfitting is when the test score is way below the training score, and when the latter is high. Here your test score is 0.92 which is not bat at all, so according to this score there is no overfitting here. To see that, you can dedicate a part of your dataset to be a validation set. So that, you will be able to evaluate training score, test score and validation score.

In the same idea you can use cross-validation (if you use scikit here), which will split you dataset into k-folds, k-1 of them will be used for the training and 1 for the test. This process in then repeated so that each of the folds is used as the test set. 

If the score varies at each iteration, it could possibly mean that you overfit, if not then you can say that your model is robust

For the second question what do you mean by continuous feature ?