Overfitting problem in model
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 ?