Exceptionally high accuracy with Random Forest, is it possible?
In order to understand if the model is performing well, first would do the following:


Plot the distribution of the classes, to understand if sampling mechanisms are required.
In case the classes are not evenly distributed, would do stratified sampling during the test-train split.
After the prediction, would plot the confusion matrix that is supported by libraries such as matplotlib or seaborn
Based on the class distribution its also important to understand what sort of metrics are required, micro-averaging / weighted / macro-averaging of precision, recall and f1 score.


This should help you evaluate if you are model is truly learning the features or if one of the classes are imbalanced causing such a spike in the results.