# imbalanced target dataset(multi class)

I have a multi-class prediction problem
but the 300classes is imbalanced
should I make it balance all 300 class will predict the better result?
is there an easier method to do this job?
if I'm using the random-forest imbalance dataset is matter?

Try over/under sampling and penalize your model by applying some custom matrix for miss classification, if required. Another point to keep note of is performance metric (avoid Accuracy paradox). Apart from deeper dive with F1, Recall & Precision; also try to look into [Kappa]1 or [ROC curves]2.