Random Forest Multi Class Python does not improve accuracy
You can check if the features are on different scales. If they are, it is  suggested to use some type of normalization. This step is essential for many linear-based models to perform well. You can take a quick look at the distributions of each numeric feature to decide what type of normalization to use.
I am making a random forest multi-classifier model. Basically there are hundred of households which have 200+ features, and based on these features I have to classify them in one of the classes {1,2,3,4,5,6}. 
The problem I am facing is I cannot improve the accuracy of the model how much ever I can try. I have used RandomSearchCV and also GridSearchCV but I can only achieve accuracy of around 68%.
Some points to note

The sample points are unbalanced. This is the order of classes in decreasing order {1,4,2,7,6,3}. I have used class_weight = "balanced" but it does improve the accuracy.
I have tried number of estimators ranging from 50-450
I have also calculated the f1 score and not only going by accuracy to compare the models

What else do you guys suggest to improve the accuracy/f1-score? I am stuck with this problem from a long time. Any help will be highly appreciated.