# How to reduce the Root mean square error

I have dataset which describe "how many passenger arriving in some airport " and I would like to predict how many passenger arriving in monthly bases for next year. The features that I have is the following :

year, month, airport, number of passenger (monthly)

In the data, three out of of 50 airports are usually have huge passengers arriving.

I have used random forest classifier but the issue that i'm encounter is I have RMSE is high. As a result i see huge different between the actual value and predicting value. How to fix this issue ?

Those three airports seems like leverage points. Check this wiki topic. You can log transform your target values or simply remove those 3 airports from your training set.