what should i do if my target variable is categorical when using decision tree? (many categorical variables) all, 

i'm trying to classify a set of features to belong to a particular company (my dependent variable). my independent variables are a mixture of continuous and categorical features. 

my data-set i am training on is labelled data with the label being the company ( the dependent var). I am not sure how i should go about dealing with my dependent variable should i use one hot encoding on the the entire data-set and then split it into training and test? 

i am uncertain whether to onehot encode before i feed into decsion tree.
This is called multiclass classification, and the encoding needed for the target variable depends on what package and model you're using.  You may be expected to one-hot encode (e.g. neural networks usually have an output neuron for each class), ordinal encode (e.g. most(?) sklearn multiclass classifiers), or leave them as strings (most R models, I'd guess?).