Use Case:
In this case study, we will build a decision tree to predict the income of a given population, which is labelled as <=$50K and >$50K. The attributes (predictors) are age, working class type, marital status, gender, race etc. In the following sections, we'll: - clean and prepare the data, - build a decision tree with default hyperparameters, - understand all the hyperparameters that we can tune, and finally - choose the optimal hyperparameters using grid search cross-validation.