the right way can have bigger influence on the performance of supervised model than the exact parameters you choose in this we will first go over the important and very common case of categorical featuresand then give some examples of helpful transformations for specific combinations of features and models categorical variables as an examplewe will use the dataset of adult incomes in the united statesderived from the census database the task of the adult dataset is to predict whether worker has an income of over $ , or under $ , the features in this dataset include the workersageshow they are employed (self employedprivate industry employeegovernment employeeetc )their educationtheir gendertheir working hours per weekoccupationand more table - shows the first few entries in the dataset table - the first few entries in the adult dataset age workclass state-gov education bachelors gender hours-per-week occupation male adm-clerical income <= self-emp-not-inc bachelors male exec-managerial <= private hs-grad male handlers-cleaners <= private th male handlers-cleaners <= private bachelors female prof-specialty <= private masters female exec-managerial <= private th female other-service <= self-emp-not-inc hs-grad male exec-managerial > private masters female prof-specialty > private bachelors male exec-managerial > private some-college male exec-managerial > the task is phrased as classification task with the two classes being income <= and > it would also be possible to predict the exact incomeand make this regression task howeverthat would be much more difficultand the division is interesting to understand on its own in this datasetage and hours-per-week are continuous featureswhich we know how to treat the workclasseducationsexand occupation features are categoricalhowever all of them come from fixed list of possible valuesas opposed to rangeand denote qualitative propertyas opposed to quantity representing data and engineering features