These variables are also known as categorical or binary features. This approach will be a good choice if we have a small number of distinct values for the feature to be transformed. In the Titanic data samples, the Embarked feature has only three distinct values (S, C, and Q) that occur frequently. So, we can transform the Embarked feature into three dummy variables, ('Embarked_S', 'Embarked_C', and 'Embarked_Q') to be able to use the random forest classifier.

The following code will show you how to do this kind of transformation:

# constructing binary features
def process_embarked():
    global df_titanic_data

    # replacing the missing values with the most common value in the variable
    df_titanic_data.Embarked[df.Embarked.isnull()] = df_titanic_data.Embarked.dropna().mode().values

    # converting the values into numbers
    df_titanic_data['Embarked'] = pd.factorize(df_titanic_data['Embarked'])[0]

    # binarizing the constructed features
    if keep_binary:
        df_titanic_data = pd.concat([df_titanic_data, pd.get_dummies(df_titanic_data['Embarked']).rename(
            columns=lambda x: 'Embarked_' + str(x))], axis=1)