Replacing missing values with constants
While removing missing data entirely maybe a correct approach in many situations, this may result in a lot of information being omitted from your models.
You may find categorical columns where the missing value is a valid piece of information in itself, such as someone refusing to answer a question in a survey. In these cases, you can fill all missing values with a new category entirely, for example 'No response given'.
Deze oefening maakt deel uit van de cursus
Feature Engineering for Machine Learning in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Print the count of occurrences
print(so_survey_df['Gender']____)