Feature splitting helps data analysts and data scientists create more new features for modeling. It allows machine learning algorithms to comprehend features and uncover potential information for decision-making; for example, splitting name features into first, middle, and last name and splitting an address into house number, locality, landmark, area, city, country, and zip code.
Composite features such as string and date columns violate the tidy data principles. Feature splitting is a good option if you wish to generate more features from a composite feature. We can utilize the components of a column to do this. For example, from a date object, we can easily get the year, month, and weekday. These features may directly affect the prediction model. There is no rule of thumb when it comes to breaking the features into components; this depends on the characteristics of the feature:
# Split the name column in first and last name
data['first_name']=data.name.str...
