Thus far, we have gone over several methods of imputing missing values in our categorical and numerical data, encoding our categorical variables, and creating custom transformers to fit into a pipeline. We also dove into several feature construction methods for both numerical data and text-based data. 

In the next chapter, we will take a look at the features we have constructed, and consider appropriate methods of selecting the right features to use for our machine learning models.