Welcome to another lesson on Practical Challenges in Feature Engineering! As we traverse the complex terrain of Feature Engineering with the UCI's Abalone Dataset, we now turn our attention to the real-world challenges that encapsulate industry scenarios. Our goal is to help you navigate these common challenges and emerge victorious on the other end. We will address issues that include handling missing values, encoding categorical data, and remedying high dimensionality in the datasets.
Firstly, let's fetch our Abalone data set. In previous lessons, we made significant strides identifying valuable features using the techniques taught. To quickly review, we learned how to import the UCI Abalone Dataset leveraging Python and the pandas
library. Soon after, through feature extraction and selection techniques, we identified the most pertinent features for our model. Below is the code snippet we used to import our data:
Executing the above code results in a brief overview of the Abalone dataset features, showing each abalone's Sex
, Length
, Diameter
, Height
, Whole_weight
, Shucked_weight
, , , and more.
