Exploring the UCI SECOM data

To round out this chapter and solidify your understanding of bagging, it's time to work with a new dataset! This data is from a semi-conductor manufacturing process, obtained from the UCI Machine Learning Repository.

Each row represents a production entity. The features are measurements from sensors or points in the process. The labels represent whether the entity passes (1) or fails (-1) the test.

The dataset is loaded and available to you as uci_secom. The target variable is the 'Pass/Fail' column. Use the .value_counts() and .describe() methods to check this variable. What do you notice?