Technical requirements
Some parts of this chapter require access to the DataRobot software, and some tools for data manipulation. Most of the examples deal with small datasets and therefore can be handled via Excel. The datasets that we will be using in the rest of this book are described in the following sections.
Automobile Dataset
The Automobile Dataset (source: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science) can be accessed at the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Automobile). Each row in this dataset represents a specific automobile. The features (columns) describe its characteristics, risk rating, and associated normalized losses. Even though it is a small dataset, it has many features that are numerical as well as categorical. Features are described on the web page, and the data is provided in .csv format...
