What this book covers

Chapter 1, Foreseeing Variable Problems in Building ML Models, covers how to identify the different problems that variables may present and that challenge machine learning algorithm performance. We'll learn how to identify missing data in variables, quantify the cardinality of the variable, and much more besides.

Chapter 2, Imputing Missing Data, explains how to engineer variables that show missing information for some observations. In a typical dataset, variables will display values for certain observations, while values will be missing for other observations. We'll introduce various techniques to fill those missing values with some additional values, and the code to execute the techniques.

Chapter 3, Encoding Categorical ...

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