This opening section of the book aims to provide you with a solid foundation for understanding how to work with tabular data. The chapters in this section explore the unique characteristics of tabular data, the different modeling approaches (machine learning vs. deep learning), and the best practices for exploratory data analysis and preparation. By reading these chapters, you will acquire a good understanding of the peculiarities of working with tabular data. You will be prepared to tackle more advanced techniques in the following sections.
Chapter 1 introduces tabular data. The chapter explains what tabular data is, why it’s important, and how it differs from other kinds of data, such as images, audio, and text. We also introduce machine learning and deep learning concepts, and we try to unravel the controversy about using deep learning methodologies on tabular data. The chapter concludes by reviewing the unique characteristics of tabular data, which require a specific and distinct approach to analysis and modeling.
Chapter 2 explores the structure and characteristics of tabular data, highlighting potential problems and remedies to common problems with real-world data. We offer guidance on how to find tabular data in online and offline sources, especially inside business organizations. This chapter also presents a complete demonstration of how to perform an effective exploratory data analysis.