Data is the starting point for any analysis or machine learning project, but raw data often comes with problems. Errors, missing values, and inconsistencies can significantly skew results and undermine the reliability of any insights derived.

This chapter introduces the concepts of data cleaning and preprocessing. You will learn:

• What data cleaning and preprocessing entail.
• Common ways data becomes inaccurate or "dirty".
• The consequences of using low-quality data for analysis or modeling.
• A high-level overview of the typical steps involved in the cleaning process.

Understanding these fundamentals is the first step towards preparing reliable data for your projects.