Conclusion. Encoding categorical data is a crucial step in data preprocessing. By converting categorical data into a numeric format, machine learning models can interpret and work more effectively.

Summary <p>This book chapter explores feature engineering techniques in machine learning, covering topics such as rescaling, handling categorical data, time&#x2010;related feature engineering, missing ...

Real-world datasets frequently contain category string data, such as zip codes, names, or occupations. Many machine learning techniques require such string features to be translated to a numerical ...