Description: Restructuring involves changing the structure of data to improve its usability. This process is fundamental in the field of data preprocessing, as it allows transforming datasets into more suitable formats for analysis and processing. Restructuring can include reorganizing data into tables, converting file formats, normalizing data, and creating new variables from existing ones. By modifying the structure of the data, the aim is to facilitate its interpretation and analysis, thus optimizing the performance of machine learning algorithms and other analytical techniques. This process is especially relevant in handling large volumes of data, where efficiency and clarity are crucial. Restructuring not only improves data quality but can also help identify patterns and relationships that might otherwise go unnoticed. In summary, restructuring is a key stage in data preprocessing that transforms raw information into a more useful and accessible format for decision-making and in-depth analysis.
