Which of the following actions is a key part of data preprocessing?

Data preprocessing is a fundamental step in the data analysis pipeline, aimed at preparing raw data for further analysis or modeling. One of the key parts of data preprocessing is cleaning and normalizing data. This involves various tasks such as removing duplicates, addressing missing values, correcting errors, and ensuring that the data is in a consistent format. Normalization, on the other hand, adjusts the range of data values to ensure that they are on a similar scale, which can be vital for algorithms that rely on distance calculations or gradient-based optimization.

Proper cleaning and normalization are essential because they improve data quality and can significantly enhance the performance of machine learning models. High-quality, well-prepared data can lead to more accurate models and better results. Thus, focusing on this aspect of data preprocessing is critical for effective data analysis and modeling.