What process is crucial for making raw data interpretable and analyzable by machine learning algorithms?

Data preparation is essential for transforming raw data into a format that is suitable for analysis and interpretation by machine learning algorithms. This process involves several key steps, including cleaning the data to remove errors and inconsistencies, handling missing values, encoding categorical variables, and normalizing or scaling numerical features. By ensuring that the data is properly structured and formatted, machine learning models can more effectively learn patterns and make accurate predictions.

Data exploration, while an important step in understanding the characteristics and relationships in data, typically follows data preparation. It focuses on analyzing the data to gain insights and identify trends, but it does not prepare the data for modeling directly. Data visualization helps in interpreting data and can support data exploration, but it is not a direct step in preparing data for machine learning. Data storage, on the other hand, pertains to how and where data is kept and does not influence the interpretability or analyzability of the data itself.

Thus, data preparation stands out as the critical process that facilitates the transition from unprocessed raw data to a structured format usable by machine learning algorithms.