“prepare data for analysis, including cleaning, transformation, and normalization. Data cleaning involves identifying and correcting errors in the data, such as incorrect values, missing data, or duplicate records. It is crucial to ensure that data is accurate, complete, and consistent before proceeding with analysis. Data transformation involves converting data into a more suitable format for analysis. This may include converting data types, scaling data, and handling outliers. For example, data may need to be normalized to ensure that all values are on the same scale. Data normalization involves scaling data so that it falls within a specific range. This is important because many statistical methods assume that data is normally distributed, and normalization helps to achieve this. Common methods of normalization include z-score normalization and min-max normalization”