Preprocessing is the process of preparing data for analysis. This usually involves cleaning up the data, organizing it in a format that is easier to work with, and sometimes transforming it into a form that is more suitable for the specific analysis that will be performed.

Preprocessing is often necessary because real-world data is usually messy and imperfect. It can be incomplete, incorrect, or just plain unorganized. But before any analysis can be done, the data must be prepared so that it can be processed by the software.

Types of Preprocessing

There are many different types of preprocessing, but some common ones include data cleaning, data transformation, and data normalization.

  • Data cleaning- is the process of identifying and correcting errors in the data. This might involve filling in missing values, correcting incorrect values, or dealing with outliers.
  • Data transformation- is the process of changing the data into a different form. This might be necessary to make the data easier to work with or to prepare it for a specific analysis. For example, data might be transformed from its original format (e.g., unstructured text) into a more structured format (e.g., tabular data).
  • Data normalization- is the process of making the data conform to a specific format or range. This might be necessary to ensure that the data is compatible with the software that will be used for analysis, or to make sure that all values are within a certain range (e.g., 0-1).

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