Which method is commonly used to address missing data?

Imputation is a commonly used method for addressing missing data in datasets. This technique involves replacing the missing values with substituted values, allowing the dataset to remain intact and usable for analysis. Imputation can take various forms, such as filling in missing values with the mean, median, mode of the data, or using more complex statistical approaches according to the underlying relationships in the data.

Commonly, missing data can introduce bias or lead to inaccurate conclusions if not properly managed. By employing imputation, analysts can maintain the structure and integrity of the dataset, thereby enabling more robust statistical analysis or machine learning models. This method helps in generating complete datasets, which is crucial for valid results in data science projects.

In contrast, normalization focuses on scaling the values of features to fall within a certain range, which does not directly address gaps in the data. Segmentation involves dividing a dataset into distinct groups or segments based on certain characteristics, and encoding refers to transforming categorical variables into numeric formats for analysis. While these methods are essential in various stages of data preprocessing, they do not specifically target the issue of missing data like imputation does.