What technique is used for replacing missing values in a dataset?

Imputation is the technique specifically designed to address the problem of missing values in a dataset. This method involves estimating the missing data points based on the values of other data points within the dataset. There are various approaches to imputation, such as replacing missing values with the mean, median, or mode of the respective feature, or using more complex algorithms like k-nearest neighbors or regression. By using imputation, analysts can retain the integrity of the dataset without having to discard records with missing values, thus allowing for more robust data analysis and modeling.

In contrast, data profiling refers to the process of examining and analyzing data from existing sources to understand its structure, content, and quality, but it does not specifically address missing values. Data append involves adding new data from external sources to an existing dataset, which is unrelated to handling missing values. Aggregation is the technique of summarizing data points, typically for analysis or reporting purposes, and does not directly relate to replacing missing values.