Data imputation is a technique used in statistics and machine learning to fill in missing values in a dataset. Missing data can occur for a variety of reasons, such as measurement errors, non-response in surveys, or data entry mistakes. Imputation methods aim to estimate the missing values based on the available data, in order to preserve the integrity of the dataset and allow for further analysis. There are various imputation techniques available, ranging from simple methods such as mean imputation or mode imputation, to more complex methods such as multiple imputation or k-nearest neighbors imputation. The choice of imputation method depends on the nature of the data and the underlying assumptions of the dataset. Overall, data imputation is an important tool for researchers to handle missing data and ensure the validity and reliability of their analyses.