Data Imputation is the process of replacing missing data with substituted values. This technique is used to handle missing values in datasets, which can occur due to various reasons such as data entry errors or incomplete data collection. Imputation methods include simple techniques like mean, median, or mode substitution, as well as more complex methods like k-nearest neighbors, regression imputation, and multiple imputation. Effective data imputation helps maintain the integrity of the dataset, allowing for accurate analysis and modeling without the bias introduced by missing data.