What are common techniques for handling missing data in large datasets?

Example Concept: Imputation is a popular technique where missing values are replaced with estimates. This can be done using mean, median, or mode for numerical data, or using more sophisticated methods like k-nearest neighbors (KNN) imputation, which uses the nearest data points to estimate missing values. Another approach is to use predictive models to fill in missing data by treating the missing value as a target variable and using the rest of the data to predict it. Alternatively, deletion methods such as listwise or pairwise deletion remove data points with missing values, though this may lead to loss of information. Some algorithms, like decision trees, can handle missing values internally without needing imputation.