Missing data may lead to some critical issues; hence, imputation is the methodology that can help to avoid pitfalls. It is the process of replacing missing data with substituted values. Imputation helps in preventing list-wise deletion of cases with missing values.
In the context of data analytics, imputation refers to the process of filling in missing or incomplete data values with estimated or substituted values.
There are various techniques for imputation, including:
- Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the available data for that variable.
- Forward Fill/Backward Fill: Use the last known value (forward fill) or the next known value (backward fill) to impute missing values.
- Linear Regression Imputation: Predict missing values based on a linear regression model trained on the non-missing data.
- K-Nearest Neighbors (KNN) Imputation: Replace missing values with the average of the K nearest neighbors in the feature space.
- Multiple Imputation: Generate multiple imputed datasets, each containing estimates for missing values, based on the distributional properties of the data.
The choice of imputation method depends on factors such as the nature of the data, the extent of missingness, and the assumptions about the missing data mechanism. It’s essential to consider the potential impact of imputation on the analysis and to evaluate the validity of the imputed values.