Data Imputation

What is Data Imputation?

Data Imputation is the process of filling in missing values in a dataset by estimating them based on the available data. Missing data can occur for various reasons, such as sensor failures, data entry errors, or incomplete data collection. Data imputation techniques aim to estimate the missing values to maintain the integrity and utility of the dataset for analysis and modeling tasks.

Data Imputation techniques

There are several techniques for data imputation, including:

  • Mean, median, or mode imputation: Replacing missing values with the mean, median, or mode of the available data for that variable.
  • Nearest neighbor imputation: Estimating missing values based on the values of the nearest neighbors in the dataset.
  • Regression imputation: Using a regression model to predict the missing values based on the values of other variables in the dataset.
  • Stochastic imputation: Adding a random error term to the predicted values from a regression model to account for the variability in the data.
  • Multiple imputation: Repeating the imputation process multiple times to create several complete datasets, which can be combined to obtain more accurate estimates and uncertainty measures.

Resources for learning more about Data Imputation