Data Preprocessing Questions
The common techniques used for data imputation are:
1. Mean imputation: This technique replaces missing values with the mean of the available values for that variable.
2. Median imputation: Similar to mean imputation, this technique replaces missing values with the median of the available values for that variable.
3. Mode imputation: This technique replaces missing values with the mode (most frequent value) of the available values for that variable.
4. Regression imputation: In this technique, a regression model is used to predict missing values based on the relationship between the variable with missing values and other variables.
5. K-nearest neighbors imputation: This technique replaces missing values with the values of the nearest neighbors in the dataset.
6. Multiple imputation: This technique involves creating multiple imputed datasets by estimating missing values multiple times using statistical models, and then combining the results to obtain a final imputed dataset.
7. Hot deck imputation: This technique replaces missing values with values from similar individuals in the dataset, based on certain matching criteria.
8. Stochastic regression imputation: This technique uses a regression model to predict missing values, but also incorporates a random component to account for uncertainty.
These techniques are commonly used to handle missing data and impute values in order to ensure the integrity and completeness of the dataset for further analysis.