Data Preprocessing Questions Medium
There are several techniques used for missing value imputation in data preprocessing. Some of the commonly used techniques are:
1. Mean/Median/Mode imputation: In this technique, missing values are replaced with the mean, median, or mode of the available data for that particular feature. This method assumes that the missing values are missing completely at random (MCAR) and does not consider the relationship between the missing values and other variables.
2. Hot deck imputation: This technique involves replacing missing values with values from similar records in the dataset. The similar records are identified based on certain matching criteria such as nearest neighbor or similar characteristics. This method assumes that the missing values are missing at random (MAR) and considers the relationship between the missing values and other variables.
3. Regression imputation: Regression imputation involves predicting the missing values based on the relationship between the missing variable and other variables in the dataset. A regression model is built using the available data, and the missing values are then estimated using this model. This method assumes that the missing values are missing at random (MAR) and considers the relationship between the missing values and other variables.
4. Multiple imputation: Multiple imputation is a technique that involves creating multiple imputed datasets by filling in the missing values with plausible values based on the observed data. This technique takes into account the uncertainty associated with the missing values and provides more accurate estimates compared to single imputation methods.
5. K-nearest neighbors imputation: In this technique, missing values are imputed by finding the k-nearest neighbors based on the available data and using their values to estimate the missing values. This method assumes that the missing values are missing at random (MAR) and considers the relationship between the missing values and other variables.
6. Expectation-Maximization (EM) imputation: EM imputation is an iterative algorithm that estimates the missing values by maximizing the likelihood of the observed data. It assumes that the missing values are missing at random (MAR) and considers the relationship between the missing values and other variables.
These techniques can be applied based on the nature of the data and the assumptions made about the missing values. It is important to carefully consider the implications of each technique and choose the most appropriate one for the specific dataset and analysis.