For addressing issues pertaining to MNAR, the solution is improved Data Management solutions, such as Master Data Management (MDM), data integration methods such as ETL (extract/transform/load) and EAI (enterprise application integration), Data Governance, and more. The goal of Data Management is improved reliability, accuracy, security, and compliance, and reduced cost.
For addressing issues pertaining to MAR, the solutions can russia whatsapp number data involve data imputation methods. Imputation is the process of replacing missing data with substituted values. Common data imputation methods include Langrage’s Interpolation, Gregory Newton’s forward and backward interpolation algorithms, and Regression.
Missing at Random (MAR) is a very common missing data situation encountered by data scientists and machine learning engineers. This is mainly because MCAR and MNAR-related problems are handled by the IT department, and data issues are addressed by the data team. MAR data imputation is a method of substituting missing data with a suitable value. Some commonly used data imputation methods for MAR are:
In hot-deck imputation, a missing value is imputed from a randomly selected record coming from a pool of similar data records. In hot-deck imputation, the probabilities of selecting the data are assumed equal due to the random function used to impute the data.
In cold-deck imputation, the random function is not used to impute the value. Instead, other functions, such as arithmetic mean, median, and mode, are used.