Missing value imputation and outlier treatment are two distinct processes in data preprocessing that address different types of data quality issues. Both processes enhance the quality of data, which is vital for accurate data visualization and analysis.
### Missing Value Imputation
**Definition**: Missing value imputation refers to the process of replacing missing or null values in a dataset with substitute values. This can be done using


