Backward filling by replacing with the following value.
Assume the most common value.
The first method is to delete rows or columns. When it comes to empty cells, we normally employ this strategy.
The second method involves substituting aggregated values for missing data.
The third method entails the creation of an unknown category.
The fourth method involves predicting missing values.
When no value is available in one or more of an individual's variables, missing data appears.
Deletions. Deletion in pairs. Deleting rows one by one from a list. Complete columns are being dropped. Imputation Techniques for Beginners. Using a constant value for imputation. Using statistics for imputation (mean, median, mode) Imputation of K-Nearest Neighbors.
Imputation by deduction. Rather than a statistical rule, this is an imputation rule determined by logical argument.
Imputation of the mean, median, and mode.
Imputation of Regression.
Imputation of Stochastic Regression.
Missing data must be handled either by deletion or imputation in the field of data-related research (handling the missing values with some estimation). Moving forward, data cleansing, or managing missing values, is the initial stage in data science when working with datasets.