Backward fill by replacing with the following value.
Assume the Most Common Value.
Missing data causes a slew of issues. To begin with, the lack of data diminishes statistical power, which refers to the likelihood that the test will reject the null hypothesis if it is wrong. Second, missing data can lead to parameter estimation bias. Third, it may reduce the sample's representativeness.
When dealing with missing data, data scientists have two options for resolving the problem: imputation or data removal. For missing data, the imputation process generates credible predictions. When the percentage of missing data is low, it's the most beneficial.