In this blog, Here is a common strategy for handling missing data for different types of variables in this dataset.
For variables like “City,” “County,” “Armed,” “Gender,” “Flee,” and “Race” – which is a categorical variable.
It is crucial to find a way to fill in the missing values to ensure the integrity and reliability of the data analysis. The mode value substitution method offers a straightforward approach to addressing this issue.
By replacing missing values with the mode, which occurs most frequently in these individual-specific variables, the overall distribution of the data remains unchanged.
By using the mode to replace missing values, the resulting dataset maintains the original distribution of the variable. This is important because it ensures that any subsequent analysis or modeling performed on the data will not be biased or skewed due to the presence of missing values.