The impact of missing values can be known after identifying what kind of variables have the missing values.
* If the data analyst finds any pattern in these missing values, then there are chances of finding meaningful insights.
* In case of patterns are not found, then these missing values can either be ignored or can be replaced with default values such as mean, minimum, maximum, or median values.
* If the missing values belong to categorical variables, then they are assigned with default values. If the data has a normal distribution, then mean values are assigned to missing values.
* If 80% values are missing, then it depends on the analyst to either replace them with default values or drop the variables.
