Data Imputation Techniques

Defining || Analyzing|| Implementing Imputation Techniques

I request everyone to please read the complete part for your better understanding.
Before beginning with the techniques let us see what do you mean by imputation and why basically we need it. So let us first understand imputation meaning and cause due to which we need it.

Imputation is a technique to interchange the missing value with the imputed value from a selected technique, like mean(average), median, etc. Missing data occur often within the survey and longitudinal analysis. Incomplete data are problematic, notably within the presence of considerable absent data or systematic non-response patterns. And we need imputation because of the fact that in the real world there are many missing values present in the dataset and to work on any dataset you need to fill up these missing values.

So, now I hope you understand the meaning of imputation and its use. So let us now see types of imputation techniques with the help of figure.

So this is part 1 of Data Imputation Techniques, we are going to discuss CCA and all the handling missing numerical data techniques, and the rest…