What is Imputation? Imputation is a technique used for replacing the missing data with some substitute value to retain most of the data/information of the dataset. These techniques are used because removing the data from the dataset every time is not feasible and can lead to a reduction in the size of the dataset to a large extend, which not only raises concerns for biasing the dataset but also leads to incorrect analysis. Fig 1:- Imputation Not Sure What is Missing Data? How it occurs? And its type? Have a look HERE to know more about it. Let’s understand the concept of Imputation from the above Fig {Fig 1}. In the above image, I have tried to represent the Missing data on the left table(marked in Red) and by using the Imputation techniques we have filled the missing dataset in the right table(marked in Yellow), without reducing the actual size of the dataset. If we notice here we have increased the column size, which is possible in Imputation(Adding “Missing” category imputation)