Missing values are considered to be the first obstacle in predictive modeling. Hence, it’s important to master the methods to overcome them.

Though, some machine learning algorithms claim to treat them intrinsically, but who knows how good it happens inside the ‘black box’. The choice of method to impute missing values, largely influences the model’s predictive ability. In most statistical analysis methods, listwise deletion is the default method used to impute missing values. But, it not as good since it leads to information loss.

Source: analyticsvidhya.com
Author: Manish Saraswat