FEATURE ENGINEERING : SOME FAMOUS TECHNIQUES TO HANDLE MISSING VALUES
SOME FAMOUS TECHNIQUES TO HANDLE MISSING VALUES Author: Bhaskar Kumar Das Introduction: Humans are prone to commit errors and more importantly in many cases, errors occur not due to human negligence and faults.They occur due to variety of reasons that are beyond of human imagination, and one such frequent type of error that we encounter in Data Science is due to the presence of missing values. Missing values are generally caused when who takes/prepares data set fails to include a value or a person/system becomes unwilling to share information.( E.g it is observed that men are not likely to share info about their salary and women are sometimes reluctant to share their ages).. So, being a data scientist, it becomes our duty to handle those missing values. We have two choices: 1) Drop those rows containing the NAN values. 2) Replace those NAN values by some value. Her...