Dealing with Missing Data in Machine Learning Problems

Three popular techniques for dealing with missing values in data science and Machine Learning:

1. Dropping the Columns with Missing Values: Simplest option, but unless a column is mostly missing it can lead to loss of a lot of useful information for training your ml model
2. Imputation i.e. filling the missing value with some number: Not very accurate, but usually yields better results than dropping the entire column
3. Imputating the missing values and adding a column that indicates their location in the dataset: For some datasets this leads to improved results when compared to (2) above.