Outlier detection and handling missing values fall under the subtle art of data quality checking. A modeling or data mining process is fundamentally a complex series of computations whose output quality largely depends on the quality and consistency of the input data being fed. The responsibility of maintaining and gate keeping that quality often falls on the shoulders of a data wrangling team.
Apart from the obvious issue of poor quality data, missing data can sometimes wreak havoc with the machine learning (ML) model downstream. A few ML models, like Bayesian learning, are inherently robust to outliers and missing data, but commonly techniques like Decision Trees and Random Forest have an issue with missing data because the fundamental splitting strategy employed by these techniques depends on an individual piece of data and not a cluster. Therefore, it is almost always imperative to impute missing data before handing it over to such a ML model...