Feature Selection in Machine Learning

In the real world, data is not as clean as it's often assumed to be. That's where all the data mining and wrangling comes in; to build insights out of the data that has been structured using queries, and now probably contains certain missing values, and exhibits possible patterns that are unseen to the naked eye. That's where Machine Learning comes in: To check for patterns and make use of those patterns to predict outcomes using these newly understood relationships in the data. For one to understand the depth of the algorithm, one needs to read through the variables in the data, and what those variables represent. Understanding this is important, because when you need to prove your outcomes, based on your understanding of the data.