K-NN_and_preprocessing

Data preprocessing is an umbrella term that covers an array of operations data scientists will use to get their data into a form more appropriate for what they want to do with it. For example, before performing sentiment analysis of twitter data, you may want to strip out any html tags, white spaces, expand abbreviations and split the tweets into lists of the words they contain. When analyzing spatial data you may scale it so that it is unit-independent, that is, so that your algorithm doesn't care whether the original measurements were in miles or centimeters. However, preprocessing data does not occur in a vacuum. This is just to say that preprocessing is a means to an end and there are no hard and fast rules: there are standard practices, as we shall see, and you can develop an intuition for what will work but, in the end, preprocessing is generally part of a results-oriented pipeline and its performance needs to be judged in context.