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Schnick [Support Vector Machine Learning Tool Tester - DEMO]

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Imagine this hypothetical scenario, you are a researcher investigating a rare animal only found in the depths of the Arctic call Shnicks. Given the remoteness of these animals, only a small handful have ever been found (lets say around 5000). As a researcher, you are stuck with the question... how can I identify a Schnick?

All you have at your disposal are the research papers previously published by the handful of researchers that have seen one. In these research papers, the authors describe certain characteristics about the Schnicks they found, i.e. height, weight, number of legs etc. but all of these characteristics vary between the research papers with no discernible pattern….

How can we use this data to identify a new animal as a schnick?

One possible solution to our problem is to use a support vector machine to identify the patterns in the data and create a framework that can be used to classify animals as either a schnick or not a schnick. The first step is to create a set of data that can be used to train your support vector machine to identify schnicks. The training data is a set of inputs and matching outputs for the support vector machine to analyze and extract a pattern from.
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