In this video, we'll visualize a dataset of body metrics collected by giving people a fitness tracking device. We'll go over the steps necessary to preprocess the data, then use a technique called T-SNE to reduce the dimensionality of our data so we can visualize it.

Code + challenge for this video: https://github.com/llSourcell/visualize_dataset_demo

Keagan's winning code: https://github.com/WeldFire/prepare_dataset_challenge

Vishal's runner-up code: https://github.com/erilyth/Pokemon-Type-Classification-Challenge

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

Live T-SNE demo in the browser: http://cs.stanford.edu/people/karpathy/tsnejs/

More learning resources: https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm https://indico.io/blog/visualizing-with-t-sne/ http://blog.applied.ai/visualising-high-dimensional-data/ http://machinelearningmastery.com/visualize-machine-learning-data-python-pandas/

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