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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