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The data is some 20 MB of raw weather data. The notebook can also run in the cloud on Amazon's servers, using the free tier of credits. *> Overview[1] This notebook covers various machine learning techniques that are useful for weather and climate prediction: *> K-Means Clustering *> Sequential and Tree Ensembles *> Regression *> Generalized Linear Models *> Decision Trees *> Gradient Boosting *> Deep Learning *> Dependencies *> None *> Text OutputYou can use the following tools to view the results: *> Kaggle Notebooks *> WEKA (open-source machine learning library) *> Run the notebook[2] In this example, a Kaggle Notebook will be run on the notebook's data to generate predictions of what the temperature of the weather will be for the next week. *> URL: *> Results: *> Time to run: 4m8s *> Python interpreter: Python 2.7.10 (default, Oct 13 2016, 20:43:07) [MSC v.1500 64 bit (AMD64)] *> Python notebook version: 0.12.2 (default) *> Compiled without pygments: Yes *> Running with nbconvert: Yes *> Using jupyter widgets: Yes *> Created notebook: 2016-11-01_weather_2016.ipynb *> Tags: k-means, prediction, clustering, regression, decision tree, gradient boosting, deep learning, weather [1] ====== lsiunsuex I'd love to see some open source, community driven cloud projects like Parity, Calico, etc.




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