Implement Sci-kit Learn regression models

[cmiller8]

Here is an ML example from start to finish - it has a lot of details about the data set, the test/train data sets: https://www.kaggle.com/claytonmiller/office-building-energy-prediction-random-forest

Replicate this model using alll the regression models in this library: http://scikit-learn.org/stable/supervised_learning.html

You can do this using Python files, Jupyter notebooks, Kaggle Kernels -- how ever you'd like

[cmiller8]

Hey @talantbekov123 -- I changed the library to scikit-learn instead of shogun

Just pick the most common regression algorithms in scikit-learn and use the same performance metrics

[cmiller8]

You'll notice that I limited the for loop through the buildings to only two buildings (for singlebuilding in buildingnames[:2]:) -- remove the [:2] and it will cycle through all the buildings

Next step is to combine all of the regression models you tested into that for loop. Each model should be trained and tested for each building within that loop. Work on ways to create Python functions to make the analysis modular. There will probably need to be a for loop which calls a function to run each model and calculate the metrics.

This means that there will only be one notebook instead of a library of notebooks for each model.

Make sense?

[cmiller8]

Tested and implemented in the master notebook