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Activity 6: Clustering Utah Legislative Documents (Python, sklearn)
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For the activity, I thought I would share how I used K-Means clustering to help organize documents, and use it to recommend similar documents (bills). I used sklearn, which includes a lot of useful tools that were used throughout the whole process. This was part of a class project creating a website, and so you can actually see how K-Means is integrated into a system at the page we created. 
The data was from le.utah.gov, which has bills and voting information for Utah's legislation. I ended up scraping and cleaning the data, and then using that to cluster it. You can see my Python code here and see how the clustering was done.
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