Here is how I copied data from one S3 bucket to another:
aws s3 sync s3://bitly-challenges/hdb_sanitized s3://hughdbrown/data-capstone
import numpy | |
import scipy.stats as scs | |
def a_b_test(new_views, new_clicks, old_views, old_clicks, size=10000): | |
new_site = scs.beta(a=new_clicks + 1, b=new_views + 1).rvs(size=size) | |
old_site = scs.beta(a=old_clicks + 1, b=old_views + 1).rvs(size=size) | |
return (new_site > old_site).mean() |
Here is how I copied data from one S3 bucket to another:
aws s3 sync s3://bitly-challenges/hdb_sanitized s3://hughdbrown/data-capstone
I have a resume, but does it say what I want it to say? Specifically, do machine learning algorithms cluster my resume with the job title I would like them to?
Homeaway has data on vacation rentals. The data is not nearly so worked over as AirBNB data. Possibly there is something interesting in there to disover.
So often, job sites give candidates job listings that are far off topic. The job title is often not applicable for the candidate, and less often, the location does not match the cadidate's location.
Can we build a better system for users by applying a recommender system to existing public listings?
I was listening on NPR today and heard that within the UN, there are about a dozen different blocs that vote together on global warming issues: