Downloaded ipy notebook for logistic regression as python script, renamed to classifier.py.
Update flask server to look for model in politwit library
Broke up main() function into get_political_tweets and get_nonpolitical_tweets, called from main. Removed hashtag storage in database, because in this prototype I'm doing full-text analysis and dont need to store co-occuring hashtags in the hashtags table.
Created separate script for pickling co-occurring hashtags given starting political and nonpolitical hashtags.
Using hashtags.py, get co-occurring hashtags for tcot and p2, and add to list of political hashtags. Run seed_tweets to add tweets with these hashtags to database, changing the tweet ratio to about 50-50 political-nonpolitical.
Added co-occurring hashtag tweets to the database, changing ratio from 75-25 to 50-50 np-p tweets.

 - Using Naive Bayes, this ratio enabled classifier to score high on both precision and recall, though it tends to overclassify tweets as political.
Pickle files created from hashtags.py script, which pickles co-occurring hashtags and their count frequency. These hashtags used to add more diverse tweets to training set.