readme.txt

- Create 'scraped_data', 'user_features', 'user_labels' folders in current directory
- To classify users, put dataset (with usernames) in scraped_data
- Execute run.py 
	- Scraper FeatExt cell ---> output: saved in 'user_features'
	- Cleaning feature df cell  ---> output: feature dataframe with no nans saved in 'user_features'
	- Classify user cell ---> output: original scraped data + labels saved in 'user_labels'

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- run_TimelineScraper.py:
	- script for scraping user timeline only. Timelines are saved as csv in 'timeline_data' folder.

- run_FeatureExtraction.py:
	- script for feature extraction only. Requires timeline data generated by run_TimelineScraper.py. Feature matrix saved as csv in 'user_features' foler.