The current version generates estimated current ratios using Twitter tweets.

You can find specific instruction in the JupyterNotebook

• Build Financial Analysis From Scratch-Deliver.ipynb
• Required packages are located in the requirements file. You can install the first three packages, scrapy mongodb and mysql-connector by running the cell in the above notebook.

The files in the folders are described as follows:

• Build Financial Analysis From Scratch-Deliver.ipynb -- Interactive Jupyter Notebook to make prediction
  • Structured.py -- supporting functions to construct structured features for prediction
  • ParseTweet.py -- supporting functions to parse tweets to unstructured features for prediction
  • SelectInput.py -- generate ipywidget to take input
  • country_dict.pickle -- translate country name to two letter ISO code
  • us_state_list.pickle -- list of two letter US state names
  • canada_state_list.pickle -- list of two letter Canada state names
  • sic_reg.pickle -- translate division and industry to four digit sic code
  • datax_header.pickle -- regulate structured data
  • ridgemodel.pickle -- first part of the pre-trained model using mean estimator
  • sentimentmodel.pickle -- second part of the pre-trained model using sentiment analysis

The following files are included to support TweeterScraper App:

• TweeterScraperReadMe.md, no need to read and do not need to follow its instruction
• LICENSE, license of the app
• scrapy.cfg and folder TweetScraper are part of the App. They should not be removed.
• Temporary folder Data and temporary file get_tweet.sh will be generated to collect tweets