Predictive Analytics 2 - Implemented NER-LSTM for Claim Appeals and LSTM for Stock Prediction

[varsha1224]

This PR implements the NER-LSTM model for claim appeal documents and the LSTM model for stock prediction. The folder structure has been reorganized to separate the two projects for better clarity and management.

Key updates:

• The model uses Keras version 2.11.0, a different version than previously mentioned, to pass the pylint checks.
• Faker library (Version: 37.1.0) has been introduced to generate sample claim appeal documents for testing and training purposes.

[codit04]

• add all your imported packages in requirements.txt
• refactor your code such that your backend and frontend have a clear distinction

[pranathiir]

updated requirements.txt
rearranged code into distinct backend and frontend files

[codit04]

there are few conflicts in your requirements.txt

• removal of necessary packages pytest and pylint
• unnecessary inclusions of packages like (fqdn,future) which have never been used

[codit04]

same as comment

[codit04]

please mention the format of the input that has to be given to the stocks_app.py, also add a few datasets for the app.py file and have a single frontend app with 2 pages instead of having 2 seperate pages.

[varsha1224]

we have added a few sample documents (pdf, txt, docs) under Datasets for appeals and have combined the two separate pages into a single frontend app. The format of the input that has to be given to the stocks_app.py is also mentioned.