by Smruthi Balaji and Srivatsan Srinivasan contains all the necessary files, test cases, presentation and the main application in this repo
Mainly uses Python for the backend data analytics and Stream-lit for the front end application deploying.
Uses an ensembling concept model of Multiple Regression and LSTM (Long Short Term Memory) Recurrent Neural Network Model
By using both, we account for company stock policy changes.
The application predicts the ROI in each sector (5 sectors) along with the budget (per stock) and suggest where to invest.
It also gives a prototype on when the stock might hit a spike (all time high for the next 90 days) and suggests the user to watch out for the stock around that date.