TRINIT_OMR_ML04

Submission from Team OMR for the TRI-NIT Hackathon

Problem Statement:

ML04

Forecasting stock market prices have always been a challenging task for many business analyst and researchers. Your friend, who is interested in investing in the stock market shares of the well-known company IBM is unable to predict the company's stock market. The rate of his investment and his business opportunities in IBM's Stockmarket can increase if an efficient algorithm could be devised to predict the short term price of an individual stock.

Objectives:

The link below contains a dataset, where the TIME_SERIES_DAILY_ADJUSTED give the stock market's close value of every day with a date. Your task is to devise a model to predict the 'adjusted close' value of the next day given the stocks of all days until the current day, and developer a front-end UI (either Web app or Mobile app) that can help your friend invest the right amount of money

Link to dataset: https://www.alphavantage.co/documentation/
Link to getting an API key: https://www.alphavantage.co/support/#api-key

Model

Our method involves using a Recurrent Neural Network(RNN), in particular it uses LSTMs(Long Short Term Memory).

Tools/Languages Used:

1. Python
   
2. StreamLit
   

Members:

1. Kaustubh Khedkar
2. Harish Gumnur
3. Poorna Hegde

Demo link

https://drive.google.com/file/d/1w9NFng4gDwuaoE_bE599vF44V8ena1k9/view?usp=sharing