The objective of this project is to find correlations between valuations of a company and the stock market valuation of its shares.
I want to test this hypothesis: "When the popularity of a company increase or decrease the value of its shares changes”. If the company has an increase in the popularity of 10 and increase in the adjusted close of 40. These are data indicating that the frequency has increased 10 with respect previous day and the adjusted close value also increases with respect to previous day. These are data that are going to be studied, if there is or there isn't correlation between popularity and close adjusted, calculating their increases or decreases per share with respect to the previous day. Apply a more effective machine learning algorithm to be able to predict future data.
The company that I’ve chosen is: Amazon (USA). The data has been collected from the pages:
- Alpha Vantage - Free APIs for Realtime and Historical Stock, Forex (FX), Cryptocurrency Data, Technical Analysis, Charting, and More!
- Google Trends
The documents are:
- Amazon2018_19.csv(data training)
- Amazontesting_20.csv (data testing)
- Report Amazon v1.0.pdf (project report)
- Amazon_v1.0.ipynb (jupyter notebook, google colaboratory)
This project is licensed under the terms of the license GNU GENERAL PUBLIC LICENSE Version 3, 14 May 2020.
Copyright (C) 2020 Nazaret Serrano Romero
Attribution Foto de Ciudad creado por jcomp - www.freepik.es