Project Two
Technical Notes
Goal of the Project - Accurately predict stock prices using Machine Learning Models
Process
- Track and predict future price movements of Tesla stock (TSLA). As well as competitors Ford (F), Lucid Group (LCID), General Motors (GM), and Toyota (TM)
- Create machine learning code to help predict price. (Main Model will be LSTM)
- Stretch goals - include creating an NLP program that will help us see how potential news articles can afffect stock price movements
- Stretch goals - include using GRU, KNN, SVR,and RFR to predict stock price
- Stretch goals - Creating Neural Networks and using Sentiment Analysis
- Finalizing the project involves a summation of results of project, and answering the question of if it is possible to predict future stock price movements accurately
Libraries
This Jupyter Lab notebook utilizes the following libraries:
-- path
-- collections
-- sklearn
-- imblearn
-- pandas
-- Numpy
-- Pathlib
-- matplotlib
Acknowledgements
We would like to first acknowledge the guidance and teaching of our FinTech Boot Camp Instructor, Garth Mortensen, our TA, Roberto Salazar.