/Project-Two-Group-1

Stock Closing Price Predictions - Model Comparison

Primary LanguageJupyter Notebook

Project Two

Technical Notes

Goal of the Project - Accurately predict stock prices using Machine Learning Models

Process

  1. 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)
  2. Create machine learning code to help predict price. (Main Model will be LSTM)
  3. Stretch goals - include creating an NLP program that will help us see how potential news articles can afffect stock price movements
  4. Stretch goals - include using GRU, KNN, SVR,and RFR to predict stock price
  5. Stretch goals - Creating Neural Networks and using Sentiment Analysis
  6. 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.