A project for CS 451 Data Science Course. This application forecasts stock prices using a myriad of Machine Learning models, leveraging historical stock prices, movements, VIX data, and more. Features
- Forecast stock prices on various time horizons.
- Uses models ranging from LSTM, Logistic Regression, to Classifiers.
- Integrates a diverse set of data including: - VIX data - Historical stock prices - Stock movements
- Python 3.8+ (It may work on older versions but this is the version it was tested on)
- Relevant Python libraries (e.g. pandas, numpy, tensorflow, keras, sklearn)
Clone this repository:
git clone https://github.com/ebuinevicius/ML-stock-prediction.git cd ML-stock-prediction
Install the required packages:
pip install -r requirements.txt
Jupyter notebook should be used to run the notebooks within the notebooks directory to test and tweak various models.
Models Included
- LSTM (Long Short-Term Memory): A recurrent neural network used for sequence prediction problems.
- Logistic Regression: Used primarily to predict the direction of stock price movement.
- Classifiers: Predict stock price movements (e.g., up or down).
This application is meant for educational purposes only. Predictions made by this tool should not be considered as financial advice. Always consult with a financial advisor before making any trading decisions.