This project is a stock prediction model implemented using Python programming language, Jupyter Notebook and various machine learning algorithms to predict the stock prices of a company based on historical stock data.
Table of Contents
Installation Usage Data Sources Model Training and Evaluation License
To run this project, you need to have Jupyter Notebook installed on your computer. If you do not have Jupyter Notebook installed, you can download it here.
After installing Jupyter Notebook, you can clone the repository or download the project as a zip file. Then, open the .ipynb file in Jupyter Notebook.
Once you have opened the Jupyter Notebook file, you can run each cell one by one by pressing the Shift+Enter keys. Make sure to run the cells in order as some cells depend on previous cells.
The Jupyter Notebook contains detailed comments explaining each step of the code and the output of each cell.
This project uses historical stock data obtained from Kaggle. The stock data includes the Open, High, Low, Close, and Volume values for each day.
This project uses several machine learning algorithms to predict the stock prices of a company. The algorithms used are:
Long Short-Term Memory (LSTM) Neural Network
The performance of each algorithm is evaluated using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. The algorithm with the lowest RMSE and MAE is considered the best performing algorithm.
License
This project is licensed under the MIT License - see the LICENSE file for details.