Stock Price Prediction using Python and the LSTM Machine Learning Model

Description

This project uses Python and the LSTM machine learning model to predict the stock price of Tesla, Inc. (TSLA). It demonstrates the application of LSTM in forecasting stock prices through a comprehensive, step-by-step implementation guide.

Table of Contents

• Installation
• Usage
• Dependencies
• Demo
• Contributing
• Acknowledgements

Installation

To use this project, you need Python and the following libraries:

• yfinance
• pandas
• numpy
• matplotlib
• keras

You can install these libraries using pip:

pip install yfinance pandas numpy matplotlib keras

Usage

To use this project, follow these steps:

1. Clone the repository to your local machine using Git:

   git clone https://github.com/PrashanthReddy47/StockPricePrediction_LSTM.git

2. Navigate to the local repository on your machine:

   cd StockPricePrediction_LSTM

3. Open the StockPricePrediction_LSTM.ipynb file in a Jupyter Notebook or Python IDE.

4. Set the ticker variable to the stock ticker you want to predict the price for (default is TSLA).

5. Run the notebook to train the LSTM model and make predictions.

Dependencies

This project relies on the following libraries:

• yfinance
• pandas
• numpy
• matplotlib
• keras

Make sure these libraries are installed before running the script.

Demo

A demonstration of the project can be found in the Jupyter Notebook StockPricePrediction_LSTM.ipynb.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or additions.

Acknowledgements

This project was completed following the tutorial by Computer Science on YouTube. Thank you to Computer Science for sharing their knowledge and expertise in this video.

Watch the tutorial