Project 2: Stock Price Prediction

Overview

This project utilizes Long Short-Term Memory (LSTM) neural networks for predicting stock prices. The LSTM model is trained on historical stock data and then used to forecast future prices. The main components of this project include:

  • LSTM Model:
    • Utilizes LSTM architecture for time-series prediction.

  • Data Analysis:
    • Python's Plotly library is used for data analysis, providing interactive visualizations.

  • Data Visualization:
    • Plotly is also used for data visualization, allowing for insightful representations of stock price trends.

  • Web Deployment:
    • The prediction model is deployed on a web page built with Django, HTML, CSS, and Bootstrap, providing users with an interactive interface to input parameters and obtain predictions.

Before running the project, ensure you have the following installed:

  • Python (version 3.10.3)
  • Plotly
  • Django
  • HTML/CSS/Bootstrap (for web page deployment)

Installation:

  1. Clone this repository.
  2. Install the required dependencies using pip.

Usage:

  1. Train the LSTM model using historical stock data.
  2. Run the Django server to deploy the web page.
  3. Access the web page in your browser and input parameters to get stock price predictions.

Contributing:

  1. Fork the repository.
  2. Create a new branch.
  3. Make your changes.
  4. Commit your changes.
  5. Push to the branch.
  6. Create a new Pull Request.

Acknowledgments:

Special thanks to those who provided the historical stock data.

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This Project’s GitHub Repository