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StockPredictionWebApp

Description

StockPredictionWebApp is an advanced web application designed to provide stock trend predictions using machine learning models.

Features

  • Stock Data Retrieval: Fetch historical stock price data from Yahoo Finance based on user input.
  • Interactive Charts: Visualize stock price trends, moving averages, and predicted prices through interactive charts.
  • Streamlit Integration: Offer a user-friendly and interactive web interface built with Streamlit.

Installation

To set up the project locally, follow these steps:

git clone https://github.com/stelioszach03/StockPredictionWebApp.git
cd StockPredictionWebApp

Ensure that you have the necessary Python packages installed:

pip install numpy yfinance matplotlib pandas scikit-learn tensorflow streamlit

Usage

To run the app locally:

streamlit run your_script_name.py

Navigate to localhost:8501 in your web browser to view the app.

The app is also hosted and can be accessed directly at: StockPredictionWebApp

How It Works

  1. Data Retrieval: The app fetches historical stock data based on user input.
  2. Data Processing: Implements data normalization and prepares it for the machine learning model.
  3. Visualization: Displays interactive charts for price trends and comparisons between actual and predicted prices.

Contributing

Contributions to the StockPredictionWebApp project are welcome! If you have suggestions to improve the application or have found a bug, please open an issue or submit a pull request.

License

This project is open-source and available under the MIT License. See the LICENSE.md file for more details.