Stock Sentinel

Introduction

Stock Sentinel is a web application designed for financial analysis and prediction using machine learning. It provides users with tools to visualize historical stock data, analyze model predictions, and explore future price forecasts for stocks or indices.

The Web App (Images)

Features

• Historical Data Visualization: View and analyze historical stock data using interactive charts.
• Predictive Modeling: Utilize machine learning models to predict future stock prices based on historical data.
• Interactive Interface: User-friendly interface with toggles for basic information and model in action views.
• Detailed Insights: Obtain detailed insights including R2 score and business summaries of selected assets.

Getting Started

Installation

1. Clone the repository:

   git clone https://github.com/your-username/stock-sentinel.git

2. Navigate into the project directory:

   cd stock-sentinel

3. Install dependencies:

   pip install -r requirements.txt

Running the App

python app.py

Open your web browser and go to http://localhost:8050/ to view the application.

Usage

• Navigate to "Basic Information" to learn about the project and usage instructions.
• Navigate to "Model in Action" to input a stock ticker and view predictions and detailed stock information.

Technologies Used

• Python
• Dash (Plotly)
• Pandas
• Scikit-learn
• LSTM (Long Short-Term Memory)

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.