Stock-Dashboard

Overview

This project is an interactive dashboard for stock market trends. It uses regression techniques for predicting stock prices. The website provides a user-friendly interface for viewing stock data, predictions, and additional features to enhance the user experience.

Features

• Responsive Design: The website is fully responsive, ensuring a seamless experience on mobile, laptop, and desktop devices.
• Stock Predictions: Using Regression for accurate stock price predictions.
• Data Visualization: Interactive charts and graphs for visualizing stock trends and predictions.
• Additional Features: Extra functionalities to provide a comprehensive stock market analysis experience.

Technologies Used

• Backend: Flask
• Frontend: HTML, CSS, Bootstrap
• Machine Learning: Linear Regression (Scikit-learn)
• Data Visualization: Plotly, Matplotlib
• Web Scraping: BeautifulSoup, Requests

Installation

1. Clone the repository: bash git clone https://github.com/Abbilaash/Stock-Dashboard.git cd Stock-Dashboard

2. Create a virtual environment: bash python -m venv venv venv\Scripts\activate

3. Install the dependencies: bash pip install -r requirements.txt

4. Run the application: bash flask run

Usage

1. Open your web browser and go to http://127.0.0.1:5000.
2. Navigate through the various sections to view stock data, predictions, and additional features.

Project Structure

• app.py: The main Flask application file.
• func.py: The main stock price prediction function.
• templates/: Contains HTML templates for rendering web pages.
• static/: Contains static files (CSS, JavaScript, images).
• models/: Contains the LSTM model and related scripts.
• data/: Contains scripts for data collection and processing.
• requirements.txt: List of Python packages required for the project.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes. Make sure to follow the coding guidelines and write appropriate tests.

Acknowledgements

• Special thanks to the open-source community for providing the tools and libraries that made this project possible.

Contact

For any questions or feedback, please contact Abbilaash at abbilaashat@gmail.com