This is a Streamlit-based web application built as a side project to showcase skills in Python, data visualization, and machine learning. The app is designed for financial analysis and wealth management, allowing users to:

• Search for Stocks: Retrieve and display key financial indicators and historical data for any given stock.
• Forecast Stock Prices: Experiment with different machine learning models and tuning parameters to forecast future stock prices.

• Interactive Stock Dashboard:

  • Search for a stock by its symbol (e.g., TSLA).
  • Display key indicators such as Open, High, Low, Close, and Volume.
  • Visualize historical price trends using interactive charts.

• Machine Learning Forecasting:

  • Choose from multiple machine learning models for forecasting.
  • Adjust tuning parameters to optimize predictions.
  • Compare forecasted stock prices against historical data.

• Python: Core programming language.
• Streamlit: Framework for building the interactive web app.
• Pandas: Data manipulation and analysis.
• Matplotlib/Plotly: Data visualization libraries.
• yfinance & Alpha Vantage APIs: Data sources for stock information.
• Scikit-learn/TensorFlow/Keras: Machine learning libraries for forecasting models.

1. Clone the Repository:

   git clone https://github.com/yourusername/stock-forecasting-app.git
   cd stock-forecasting-app

2. Create a Virtual Environment:

   python -m venv venv

3. Activate the Virtual Environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the Required Dependencies:

   pip install -r requirements.txt

1. Run the Application:

   streamlit run run_streamlit.py

2. Interact with the App:

   • Use the search bar to enter a stock ticker.
   • Explore the dashboard to view key indicators and historical trends.
   • Switch to the forecasting section to select a machine learning model, adjust its parameters, and view the forecasted stock prices.

Contributions are welcome! If you have suggestions or improvements, please:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Commit your changes.
• Open a pull request.

This project is licensed under the MIT License.

• Thanks to the developers of Streamlit, yfinance, and Alpha Vantage for providing powerful tools and APIs that make this project possible.
• Special thanks to the community for their continuous support and feedback.

Feel free to adjust any sections to better match your project's specifics or personal style.