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Stock Price Prediction App

This Streamlit app predicts the future stock prices for selected companies using the Prophet model. It allows users to select a stock, view historical data, and generate forecasts for a specified number of years.

Stock Price Prediction

Features

  • Select Stock Dataset: Choose from a list of predefined stocks to predict.
  • Historical Data Display: View the raw historical data for the selected stock.
  • Interactive Plots: Visualize the historical stock prices with interactive plots.
  • Future Predictions: Generate and display future stock price predictions using the Prophet model.
  • Forecast Components: View components of the forecast such as trends and seasonality.

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/stock-price-prediction-app.git
cd stock-price-prediction-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 packages:

    pip install -r requirements.txt

Usage

  1. Run the Streamlit app:

    streamlit run app.py

  2. Open your web browser and navigate to http://localhost:8501 to access the app.

File Structure

  • app.py: Main application file containing the Streamlit app.
  • requirements.txt: List of Python packages required to run the app.

How It Works

  1. Data Fetching: The app fetches historical stock data from Yahoo Finance starting from January 1, 2015, to the current date.
  2. Data Visualization: Displays the raw data and an interactive plot of the historical stock prices.
  3. Model Training: Uses the Prophet model to train on the historical stock price data.
  4. Forecasting: Generates future stock price predictions based on the trained model.
  5. Plotting Forecasts: Displays the forecasted stock prices and their components.

Dependencies

  • streamlit: For creating the web application.
  • yfinance: For fetching historical stock data.
  • prophet: For time series forecasting.
  • plotly: For interactive data visualization.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

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

Acknowledgements