Stock Market Prediction Web App Developed with Streamlit

TODO: App Icon

This web application is designed to predict stock market trends using machine learning models and visualizing the results with Streamlit.

Features

• Interactive Dashboard: User-friendly interface to input stock symbols, select date ranges, and visualize predictions.

• Machine Learning Models: Utilizes the Prophet model from Facebook for time-series forecasting and scikit-learn for additional analysis.

• Data Retrieval: Fetches historical stock data using the yfinance library.

• Beautiful Visualizations: Presents predictions and historical data with interactive charts powered by Plotly.

Technologies Used

• Streamlit: The main framework for building the web application.

• Prophet: A forecasting tool from Facebook for time-series data.

• yfinance: Retrieves financial data, including stock prices.

• Plotly: Creates interactive and visually appealing charts.

• scikit-learn: Used for machine learning tasks.

Installation

• Clone the repository:

git clone https://github.com/abdellatif-laghjaj/stock-market-prediction-app.git

• Install the required dependencies:

pip install -r requirements.txt

• Run the Streamlit app:

streamlit run main.py

Or run the app on save mode:

streamlit run main.py --server.runOnSave true

Or run the app in debug mode:

streamlit run main.py --server.runOnSave true --server.enableCORS false

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

Usage

• Enter the stock symbol and select the date range.

• Explore the interactive charts to analyze historical data.

• View the predictions generated by the machine learning model.

Screenshots

TODO: App Screenshots

Contributing

Contributions are welcome! If you'd like to enhance the app or fix any issues, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

• Special thanks to the creators of Streamlit, Prophet, yfinance, Plotly, and scikit-learn.