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https://github.com/abdellatif-laghjaj/stock-market-prediction
Welcome to the Stock Market Prediction Web App repository! This project aims to provide a user-friendly web application for predicting stock market trends using machine learning models.
https://github.com/abdellatif-laghjaj/stock-market-prediction
data-visualization machine-learning market prediction python stock-market stock-price-prediction streamlit trading
Last synced: about 12 hours ago
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Welcome to the Stock Market Prediction Web App repository! This project aims to provide a user-friendly web application for predicting stock market trends using machine learning models.
- Host: GitHub
- URL: https://github.com/abdellatif-laghjaj/stock-market-prediction
- Owner: abdellatif-laghjaj
- License: mit
- Created: 2023-12-17T11:38:48.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2023-12-24T15:03:32.000Z (11 months ago)
- Last Synced: 2024-05-20T23:53:16.425Z (6 months ago)
- Topics: data-visualization, machine-learning, market, prediction, python, stock-market, stock-price-prediction, streamlit, trading
- Language: Python
- Homepage: https://forcastify.streamlit.app/
- Size: 69.3 KB
- Stars: 8
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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
1. Clone the repository:
```bash
git clone https://github.com/abdellatif-laghjaj/stock-market-prediction-app.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app:
Run the app normally:
```bash
streamlit run main.py
```
Or run the app on save mode:
```bash
streamlit run main.py --server.runOnSave true
```
Or run the app in debug mode:
```bash
streamlit run main.py --server.runOnSave true --server.enableCORS false
```
4. Open your web browser and navigate to `http://localhost:8501` to access the app.
## Usage
1. Enter the stock symbol and select the date range.
2. Explore the interactive charts to analyze historical data.
3. 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](LICENSE).
## Acknowledgments
- Special thanks to the creators of Streamlit, Prophet, yfinance, Plotly, and scikit-learn.
Happy predicting!