Machine Learning Algorithms for Stock Price Prediction

This project uses historical data of the Bank Nifty index over the last 10 years to predict future stock prices using Machine Learning models. It features a web-based client to visualize the predictions.

✨ Screenshot

🚀 Features

• Multiple Models: Implements and experiments with both LSTM (Long Short-Term Memory) and XGBoost models for price prediction.
• Data-Driven: Trained on 10 years of historical Bank Nifty index data.
• Web Interface: A Svelte-based frontend to display historical data and model predictions.
• Flask Backend: A Python-based backend to serve the trained models and data.

🛠️ Tech Stack

• Backend:

  • Python
  • Flask
  • Waitress
  • TensorFlow / Keras
  • XGBoost
  • NumPy
  • Pandas

• Frontend:

  • Svelte
  • Vite.js
  • Chart.js

• Data & Modeling:

  • Jupyter Notebooks for data analysis and model experimentation.

📂 Project Structure

.
├── client/         # Svelte frontend application
├── server/         # Flask backend server
├── model/          # Saved model files (.h5, .pkl)
├── data/           # Raw and processed data files (.csv)
├── *.ipynb         # Jupyter notebooks for experimentation
└── README.md

🏁 Getting Started

Prerequisites

• Python 3.8+
• Node.js 16+
• pip and npm

Backend Setup

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name/server/ml-with-stocks
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install Python dependencies:

   pip install -r requirements.txt
   

Frontend Setup

1. Navigate to the client directory:

   cd ../../client
   

2. Install Node.js dependencies:

   npm install
   

🏃‍♂️ Usage

1. Run the Backend Server: From the server/ml-with-stocks directory, start the Flask server.

   waitress-serve --host 127.0.0.1 --port 5000 app:app
   

   The server will be running at http://127.0.0.1:5000.

2. Run the Frontend Client: From the client directory, start the Vite development server.

   npm run dev
   

   The client will be available at http://localhost:3000 (or another port if 3000 is busy).

🧠 Model Training

The Jupyter notebooks (.ipynb files) in the notebooks directory contain the code for data preprocessing, model training, and evaluation. You can explore these notebooks to understand the different strategies used for training the LSTM and XGBoost models.

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

📄 License

This project is open-source. See the LICENSE file for details.