https://github.com/sankethsj/ml-algorithms-on-stocks
Machine Learning Algorithms for Stock Price Prediction
https://github.com/sankethsj/ml-algorithms-on-stocks
flask lstm-neural-network svelte tensorflow xgboost-algorithm
Last synced: 5 months ago
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Machine Learning Algorithms for Stock Price Prediction
- Host: GitHub
- URL: https://github.com/sankethsj/ml-algorithms-on-stocks
- Owner: sankethsj
- License: mit
- Created: 2025-07-19T01:31:47.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-07-19T01:33:45.000Z (5 months ago)
- Last Synced: 2025-07-19T06:12:54.566Z (5 months ago)
- Topics: flask, lstm-neural-network, svelte, tensorflow, xgboost-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 8.37 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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:**
```bash
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:**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install Python dependencies:**
```bash
pip install -r requirements.txt
```
### Frontend Setup
1. **Navigate to the client directory:**
```bash
cd ../../client
```
2. **Install Node.js dependencies:**
```bash
npm install
```
## 🏃♂️ Usage
1. **Run the Backend Server:**
From the `server/ml-with-stocks` directory, start the Flask server.
```bash
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.
```bash
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](LICENSE) file for details.