{"id":29684853,"url":"https://github.com/sankethsj/ml-algorithms-on-stocks","last_synced_at":"2026-04-12T09:37:27.524Z","repository":{"id":305254860,"uuid":"1022391213","full_name":"sankethsj/ml-algorithms-on-stocks","owner":"sankethsj","description":"Machine Learning Algorithms for Stock Price Prediction","archived":false,"fork":false,"pushed_at":"2025-07-19T01:33:45.000Z","size":8773,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-04-12T09:37:25.218Z","etag":null,"topics":["flask","lstm-neural-network","svelte","tensorflow","xgboost-algorithm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sankethsj.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-19T01:31:47.000Z","updated_at":"2025-07-19T01:34:58.000Z","dependencies_parsed_at":"2025-07-19T06:25:18.690Z","dependency_job_id":null,"html_url":"https://github.com/sankethsj/ml-algorithms-on-stocks","commit_stats":null,"previous_names":["sankethsj/ml-algorithms-on-stocks"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sankethsj/ml-algorithms-on-stocks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sankethsj%2Fml-algorithms-on-stocks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sankethsj%2Fml-algorithms-on-stocks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sankethsj%2Fml-algorithms-on-stocks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sankethsj%2Fml-algorithms-on-stocks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sankethsj","download_url":"https://codeload.github.com/sankethsj/ml-algorithms-on-stocks/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sankethsj%2Fml-algorithms-on-stocks/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31710792,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-12T06:22:27.080Z","status":"ssl_error","status_checked_at":"2026-04-12T06:21:52.710Z","response_time":58,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["flask","lstm-neural-network","svelte","tensorflow","xgboost-algorithm"],"created_at":"2025-07-23T03:09:18.954Z","updated_at":"2026-04-12T09:37:27.501Z","avatar_url":"https://github.com/sankethsj.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Algorithms for Stock Price Prediction\n\nThis 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.\n\n## ✨ Screenshot\n\n![Web App Screenshot](docs/Screenshot%202025-07-19%20101951.png)\n\n## 🚀 Features\n\n- **Multiple Models**: Implements and experiments with both LSTM (Long Short-Term Memory) and XGBoost models for price prediction.\n- **Data-Driven**: Trained on 10 years of historical Bank Nifty index data.\n- **Web Interface**: A Svelte-based frontend to display historical data and model predictions.\n- **Flask Backend**: A Python-based backend to serve the trained models and data.\n\n## 🛠️ Tech Stack\n\n- **Backend**:\n  - Python\n  - Flask\n  - Waitress\n  - TensorFlow / Keras\n  - XGBoost\n  - NumPy\n  - Pandas\n\n- **Frontend**:\n  - Svelte\n  - Vite.js\n  - Chart.js\n\n- **Data \u0026 Modeling**:\n  - Jupyter Notebooks for data analysis and model experimentation.\n\n## 📂 Project Structure\n\n```\n.\n├── client/         # Svelte frontend application\n├── server/         # Flask backend server\n├── model/          # Saved model files (.h5, .pkl)\n├── data/           # Raw and processed data files (.csv)\n├── *.ipynb         # Jupyter notebooks for experimentation\n└── README.md\n```\n\n## 🏁 Getting Started\n\n### Prerequisites\n\n- Python 3.8+\n- Node.js 16+\n- `pip` and `npm`\n\n### Backend Setup\n\n1.  **Clone the repository:**\n    ```bash\n    git clone https://github.com/your-username/your-repo-name.git\n    cd your-repo-name/server/ml-with-stocks\n    ```\n\n2.  **Create and activate a virtual environment:**\n    ```bash\n    python -m venv venv\n    source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n    ```\n\n3.  **Install Python dependencies:**\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n### Frontend Setup\n\n1.  **Navigate to the client directory:**\n    ```bash\n    cd ../../client\n    ```\n\n2.  **Install Node.js dependencies:**\n    ```bash\n    npm install\n    ```\n\n## 🏃‍♂️ Usage\n\n1.  **Run the Backend Server:**\n    From the `server/ml-with-stocks` directory, start the Flask server.\n    ```bash\n    waitress-serve --host 127.0.0.1 --port 5000 app:app\n    ```\n    The server will be running at `http://127.0.0.1:5000`.\n\n2.  **Run the Frontend Client:**\n    From the `client` directory, start the Vite development server.\n    ```bash\n    npm run dev\n    ```\n    The client will be available at `http://localhost:3000` (or another port if 3000 is busy).\n\n## 🧠 Model Training\n\nThe 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.\n\n## 🤝 Contributing\n\nContributions are welcome! Please feel free to submit a pull request or open an issue.\n\n## 📄 License\n\nThis project is open-source. See the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsankethsj%2Fml-algorithms-on-stocks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsankethsj%2Fml-algorithms-on-stocks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsankethsj%2Fml-algorithms-on-stocks/lists"}