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It provides visualizations of predicted demand trends and allows users to input additional data for more accurate forecasts.\n\n## Problem Statement\n**Problem Statement 5: Predictive Analysis for Part Numbers**  \nCreate a web application that predicts the future demand for part numbers based on historical data. The historical data might be sparse, so the prediction algorithm should be capable of working effectively with limited training data. The system should visualize the predicted demand trends and allow users to input additional data for more accurate predictions. The frontend is developed using React, while the backend is developed using Python.\n\n## Tech Stack\n- **Frontend**: React.js\n- **Backend**: FastAPI (Python)\n- **Data Handling**: Pandas\n- **Deployment**: Render (Backend), Render (Frontend)\n\n## Features\n- Predict future demand based on historical data.\n- Visualize demand trends with interactive plots.\n- Allow users to add new data for improved prediction accuracy.\n- User-friendly interface for seamless interaction.\n\n## Installation\n\n### Prerequisites\n- Node.js and npm (for the frontend)\n- Python 3.x (for the backend)\n- A code editor (e.g., Visual Studio Code)\n\n### Frontend Setup\n1. Navigate to the frontend directory:\n   ```bash\n   cd frontend\n   ```\n2. Install dependencies:\n   ```bash\n   npm install\n   ```\n3. Start the development server:\n   ```bash\n   npm start\n   ```\n\n### Backend Setup\n1. Navigate to the backend directory:\n   ```bash\n   cd backend\n   ```\n2. Create a virtual environment (optional but recommended):\n   ```bash\n   python -m venv venv\n   ```\n3. Activate the virtual environment:\n   - On Windows:\n     ```bash\n     venv\\Scripts\\activate\n     ```\n   - On macOS/Linux:\n     ```bash\n     source venv/bin/activate\n     ```\n4. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n5. Run the FastAPI server:\n   ```bash\n   python main.py\n   ```\n\n## Deployment\n- The backend is deployed on **Render**.\n- The frontend is deployed on **Render**.\n\n## Contributing\nContributions are welcome! Please feel free to submit a pull request or open an issue if you have suggestions or improvements.\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n- Thank you to everyone who contributed to this project.\n- Special thanks to the resources that helped in the development of this application.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgauranshgoel123%2Fpredictive-demand-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgauranshgoel123%2Fpredictive-demand-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgauranshgoel123%2Fpredictive-demand-analysis/lists"}