{"id":23869361,"url":"https://github.com/alphacrypto246/rainfall-prediction","last_synced_at":"2026-06-12T21:01:42.220Z","repository":{"id":270653506,"uuid":"911047515","full_name":"alphacrypto246/Rainfall-Prediction","owner":"alphacrypto246","description":"This project incorporates the use of a Random Forest Classifier for predictive modeling, MLflow for model tracking and experiment management, and a Flask web application to serve the model for user interaction.","archived":false,"fork":false,"pushed_at":"2025-01-02T06:26:23.000Z","size":720,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-12T21:23:07.341Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alphacrypto246.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2025-01-02T05:59:01.000Z","updated_at":"2025-01-16T05:53:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"12031f0f-9cad-44a2-8534-25ff43e2fac2","html_url":"https://github.com/alphacrypto246/Rainfall-Prediction","commit_stats":null,"previous_names":["alphacrypto246/rainfall-prediction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/alphacrypto246/Rainfall-Prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FRainfall-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FRainfall-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FRainfall-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FRainfall-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alphacrypto246","download_url":"https://codeload.github.com/alphacrypto246/Rainfall-Prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FRainfall-Prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34262157,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-12T02:00:06.859Z","response_time":109,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2025-01-03T12:20:03.981Z","updated_at":"2026-06-12T21:01:42.209Z","avatar_url":"https://github.com/alphacrypto246.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Rainfall Prediction\n\n## Project Overview\nThe **Rainfall Prediction** project leverages machine learning techniques to predict rainfall based on various environmental factors. This project incorporates the use of a **Random Forest Classifier** for predictive modeling, **MLflow** for model tracking and experiment management, and a **Flask** web application to serve the model for user interaction.\n\n---\n\n## Features\n- **Random Forest Classifier**: Utilized to build an accurate and robust rainfall prediction model.\n- **MLflow**: Integrated for tracking experiments, storing model artifacts, and managing the machine learning lifecycle.\n- **Flask Application**: Provides an interactive interface for users to input data and receive rainfall predictions.\n\n---\n\n## Tools and Technologies\n- **Python**\n- **Scikit-learn**\n- **MLflow**\n- **Flask**\n- **Pandas**\n- **NumPy**\n- **Matplotlib** / **Seaborn** (for visualization, if applicable)\n\n---\n\n## Project Structure\n```\nRainfall-Prediction/\n|-- data/               # Dataset files\n|-- models/             # Saved machine learning models\n|-- notebooks/          # Jupyter notebooks for EDA and experimentation\n|-- app.py              # Flask application script\n|-- requirements.txt    # Python dependencies\n|-- mlruns/             # MLflow tracking directory\n|-- README.md           # Project documentation (this file)\n```\n\n---\n\n## Installation\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/alphacrypto246/Rainfall-Prediction.git\n   cd Rainfall-Prediction\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 the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. Set up MLflow tracking:\n   - Run the MLflow server locally:\n     ```bash\n     mlflow ui\n     ```\n   - Access the MLflow UI at `http://localhost:5000`.\n\n5. Run the Flask application:\n   ```bash\n   python app.py\n   ```\n   - Access the application at `http://localhost:5000`.\n\n---\n\n## Usage\n1. Open the Flask application in your browser.\n2. Input the required environmental data (e.g., temperature, humidity, pressure, etc.).\n3. Submit the form to receive a prediction on whether rainfall is expected.\n\n---\n\n## Dataset\n- The dataset used for training the model includes features such as temperature, humidity, wind speed, and pressure.\n- Ensure the dataset is placed in the `data/` directory before running the application.\n\n---\n\n## Model\n- The **Random Forest Classifier** was trained using the processed dataset.\n- Hyperparameter tuning and evaluation were tracked using **MLflow**.\n- Model performance metrics include accuracy, precision, recall, and F1-score.\n\n---\n\n## Future Improvements\n- Enhance the user interface for better usability.\n- Deploy the application to a cloud platform (e.g., AWS, Heroku, or Azure).\n- Experiment with additional machine learning models to improve prediction accuracy.\n- Incorporate real-time weather data through APIs.\n\n---\n\n## Author\n- **Arya Deep Chowdhury**\n\nFeel free to connect with me on [LinkedIn](https://www.linkedin.com/in/aryadeepchowdhury/) or reach out via email for any questions or suggestions.\n\n---\n\n## Acknowledgements\n- Scikit-learn documentation for clear guidance on model implementation.\n- Flask and MLflow communities for providing excellent resources and support.\n- [Kaggle](https://www.kaggle.com/) for dataset inspiration and ideas.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falphacrypto246%2Frainfall-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falphacrypto246%2Frainfall-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falphacrypto246%2Frainfall-prediction/lists"}