{"id":22429863,"url":"https://github.com/thecoderpinar/globalwarmingforecast","last_synced_at":"2025-03-27T07:13:48.699Z","repository":{"id":265559359,"uuid":"896256755","full_name":"ThecoderPinar/GlobalWarmingForecast","owner":"ThecoderPinar","description":"🌍 Global Warming Forecast Tool An advanced tool for analyzing and forecasting climate trends using ARIMA and Prophet models, with interactive visualizations and scenario simulations.","archived":false,"fork":false,"pushed_at":"2024-11-29T22:29:50.000Z","size":2174,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T07:13:44.223Z","etag":null,"topics":["arima","climate-change","data-analysis","environmental-science","forecasting","global-warming","machine-learning","prophet","streamlit","time-series-analysis","visualization"],"latest_commit_sha":null,"homepage":"https://global-warming-forecast.streamlit.app/","language":"HTML","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/ThecoderPinar.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":"2024-11-29T22:25:05.000Z","updated_at":"2024-11-29T22:34:51.000Z","dependencies_parsed_at":"2024-11-29T23:35:00.957Z","dependency_job_id":null,"html_url":"https://github.com/ThecoderPinar/GlobalWarmingForecast","commit_stats":null,"previous_names":["thecoderpinar/globalwarmingforecast"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ThecoderPinar%2FGlobalWarmingForecast","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ThecoderPinar%2FGlobalWarmingForecast/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ThecoderPinar%2FGlobalWarmingForecast/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ThecoderPinar%2FGlobalWarmingForecast/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ThecoderPinar","download_url":"https://codeload.github.com/ThecoderPinar/GlobalWarmingForecast/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245798358,"owners_count":20673902,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["arima","climate-change","data-analysis","environmental-science","forecasting","global-warming","machine-learning","prophet","streamlit","time-series-analysis","visualization"],"created_at":"2024-12-05T21:05:59.447Z","updated_at":"2025-03-27T07:13:48.676Z","avatar_url":"https://github.com/ThecoderPinar.png","language":"HTML","readme":"# 🌍 Global Warming Forecast Tool\n\n![Global Warming GIF](https://media.giphy.com/media/26ufdipQqU2lhNA4g/giphy.gif)\n\nAn advanced tool for analyzing and forecasting climate trends using ARIMA and Prophet models. Designed for researchers, policy-makers, and enthusiasts, it offers interactive visualizations, scenario simulations, and insights into global warming dynamics.\n\n---\n\n## 🚀 Features\n\n- **🔮 Time Series Forecasting:** Interactive forecasting using ARIMA \u0026 Prophet models to predict future climate trends.\n- **📊 Advanced Visualizations:** Dynamic visualizations including time series plots, correlation heatmaps, and more to explore climate data effectively.\n- **🌍 Scenario Analysis:** Simulate the potential impact of scenarios like \"No Policy Change,\" \"Carbon Neutral by 2050,\" and \"Global Collaboration.\"\n- **📈 Data Upload \u0026 Analysis:** Upload your own datasets to explore insights, correlations, and patterns.\n- **📥 Download Reports:** Export your analysis in multiple formats including CSV, Excel, and PDF.\n- **🌐 User-Friendly Interface:** A sleek, easy-to-navigate dashboard powered by Streamlit for seamless interaction.\n\n---\n\n## 🛠️ Technologies Used\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://cdn-icons-png.flaticon.com/512/5968/5968350.png\" alt=\"Python\" width=\"40\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://streamlit.io/images/brand/streamlit-logo-primary-colormark-lighttext.png\" alt=\"Streamlit\" width=\"120\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/3/37/Plotly-logo-01-square.png\" alt=\"Plotly\" width=\"40\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://altair-viz.github.io/_static/altair-logo-light.png\" alt=\"Altair\" width=\"40\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/8/84/Matplotlib_icon.svg\" alt=\"Matplotlib\" width=\"40\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://seaborn.pydata.org/_static/logo-wide-lightbg.svg\" alt=\"Seaborn\" width=\"120\" /\u003e\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/e/ed/Pandas_logo.svg\" alt=\"Pandas\" width=\"100\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://prophetpy.readthedocs.io/en/latest/_static/prophet-logo.png\" alt=\"Prophet\" width=\"100\" height=\"40\" /\u003e\u0026nbsp;\n  \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/1/1d/Scikit_learn_logo_small.svg\" alt=\"Scikit-learn\" width=\"40\" height=\"40\" /\u003e\n\u003c/div\u003e\n\n---\n\n## 📋 Table of Contents\n\n1. [Installation](#installation)\n2. [Usage](#usage)\n3. [Project Structure](#project-structure)\n4. [Features in Detail](#features-in-detail)\n5. [Interactive Dashboard](#interactive-dashboard)\n6. [Contributing](#contributing)\n7. [License](#license)\n\n---\n\n## 🛠️ Installation\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/ThecoderPinar/GlobalWarmingForecast.git\n   ```\n2. Navigate to the project directory:\n   ```bash\n   cd GlobalWarmingForecast\n   ```\n3. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n---\n\n## ▶️ Usage\n\n1. **Run the Application:**\n   ```bash\n   streamlit run app.py\n   ```\n2. **Navigate the Interface:**\n   - **📥 Upload \u0026 Analyze Data:** Upload your dataset in CSV format to explore and analyze.\n   - **🔮 Time Series Forecast:** Generate ARIMA \u0026 Prophet model forecasts for temperature anomalies.\n   - **📋 Generate Reports:** Download your results in various formats, including CSV, Excel, and PDF.\n\n---\n\n## 📂 Project Structure\n\n```\nGlobalWarmingForecast/\n├── app.py               # Main Streamlit application\n├── data/                # Data files\n├── models/              # ARIMA \u0026 Prophet models\n├── requirements.txt     # Dependencies\n├── README.md            # Project documentation\n```\n\n---\n\n## 💡 Features in Detail\n\n### 🔮 Time Series Forecasting\n- Forecast temperature anomalies using **ARIMA** and **Prophet** models.\n- Visualize the results with interactive Plotly charts, allowing users to zoom, pan, and explore the trends.\n\n![ARIMA Forecast GIF](https://media.giphy.com/media/LmNwrBhejkK9EFP504/giphy.gif)\n\n### 📊 Advanced Visualizations\n- Visualize correlation heatmaps, scatter plots, histograms, and time series trends.\n- Customize graphs based on different metrics and gain insights into relationships between variables.\n\n### 🌍 Scenario Analysis\n- Simulate different scenarios such as:\n  - **\"No Policy Change\"**: Forecast the impact if current policies remain unchanged.\n  - **\"Carbon Neutral by 2050\"**: Analyze the potential effects of achieving carbon neutrality.\n  - **\"Global Collaboration\"**: Understand how international efforts can change climate outcomes.\n\n### 📈 Data Upload \u0026 Analysis\n- Upload your own datasets (CSV format) to perform interactive analysis.\n- Features include summary statistics, correlation matrices, and the ability to explore trends through different visualizations.\n\n---\n\n## 📊 Interactive Dashboard\n\nThe **Interactive Dashboard** offers:\n- **Real-Time Forecasting**: Choose different time horizons and models to see how climate trends evolve.\n- **Dynamic Visualizations**: Toggle between different visual elements to explore the data in depth.\n- **Scenario Customization**: Adjust inputs such as greenhouse gas emissions and renewable energy usage to see how predictions change.\n\n![Interactive Dashboard GIF](https://media.giphy.com/media/3o6Zt481isNVuQI1l6/giphy.gif)\n\n---\n\n## 🤝 Contributing\n\nWe welcome contributions! Here's how you can help:\n- **Fork the repository**\n- **Create a new branch** (`git checkout -b feature-name`)\n- **Commit your changes** (`git commit -m 'Add some feature'`)\n- **Push to the branch** (`git push origin feature-name`)\n- **Open a pull request**\n\nFor major changes, please open an issue first to discuss what you would like to change.\n\n---\n\n## 📜 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## 🌟 Show Your Support\n\nIf you found this project useful, please consider giving it a ⭐ on GitHub!\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ThecoderPinar/GlobalWarmingForecast\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/stars/ThecoderPinar/GlobalWarmingForecast?style=social\" alt=\"GitHub Repo stars\"\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n---\n\n## 📧 Contact\n\nFor any inquiries, please reach out:\n- **Email:** [piinartp@gmail.com](mailto:piinartp@gmail.com)\n- **GitHub:** [@your-repo](https://github.com/your-repo)\n- **LinkedIn:** [Your LinkedIn](https://www.linkedin.com/piinartp)\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"#installation\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/🔗-Go%20to%20Installation-blue\" alt=\"Go to Installation\" style=\"margin: 10px;\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"#usage\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/▶️-Go%20to%20Usage-green\" alt=\"Go to Usage\" style=\"margin: 10px;\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"#contributing\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/🤝-Contributing-orange\" alt=\"Contributing\" style=\"margin: 10px;\"\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthecoderpinar%2Fglobalwarmingforecast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthecoderpinar%2Fglobalwarmingforecast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthecoderpinar%2Fglobalwarmingforecast/lists"}