An 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.
- 🔮 Time Series Forecasting: Interactive forecasting using ARIMA & Prophet models to predict future climate trends.
- 📊 Advanced Visualizations: Dynamic visualizations including time series plots, correlation heatmaps, and more to explore climate data effectively.
- 🌍 Scenario Analysis: Simulate the potential impact of scenarios like "No Policy Change," "Carbon Neutral by 2050," and "Global Collaboration."
- 📈 Data Upload & Analysis: Upload your own datasets to explore insights, correlations, and patterns.
- 📥 Download Reports: Export your analysis in multiple formats including CSV, Excel, and PDF.
- 🌐 User-Friendly Interface: A sleek, easy-to-navigate dashboard powered by Streamlit for seamless interaction.
- Clone the repository:
git clone https://github.com/ThecoderPinar/GlobalWarmingForecast.git
- Navigate to the project directory:
cd GlobalWarmingForecast - Install the required dependencies:
pip install -r requirements.txt
- Run the Application:
streamlit run app.py
- Navigate the Interface:
- 📥 Upload & Analyze Data: Upload your dataset in CSV format to explore and analyze.
- 🔮 Time Series Forecast: Generate ARIMA & Prophet model forecasts for temperature anomalies.
- 📋 Generate Reports: Download your results in various formats, including CSV, Excel, and PDF.
GlobalWarmingForecast/
├── app.py # Main Streamlit application
├── data/ # Data files
├── models/ # ARIMA & Prophet models
├── requirements.txt # Dependencies
├── README.md # Project documentation
- Forecast temperature anomalies using ARIMA and Prophet models.
- Visualize the results with interactive Plotly charts, allowing users to zoom, pan, and explore the trends.
- Visualize correlation heatmaps, scatter plots, histograms, and time series trends.
- Customize graphs based on different metrics and gain insights into relationships between variables.
- Simulate different scenarios such as:
- "No Policy Change": Forecast the impact if current policies remain unchanged.
- "Carbon Neutral by 2050": Analyze the potential effects of achieving carbon neutrality.
- "Global Collaboration": Understand how international efforts can change climate outcomes.
- Upload your own datasets (CSV format) to perform interactive analysis.
- Features include summary statistics, correlation matrices, and the ability to explore trends through different visualizations.
The Interactive Dashboard offers:
- Real-Time Forecasting: Choose different time horizons and models to see how climate trends evolve.
- Dynamic Visualizations: Toggle between different visual elements to explore the data in depth.
- Scenario Customization: Adjust inputs such as greenhouse gas emissions and renewable energy usage to see how predictions change.
We welcome contributions! Here's how you can help:
- Fork the repository
- Create a new branch (
git checkout -b feature-name) - Commit your changes (
git commit -m 'Add some feature') - Push to the branch (
git push origin feature-name) - Open a pull request
For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.
If you found this project useful, please consider giving it a ⭐ on GitHub!
For any inquiries, please reach out:
- Email: piinartp@gmail.com
- GitHub: @your-repo
- LinkedIn: Your LinkedIn
