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sustainable-energy-ml-analysis

A project for analyzing and predicting global energy sustainability using machine learning, including data analysis, modeling, and interactive visualization dashboard.

Sustainable Energy Analysis and Prediction Using Machine Learning

Project Overview

This project focuses on analyzing and predicting global energy sustainability using machine learning techniques. It includes data preprocessing, exploratory data analysis, model building, and visualization of results. The project is structured to provide insights into global sustainable energy trends and to demonstrate the application of ML models in this domain.

Directory Structure

• Analysis and Predicting Global Energy Sustainability Using Machine LearningYingxuan Zhang.pdf
  Project report and documentation (PDF).
• UG Finnal.ipynb
  Main Jupyter Notebook for data analysis, modeling, and results.
• Dataset/
  Contains all raw and processed data files used in the project.
• Visualization Dashboard/
  Interactive dashboard for visualizing key findings and results.

Dataset

The Dataset folder contains:

• Global ESG data (multiple CSV files)
• World and sustainable energy data (CSV)
• Additional data archives

Jupyter Notebook

The notebook UG Finnal.ipynb includes:

• Data loading and cleaning
• Exploratory data analysis (EDA)
• Feature engineering
• Machine learning model training and evaluation
• Result interpretation

Visualization Dashboard

The Visualization Dashboard folder provides an interactive web-based dashboard for visualizing the main results of the analysis. It includes:

• index.html: Main entry point for the dashboard
• js/, css/, images/, font/, picture/: Supporting resources for charts, styles, and images
• The dashboard uses ECharts and jQuery for dynamic data visualization

How to Use the Dashboard

1. Open Visualization Dashboard/index.html in your web browser.
2. Explore the interactive charts and visual summaries of the analysis results.
3. The dashboard is self-contained and does not require a backend server.

How to Run the Project

1. Open UG Finnal.ipynb in Jupyter Notebook or JupyterLab.
2. Run the cells step by step to reproduce the analysis and modeling.
3. Review the results and visualizations generated in the notebook.
4. For interactive visualization, use the dashboard as described above.

Requirements

• Python 3.x
• Jupyter Notebook / JupyterLab
• Common data science libraries: pandas, numpy, matplotlib, scikit-learn, etc.

License

This project is for academic and research purposes only.

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For any questions or suggestions, please contact the project author.

Requirements

• Python 3.x
• Jupyter Notebook / JupyterLab
• Common data science libraries: pandas, numpy, matplotlib, scikit-learn, etc.

License

This project is for academic and research purposes only.

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For any questions or suggestions, please contact the project author.

e226975 (Initial commit: Sustainable Energy ML Analysis project)