https://github.com/judahpaul16/energy-needs
The "Energy Needs" project dives deep into the world of energy capacities and consumptions across nations.
https://github.com/judahpaul16/energy-needs
data-science energy pandas
Last synced: 2 months ago
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The "Energy Needs" project dives deep into the world of energy capacities and consumptions across nations.
- Host: GitHub
- URL: https://github.com/judahpaul16/energy-needs
- Owner: judahpaul16
- License: mit
- Created: 2023-10-17T15:20:56.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-06T01:14:29.000Z (over 2 years ago)
- Last Synced: 2025-02-19T22:36:38.262Z (over 1 year ago)
- Topics: data-science, energy, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 4.03 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# 🔋 Energy Needs: A Data Science Exploration 🔍
## 🌍 Overview
The "Energy Needs" project dives deep into the world of energy capacities and consumptions across nations. By leveraging data, we aim to uncover countries at the forefront of potential energy challenges. Through rigorous analysis, this project pinpoints nations that might face energy shortfalls by contrasting their present energy infrastructure with forecasted demands rooted in historical patterns.
## 🎯 Objectives
1. 📊 Analyze energy capacity and consumption datasets from various countries.
2. 📈 Establish consumption trends over past years and project future demands.
3. 🔍 Identify nations most likely to encounter energy deficits in the near future.
4. 🚀 Provide actionable insights for policymakers and stakeholders.
## 🛠️ Tools and Technologies
- **Data Collection**: Web scraping tools, APIs from global energy databases.
- **Data Analysis**: Python, Pandas, Numpy.
- **Visualization**: Matplotlib, Seaborn, Tableau.
- **Machine Learning (for projections)**: Scikit-learn, TensorFlow.
## 📚 Data Sources
- [Global Electricity Statistics (1980-2021)](https://www.kaggle.com/datasets/akhiljethwa/global-electricity-statistics)
## 📝 Methodology
1. **Data Collection**: Gather data from multiple sources to ensure comprehensiveness.
2. **Data Cleaning**: Preprocess and cleanse the data to ensure accuracy and reliability.
3. **Exploratory Data Analysis**: Understand patterns, outliers, and distributions in the dataset.
4. **Trend Analysis**: Analyze historical data to determine energy consumption trends.
5. **Predictive Modeling**: Use machine learning models to project future energy consumption.
6. **Insight Generation**: Extract and document critical findings from the analysis.
## 💡 Key Findings
- [Preliminary findings can be listed here]
- [Charts n Stuff]
- ...
## 🤝 Contributing
Interested in contributing to "Energy Needs"? Please read our [CONTRIBUTING](CONTRIBUTING.md) file for guidelines on how to pitch in!
## 📜 License
This project is licensed under the MIT License. For details, see the [LICENSE](LICENSE) file.
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