https://github.com/dragonscypher/sustaining-balance

https://github.com/dragonscypher/sustaining-balance

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/dragonscypher/sustaining-balance
• Owner: dragonscypher
• License: mit
• Created: 2024-05-14T22:14:24.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-14T22:25:39.000Z (8 months ago)
• Last Synced: 2024-05-15T17:55:37.102Z (7 months ago)
• Language: Jupyter Notebook
• Size: 10.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sustaining Balance: Innovative Data Approaches for Food Security and Environment 🌍🍽️

## Abstract

Our research, "Sustaining Balance: Innovative Data Approaches for Food Security and Environment," explores how global food security needs differ by region and how they will evolve over the coming decades. We analyze key dimensions of food security and their implications for environmental stewardship. 

## Introduction

We move beyond elementary statistical study to understand the delicate symphony of preservation unfolding across the globe. This project combines agricultural activity, governance, and environmental equilibrium.

## Tech Stacks Used

### Programming Languages

- **Python** 🐍

### Libraries and Frameworks

- **Data Manipulation**:

  - Pandas 📊

  - NumPy 🔢

- **Data Visualization**:

  - Matplotlib 📈

  - Seaborn 🌈

- **Natural Language Processing (NLP)**:

  - spaCy 🔠

  - NLTK 📖

  - Hugging Face Transformers 🤗

- **Machine Learning**:

  - Scikit-learn 🤖

- **Geospatial Analysis**:

  - GeoPandas 🗺️

- **Deep Learning**:

  - TensorFlow 🌐

  - PyTorch 🔥

### Tools

- **Jupyter Notebooks** 📒

- **Tableau** 📊

### APIs and Datasets

- **FAO Datasets** 🌾

- **World Bank Indicators** 🏦

- **Kaggle Datasets** 💾

### Chatbot Integration

- **Transformers** 🤗

- **AutoTokenizer** from Hugging Face

- **AutoModelForQuestionAnswering** and **AutoModelForSequenceClassification** from Hugging Face

## Data Collection and Preparation

We collected and prepared data from various sources, including FAO and World Bank datasets. The data cleaning process involved:

- Identification of missing values 🕵️

- Treatment of missing data 🔄

- Data type conversions 🔀

- Outlier detection and management 🚨

- Consistency verification ✔️

- Normalization/Standardization ⚖️

## Analysis and Visualization

Our analysis involved:

- Geospatial analysis 🌍

- Sentiment analysis 🗣️

- Machine learning algorithms 🤖

We visualized the results using Tableau dashboards.

## Results

Key findings include:

- Renewable energy consumption significantly impacts ESG environmental ratings.

- Political stability is crucial for high ESG ratings.

- Public sentiment correlates with environmental and governance indicators.

## Discussion

Our research highlights the importance of combining environmental practices, governance quality, and public sentiment to address global food security challenges.

## Conclusion

This project aims to guide policies and spark efforts to secure food for all while being mindful of our planet.

## References

1. [World Bank - World Development Indicators](https://databank.worldbank.org/reports.aspx?source=world-development-indicators#)

2. [FAO - Sustainable Development Goals Data Portal](https://www.fao.org/sustainable-development-goals-data-portal/data/)

3. [Kaggle - Climate Sentiment in Twitter](https://www.kaggle.com/datasets/joseguzman/climate-sentiment-in-twitter)

4. [Kaggle - Social Media Sentiments Analysis Dataset](https://www.kaggle.com/datasets/kashishparmar02/social-media-sentiments-analysis-dataset)

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🌟 **Thank you for exploring our project!** 🌟