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Food production is one of the leading causes of environmental degradation, contributing to climate change, water scarcity, land degradation, and biodiversity loss. The environmental footprint of agricultural activities varies based on food type, farming practices, and geographic location.

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🌍🍽️ Environmental Impact of Food Production Analysis


Environmental Sustainability


🚀 Project Overview

Welcome to an engaging exploration of the Environmental Impacts of Food Production! Our mission is to uncover critical insights into sustainability by analyzing essential metrics such as land use, water consumption, and carbon footprints using robust analytical methods. By applying the CRISP-DM framework, we provide clear, actionable insights aimed at fostering sustainable practices in food production.

Let's shape a sustainable future, one bite at a time! 🌱


📂 Repository Structure

.
├── 📄 README.md                # Project introduction and guide
├── 📁 data/                    # Raw and processed datasets
├── 📁 scripts/                 # Python scripts for data processing and analysis
├── 📁 visualizations/          # Impactful graphs, charts, and dashboards
└── 📁 presentation/            # Summary presentations and insights

🛠️ Tools & Technologies

  • 🐍 Python (pandas, NumPy, Matplotlib, Seaborn, scikit-learn) — For powerful data analysis and visualization.
  • 📓 Jupyter Notebook — Interactive coding and documentation environment.
  • 💻 VS Code — Integrated Development Environment for scripting and analysis.
  • 🌐 GitHub — Version control and collaborative development.

📊 Dataset Highlights

Our rich dataset covers 43 common food items and evaluates 23 environmental impact variables, including:

  • 🌿 Greenhouse gas emissions per kg at various production stages (land use change, animal feed, farming, processing, transport, packaging, and retail).
  • 💧 Water usage and eutrophication caused by agricultural runoff.

🔄 CRISP-DM Framework

1. 🎯 Business Understanding

  • Clearly define key sustainability challenges in food production.
  • Formulate 7+ analytical questions to guide our exploration.

2. 🔍 Data Understanding

  • Thoroughly explore data attributes, detect missing values, and assess distributions.

3. 🧹 Data Preparation

  • Meticulously clean and preprocess data for accurate analysis.

4. 📈 Modeling & Analysis

  • Conduct comprehensive Exploratory Data Analysis (EDA).
  • Develop insightful visualizations for impactful storytelling.

5. 📋 Evaluation

  • Critically assess results and effectively answer analytical questions.

6. 📢 Deployment & Documentation

  • Clearly document findings and share summarized insights through accessible presentations.
  • Regularly update and refine documentation.

🎯 Final Deliverables

  • 📌 Interactive Visualizations & Dashboards — Clearly communicate critical findings.
  • 📘 Comprehensive Documentation — Well-maintained and insightful README.
  • 💻 Python Scripts — Efficiently organized analysis code.
  • 🎤 Insightful Presentation — Clearly articulated business insights and recommendations.

🤝 Contribution

Your insights matter! 🌟

Feel free to enhance this project:

  • 🚩 Submit an issue
  • 🔀 Create a pull request

Let's collaborate to make a lasting impact!


🙌 Acknowledgments

Special thanks to the Azubi Technical Team for providing this invaluable dataset. Your dedication to sharing essential sustainability data makes meaningful analysis possible.


📩 Connect with Me

Nana Akua Afra Owusu-Addo

📧 Email: naaowusu.addo@gmail.com
🔗 LinkedIn: LinkedIn Profile

nb: Here is a link to my medium article on this project! https://medium.com/@naaowusu.addo/from-plate-to-planet-why-what-you-eat-might-be-the-most-powerful-climate-choice-you-make-e5685844bfd1

Let's drive sustainable change together! 🌱✨


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Food production is one of the leading causes of environmental degradation, contributing to climate change, water scarcity, land degradation, and biodiversity loss. The environmental footprint of agricultural activities varies based on food type, farming practices, and geographic location.

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