Food production is a major driver of environmental challenges, including climate change, deforestation, water scarcity, and biodiversity loss. This project aims to analyze the ecological footprint of various food products using data science techniques.
The analysis covers:
- Carbon emissions across different food categories.
- Land and water usage per food item.
- Eutrophication impacts (nutrient pollution).
- Sustainability insights for policymakers, producers, and consumers.
The dataset includes 43 common food products and their environmental impact across 23 metrics, such as:
- Greenhouse gas emissions (kg CO₂-equivalents per kg of product).
- Water usage (liters per kg, per 100g protein, per 1000 kcal).
- Land use (m² per kg, per 100g protein, per 1000 kcal).
- Stages of food production (farm, transport, packaging, retail).
- Business Understanding
- Assess the environmental impact of food production.
- Identify sustainable alternatives to high-impact foods.
- Data Understanding
- Explore the structure of the dataset, identify missing values, and clean data.
- Data Preparation
- Fix inconsistencies, handle missing values, and normalize data for analysis.
- Modeling & Analysis
- Compute emissions rankings.
- Identify relationships between land use, water use, and carbon footprint.
- Visualize trends and insights.
- Evaluation & Insights
- Provide sustainability recommendations.
- Discuss trade-offs between food choices.
- Deployment & Documentation
- Store findings, code, and visualizations in this GitHub repository.
Libraries Used:
- pandas: Data cleaning and manipulation.
- seaborn & matplotlib: Static visualizations.
- plotly: Interactive visualizations.
- Used for writing and running code interactively.
- Version control and collaboration.
- Quick data exploration.
- Formatting the dataset into a clear, readable format.
- Dashboard for advanced visualizations.