"The Environmental Footprint of Food Production"

Introduction
With the increasing life expectancy and the rapid growth of the global population, it is becoming increasingly difficult for humanity to meet its nutritional needs. The inefficient expansion of food production is having a noticeable negative impact on the planet. Deforestation, the rise of intensive livestock farming, and the overuse of fertilizers—factors all linked to food production—are the main causal drivers behind the increase in greenhouse gas emissions. The data we use comes from the United Nations for Food and Agriculture (FAOSTAT), a reliable source that collects global information on food production and the resources required for it. Based on this data, we aim to analyze how food production has evolved and its relationship with rising temperatures. This analysis will help raise public awareness of the issue and encourage a more thoughtful and responsible approach to decision-making.


Description
This project aims to analyze global temperature changes over the years, their impact on food production, and resource use efficiency. Using a data-driven approach, it identifies key patterns, highlights the most affected countries, and proposes recommendations to mitigate the effects of climate change and improve agricultural sustainability.


Project Structure
The project is organized into the following stages:
Data Extraction, Transformation, and Loading (ETL):
-Cleaning and transforming data on temperature, food production, and efficiency.
-Key files: extraction.py.
Exploratory Data Analysis (EDA):
-Visualizing patterns and trends in the data.
-Key files: visualization.py.
Results and Conclusions:
-Identifying countries with the highest and lowest temperature increases.
-Analyzing the relationship between temperature, food production, and efficiency.
-Key file: Pro1_April.ipynb.
Visualizations:
-Charts generated to represent the results.
-Folder: visualizations/.
Dashboards:
-GIFs of dashboards created for data analysis.
-Folder: dashboards/.

QUESTIONS WE AIM TO ANSWER IN THIS PROJECT
-Which countries stand out as the most or least efficient in meeting their food needs?
-What are the most consumed foods globally and by country, and how do they contribute to environmental impact in terms of carbon emissions?
-What is the relationship between global temperature increases and agricultural emission trends?


DATASET
I used the Global Food and Agriculture Statistics dataset. Although the dataset on Kaggle is well-structured, I decided to perform additional preprocessing to ensure the data was consistent and suitable for our analysis. During the extraction process, I verified that the datasets were correctly loaded, ensuring the files were not empty and displaying basic information about the datasets. In the data transformation stage, the process was systematic. I started by removing duplicates, replacing null values, and normalizing column names (removing spaces and converting column names to lowercase). Then, I applied functions to specific columns, etc. Finally, I concluded the process by saving the processed data and ensuring the script was robust and would not halt due to minor issues. Given the wide range of information contained in the original Kaggle dataset, we decided to focus on the most relevant data to address our research questions.

https://github.com/CarlosRomanM/proyecto_abril
https://public.tableau.com/app/profile/carlos.roman4629/viz/DASH_PROJECT/Historia12