https://github.com/lu-sketch/eda-global-meat-consumption

#data#analytics #datascience #trade #plotly #python #pandas
https://github.com/lu-sketch/eda-global-meat-consumption

Last synced: 5 days ago
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#data#analytics #datascience #trade #plotly #python #pandas

• Host: GitHub
• URL: https://github.com/lu-sketch/eda-global-meat-consumption
• Owner: lu-sketch
• License: gpl-3.0
• Created: 2024-07-22T05:47:23.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-08-24T17:58:27.000Z (5 months ago)
• Last Synced: 2024-11-29T18:14:58.886Z (2 months ago)
• Language: Jupyter Notebook
• Size: 5.79 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 🌍📊 Economic Data Analysis: Global Meat Consumption 📈🍖

![Project Overview Image](https://drive.google.com/uc?export=view&id=1vudsuVfy0ULFRcdumSgAvseSNYn3YHs7)

Welcome to this project focused on exploring the relationship between global meat consumption and economic indicators like GDP growth. 🥩✨

## 🔍 Project Overview

In this analysis, we examine:

- **Meat Types:** Consumption trends for **sheep**, **beef**, **poultry**, and **pig** across various regions and countries.

- **Consumption Over Time:** How meat consumption patterns have evolved over the years.

- **Top Consumers:** Comparing meat consumption habits of leading countries.

- **Economic Influence:** Understanding the correlation between GDP growth and meat consumption.

## 📈 Key Insights

Our project provides a detailed analysis of:

- Changes in meat consumption over time.

- Consumption habits among top countries.

- The impact of GDP growth on meat consumption and the influence of economic factors.

🚀 This repository offers valuable insights into global economic trends and consumption behavior.

---

## 🛠️ How to Run the Project

### Option 1: Run Directly in Google Colab

1. Click the link below to open the notebook directly in Google Colab:

 

2. Once opened, click "Run All" to execute the notebook. You might need to allow access to Google Drive or upload data if prompted.

### Option 2: Clone the Repository Locally

1. Clone this repository using the command:

    ```bash

    git clone https://github.com/yourusername/repo-name.git

    ```

2. Install the required packages:

    ```bash

    pip install -r requirements.txt

    ```

3. Run the notebook locally using Jupyter or VS Code.

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#data #analytics #datascience #trade #plotly #python #pandas