vincent-otoo/Greenhouse-Gas-Emission

This project analyzes global CO₂ emissions from 1750 to 2022, focusing on patterns across continents, income groups, and individual countries. While the dataset covers worldwide emissions, the analysis zooms in on Africa, with a special spotlight on West African countries. # 🌍 Global CO₂ Emissions Analysis (1750–2022) This project explores global carbon dioxide (CO₂) emissions trends from **1750 to 2022**, with a special focus on **Africa** — particularly **West African countries**. Using historical emissions data, the analysis aims to uncover patterns across **continents**, **income groups**, and **individual countries**, highlighting regional contributors and long-term trends. --- ## 📊 Key Objectives - Analyze global CO₂ emissions from 1750–2022. - Compare emissions by **continent**, **income group**, and **country**. - Zoom in on **Africa**, with detailed focus on **West Africa**. - Identify the **top 10 CO₂ emitters in West Africa** from 2000–2022. --- ## 🔧 Tools & Technologies - **Python** 🐍 - **Pandas** – data wrangling and manipulation - **Matplotlib** & **Seaborn** – data visualization - **Jupyter Notebook** – exploratory analysis --- ## 📌 Key Insights - Global emissions have increased sharply since the industrial revolution, with most growth concentrated in high-income countries. - Annual CO₂ emission across the various continents in the year 2022 was identified abd visualized, with **North America** recording the highest, followed by **Africa** - From 2000–2022, the top CO₂ emitters in West Africa were identified and visualized, offering insights into regional policy planning. --- ## 📁 Project Structure CO₂ emission-analysis/ ├── data/ # Source datasets ├── visuals/ # Charts and preview images ├── notebooks/ # Main analysis notebook ├── README.md --- ## 📥 Data Source The dataset used is from **Our World in Data** (ourworldindata.org), which compiles CO₂ emissions data from international databases such as the Global Carbon Project. --- ## 🧠 What I Learned - How to preprocess and clean large-scale time series datasets - How to group and filter data for regional and categorical comparisons - How to avoid assumptions and ensure meaningful segm …