🏭 A project for analyzing global CO₂ emissions using R, leveraging OWID data to create 12 visualizations that explore historical trends, economic relationships, and top emitters. Includes a linear regression of per-capita emissions on GDP per capita and supports reproducible workflows via Makefile, RMarkdown, and Docker.

A Python web scraper using Selenium to extract global energy data from the IEA website. Collects data such as energy mix, electricity mix, CO2 emissions, and yearly statistics for countries. Saves results in a CSV file for analysis. Ideal for researchers and data enthusiasts interested in energy trends.

Carbon Dioxide is a major component of Green House Gas (GHG) emissions, accounting for 81% of the total emissions. Released naturally (respiration, ocean drive, and decomposition) or as a consequence of human activity (cement production, burning of fossil fuels such as coal, oil and natural gas, etc.), carbon dioxide has played a significant role in Global Warming. It is a phenomenon whose existence can be found in the melting of polar ice caps or progressive rise in the annual global temperatures. India, an emerging economy is the fourth largest producer of CO2 emissions, behind China, USA and the European Union. A nation, with the drive to become a leading superpower in the coming years, India’s booming economy and development poses a serious challenge to the levels of carbon dioxide emissions. Overtaking Russia, to become the third largest producer of electricity, India still relies on coal as the biggest source of electricity, whose burning yields CO2 into the atmosphere. Awaken by the adversities of CO2 emissions, India has signed the Paris Agreement and pledged to reduce the CO2 levels to 30-35% of the level in the year 2005. This agreement is set to be starting in 2020. To have a better understanding of what the challenge is going to be in the year 2020, prior to the implementation of the Paris agreement, this paper aims at forecasting the levels of CO2 emissions and its constituents (solid, liquid and gaseous fuels) in India. The technique used in the forecasting is Exponential Smoothing. Starting from 1960, first two years data has been used for initialization and the value of model parameters (alpha and beta) has been optimized with data from 1962-2017 using Solver. The forecast is estimated for the years 2018-2020. Keywords: Predictive Analytics, CO2 emissions, Time Series Analysis, R, Double Exponential Smoothing, Paris Agreement

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Analysis of global CO2 emissions data using Python as part of the CareerFoundry Data Analytics program.

Developed an interactive Tableau dashboard to analyze global CO₂ emissions from 1960–2023, showcasing top emitters, regional patterns, and long-term trends through advanced visualizations and filters.

This project uses a Python script to analyze and visualize global CO₂ emissions data from the Our World in Data dataset. The script generates three key interactive plots using the Pandas and Plotly libraries to explore historical emissions trends.

Analysis of CO2 emissions Global using a ML

This repository contains global CO2 emissions analysis using Tableau.

Repository for global climate change and CO2 emissions analysis project.

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Regression analysis exploring cause of increasing global temperature change through independent variables related to CO2-emissions

Analysis of factors that contribute to global and national CO2 emissions, using data from the World Bank’s World Development Indicators.

This project showcases models and analysis of global CO2 emissions data from 1981-2020. I developed regression, clustering and classification models with this data set, as well as some interactive visualizations.

Global CO2 Emissions Exploratory Data Analysis

A data analysis and visualization project exploring global CO₂ emission trends, per-capita differences, and economic drivers from 1960 to 2022 using Python, pandas, and matplotlib.

nteractive data visualization dashboard analyzing global CO₂ emissions from 1970 to 2024. Features multiple coordinated charts including temporal trends, geographical distribution by continent and country, sectoral breakdown, and correlations with life expectancy.

A look at the CO2 emission trends from 1921-2021 across countries and continents.

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Statistical analysis of global CO2 and greenhouse gas emissions using economic and energy indicators. Includes data cleaning, exploratory data analysis, regression modeling, PCA, and clustering.

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Exploring global fossil fuel CO₂ emissions from 1750 to 2021

A comprehensive analysis of global CO2 emissions (2019–2023) with preprocessing, trend insights, and dynamic Power BI visualizations. Includes country-level, sectoral, and global contribution breakdowns for actionable insights.

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Exploratory data analysis and visualization of global CO₂ emissions using Python (pandas, matplotlib)

Data analysis project on global CO₂ emissions: cleaning, exploring, and visualizing emission trends by country and sector with insights and policy suggestions.

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