The goal of this project is to analyse the Ecological footprints of 192 countries to predict future carbon levels in each country. This project implements time series analysis to calculate the ecological forecasts for each country . Time series is a useful data mining technique to understand how the variables within the dataset change over time. The data used for this project was sourced from Kaggle and extensive analysis of this dataset was carried out using Excel, RStudio, SPSS and Tableau. This study highlights which countries has the most crop land, grazing land, forest land, fishing ground, built up land, all measured in global hectares. All these factors can affect how much carbon a country will produce. This study finds a link between variables in land usage and how they affect carbon levels.
Python, R, Tableau, SPSS, Excel
Final year Technology Management student. One year of experience in technical support with Irish Life. Strong communication skills developed over four years of customer service experience working as a cinema host and technical support specialist. Strong web development skills with experience using Android Studio, Microsoft Visual Studio and Notepad++. Currently learning R and Python for final year project. Seeking graduate opportunities in Data Analytics or Technology Management.