Carbon Emission Prediction on the World Bank Dataset for Canada
The continuous rise in CO2 emission into the environment is one of the most
crucial issues facing the whole world. Many countries are making crucial
decisions to control their carbon footprints to escape some of their
catastrophic outcomes. There has been a lot of research going on to project the
amount of carbon emissions in the future, which can help us to develop
innovative techniques to deal with it in advance. Machine learning is one of
the most advanced and efficient techniques for predicting the amount of carbon
emissions from current data. This paper provides the methods for predicting
carbon emissions (CO2 emissions) for the next few years. The predictions are
based on data from the past 50 years. The dataset, which is used for making the
prediction, is collected from World Bank datasets. This dataset contains CO2
emissions (metric tons per capita) of all the countries from 1960 to 2018. Our
method consists of using machine learning techniques to take the idea of what
carbon emission measures will look like in the next ten years and project them
onto the dataset taken from the World Bank's data repository. The purpose of
this research is to compare how different machine learning models (Decision
Tree, Linear Regression, Random Forest, and Support Vector Machine) perform on
a similar dataset and measure the difference between their predictions.