A Predictive Analysis on CO2 Emissions in Automobiles using Machine Learning Techniques

Abstract

1.80 metric tonnes of CO2 are emitted by citizens in India, which is highly detrimental to all living beings. Climate change and glacier melting are the results of CO2 emissions. Sea levels are rising as a result of global warming, which is mostly caused by CO2. In the past, the prediction has been accomplished using statistical approaches including the t-test, ANOVA test, ARIMA, and SARIMAX. The Random Forest, Decision Tree, and Regression Models are increasingly used to forecast CO2 emissions. When several vehicle feature inputs are used, multivariate polynomial regression and multiple linear regression may reliably forecast the emissions. For inputs with a single feature, single linear regression is used for the prediction. Based on factors including engine size, fuel type, cylinder count, vehicle class, and model, CO2 emissions are anticipated. Python Scikit-Learn and the Matplotlib package are used to analyze CO2 emissions. The efficiency of the implemented models is assessed by using performance metrics. The accuracy of each model is predicted by using the Regression Score (R2-Score), MAE (Mean Absolute Error), and MSE (Mean Squared Error).