A Predictive Analysis on CO ₂ Emissions in Automobiles using Machine Learning Techniques

1.80 metric tonnes of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> are emitted by citizens in India, which is highly detrimental to all living beings. Climate change and glacier melting are the results of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. Sea levels are rising as a result of global warming, which is mostly caused by CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . 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 CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> 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, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions are anticipated. Python Scikit-Learn and the Matplotlib package are used to analyze CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> 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).