IEEE BASE PAPER TITLE:
CO2 Emission Rating by Vehicles Using Data Science
OUR PROPOSED PROJECT TITLE:
A Machine Learning based Approach for Rating and Prediction of CO2 Vehicle Emissions using Data Science
IEEE BASE PAPER ABSTRACT:
The usage of private transportation is a significant contributor to the exacerbation of global warming. When a gallon of gasoline is burned in a car’s engine, it produces approximately 24 pounds of greenhouse gases, which contribute to about 20% of total emissions. Most of these emissions, over 19 pounds, are released directly from the car’s tailpipe as heat-trapping pollutants. However, the number of emissions produced during the fuel’s extraction, manufacture, and delivery processes is relatively small in comparison. On average, gasoline-powered vehicles that are commonly used on roads around the world have a fuel efficiency of 22 miles per gallon and travel 11,500 miles per year. For every gallon of fuel used, these vehicles produce about 8,887 grams of carbon dioxide. In 1998, the auto industry made a voluntary pledge to cut emissions from new cars by 25 percent by 2008. At that time, new cars’ CO2 emissions on the road were roughly 203 gram per kilometer. They are currently hovering at 170 gram per kilometer and won’t likely drop to 140g/km until around 2020. The amount of carbon dioxide emitted by a vehicle can vary depending on factors such as the type of gasoline used, the vehicle’s fuel efficiency, and the distance it travels in a year. The projected accuracy decreases as the number of controlled and uncontrolled effect variables that affect the properties of CO2 increases. Nevertheless, by taking into account the controllable effect factors and their interactions, a few experimental designs have been proposed. The Road and Transport Authority will seize that specific car if the model we developed to anticipate gas emission from cars exceeds the threshold. The model uses the properties of the car to specify if the car has exceeded the threshold value of CO2. One excellent method for forecasting the CO2 emission rating is supervised machine learning.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Classifier
OUR PROPOSED ABSTRACT:
The project, “CO2 Emission Rating by Vehicles Using Data Science,” is a data-driven initiative aimed at assessing and rating the carbon dioxide (CO2) emissions of new light-duty vehicles available for retail sale in Canada in 2022. Leveraging the power of Python programming and employing sophisticated machine learning models, namely the Random Forest Classifier and the Decision Tree Classifier, this project offers a comprehensive analysis of vehicle emissions. The dataset utilized for this project contains crucial information, including fuel consumption ratings, CO2 emissions in grams per kilometer, CO2 ratings on a scale from 1 (worst) to 10 (best), and smog ratings on a scale from 1 (worst) to 10 (best). These data elements provide a holistic perspective on the environmental performance of various vehicle models, allowing consumers and policymakers to make informed choices. The Random Forest Classifier, a powerful ensemble learning algorithm, and the Decision Tree Classifier were employed to build predictive models. These models achieved remarkable accuracy scores, with the Random Forest Classifier achieving a 100% accuracy on the training dataset and an impressive 99% accuracy on the test dataset. Similarly, the Decision Tree Classifier exhibited outstanding performance with a 100% training accuracy and a 98% test accuracy. By combining these advanced algorithms and a rich dataset, this project contributes to sustainable transportation solutions and empowers consumers to make environmentally conscious decisions when purchasing vehicles. The CO2 Emission Rating system developed here serves as a valuable tool for evaluating the environmental impact of different vehicle models, helping reduce carbon emissions and mitigate climate change. In summary, “CO2 Emission Rating by Vehicles Using Data Science” is a pioneering project that demonstrates the potential of data science and machine learning to address critical environmental challenges. It underscores the importance of transparency and informed decision-making in the automotive industry, ultimately promoting a greener and more sustainable future.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework :
Anitha Julian; S Karthick; T Chandrakishore; A Chandramohan, “CO2 Emission Rating by Vehicles Using Data Science”, 2023 2nd International Conference for Innovation in Technology (INOCON), IEEE Conference, 2023.