Fuel Consumption Analysis using Machine Learning

Fuel consumption is a vital factor in assessing vehicle performance, environmental sustainability, and economic efficiency. As the demand for fuel-efficient vehicles rises alongside growing concerns about pollution and carbon emissions, the need for precise analysis and prediction of fuel consumption has become increasingly important. Conventional methods frequently struggle to address the intricate, nonlinear relationships among various vehicle characteristics and real-world driving scenarios. This project introduces a Machine Learning-based methodology to analyze and forecast vehicle fuel consumption utilizing historical and technical data. The system gathers numerous input features, including engine size, cylinder count, vehicle weight, fuel type, transmission type, and additional parameters. These features undergo preprocessing before being input into various machine learning algorithms such as Linear Regression, Random Forest, and Support Vector Machine to develop a predictive model. By assessing the performance of each model through metrics like Mean Squared Error and R² Score, the most accurate and dependable algorithm is chosen for final implementation. This predictive model empowers vehicle users, manufacturers, and environmental agencies to make informed, data-driven decisions regarding fuel efficiency. The findings of this project illustrate how machine learning can improve the precision of fuel consumption predictions, aiding in the reduction of fuel expenses, enhancement of vehicle design, and promotion of environmentally friendly transportation systems.