Fuel Consumption Prediction This project focuses on building a machine learning model to predict vehicle fuel consumption based on various engine specifications and environmental parameters. The objective is to help improve energy efficiency, support emission reduction goals, and assist in data-driven automotive decisions.

Project Overview Fuel consumption is influenced by multiple factors such as engine size, number of cylinders, fuel type, and CO2 emissions. In this project, a supervised machine learning approach is used to analyze historical data and predict fuel consumption accurately.

Technologies Used Python Pandas, NumPy for data processing Matplotlib, Seaborn for visualization Scikit-learn for machine learning models Jupyter Notebook for development

Dataset The dataset contains vehicle attributes including: Engine Size Number of Cylinders Transmission Type Fuel Type Vehicle Class CO2 Emissions Fuel Consumption (City, Highway, Combined)

Machine Learning Models The following models were trained and evaluated: Linear Regression Decision Tree Regressor Random Forest Regressor

Performance was measured using metrics such as: Mean Absolute Error (MAE) Mean Squared Error (MSE) R-squared (R²) Score