Optimising Fuel Efficiency of Vehicle Using Machine Learning Algorithm

Because of the negative effects fuel consumption has on the environment and the economy, car owners and operators are quite concerned about it. By anticipating fuel usage and understanding the variables that influence it, machine learning (ML) algorithms can be used to maximize fuel economy. This paper examines the optimization of fuel efficiency through the application of machine learning methods such as Random-Forest, Decision-trees and linear regression. An algorithm to manage the efficiency of fuel used by a vehicle by using multiple machine learning model such as Linear Regression, Decision-Tree, Random-Forest, XG-Boost for predicting the fuel consumption of the vehicles based on the given set of input values and predicting the output value which is the fuel consumption in Litre/100 km. For example, this paper used linear regression model, random forest regression model, decision tree regression model, and xg-boost model for predicting the fuel consumption of the Light Motor Vehicles (LMV) with an accuracy of 90.5%, 99.97%, 99.96%, and 99.96% respectively. All things considered, ML algorithms might drastically increase fuel economy. To create and test different machine learning models for optimizing fuel efficiency in various car types and operating environments, further study is nonetheless required.