Применение метода линейной регрессии для прогнозирования расхода топлива автомобиля

M. A. Zuev; V. M. Shibaev; K. S. Balanev

doi:10.47813/2782-2818-2024-4-2-0298-0305

Authors

M. A. Zuev
V. M. Shibaev
K. S. Balanev

DOI:

https://doi.org/10.47813/2782-2818-2024-4-2-0298-0305

Keywords:

machine learning, linear regression, fuel consumption forecasting, data processing, correlation analysis

Abstract

This article discusses the use of machine learning to predict vehicle fuel consumption. Linear regression is used for this task. The stages of data preparation are described, including data processing and elimination of omissions, as well as the processes of training and testing the model. The data was analyzed and a visualization of the correlation heat map was built. A linear regression model is constructed based on the features that affect the fuel consumption of the car. The effectiveness of the model was evaluated using the RMSE and R² metrics. The main attention is paid to the practical application of the model for predicting fuel consumption based on real data.

Author Biographies

M. A. Zuev

Maxim Zuev, student, Department of "BIT", Institute of Engineering and Economics, direction "Applied Informatics", National Research University «Moscow Power Engineering Institute», Moscow, Russia

V. M. Shibaev

Vladimir Shibaev, student, Department of "BIT", Institute of Engineering and Economics, direction "Applied Informatics", National Research University «Moscow Power Engineering Institute», Moscow, Russia

K. S. Balanev

Kirill Balanev, assistant Professor, National Research University «Moscow Power Engineering Institute», Moscow, Russia

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