Fuel efficiency plays a crucial role in automotive design, environmental sustainability, and performance analysis. This project presents a Machine Learning approach for predicting Miles Per Gallon (MPG) using vehicle features from the well-known Auto MPG dataset available at the UCI Machine Learning Repository.The dataset undergoes pre-processing steps including handling missing values, converting data types, and selecting key numerical attributes. Two predictive models-Linear Regression and Random Forest Regressor-are implemented and evaluated using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The Random Forest model performs significantly better, indicating its strength in capturing nonlinear patterns in vehicle characteristics.The study highlights the potential of Machine Learning to support automobile efficiency analysis and fuel consumption forecasting. Future enhancements may include model tuning, advanced algorithms, real-time prediction systems, and deployment through a web interface.
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Abhishek Sharma is a multifaceted academic, researcher, and engineer currently serving as an Assistant Professor at the Geetanjali Institute of Technical Studies (GITS) in Udaipur. With over a decade of experience in the field, Abhishek Joshi is a dedicated academician and technical expert with over 10 years' experience.
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Paperback. Zustand: new. Paperback. Fuel efficiency plays a crucial role in automotive design, environmental sustainability, and performance analysis. This project presents a Machine Learning approach for predicting Miles Per Gallon (MPG) using vehicle features from the well-known Auto MPG dataset available at the UCI Machine Learning Repository.The dataset undergoes pre-processing steps including handling missing values, converting data types, and selecting key numerical attributes. Two predictive models-Linear Regression and Random Forest Regressor-are implemented and evaluated using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The Random Forest model performs significantly better, indicating its strength in capturing nonlinear patterns in vehicle characteristics.The study highlights the potential of Machine Learning to support automobile efficiency analysis and fuel consumption forecasting. Future enhancements may include model tuning, advanced algorithms, real-time prediction systems, and deployment through a web interface. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786209635007
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Fuel efficiency plays a crucial role in automotive design, environmental sustainability, and performance analysis. This project presents a Machine Learning approach for predicting Miles Per Gallon (MPG) using vehicle features from the well-known Auto MPG dataset available at the UCI Machine Learning Repository.The dataset undergoes pre-processing steps including handling missing values, converting data types, and selecting key numerical attributes. Two predictive models-Linear Regression and Random Forest Regressor-are implemented and evaluated using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R score. The Random Forest model performs significantly better, indicating its strength in capturing nonlinear patterns in vehicle characteristics.The study highlights the potential of Machine Learning to support automobile efficiency analysis and fuel consumption forecasting. Future enhancements may include model tuning, advanced algorithms, real-time prediction systems, and deployment through a web interface.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Bestandsnummer des Verkäufers 9786209635007
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Taschenbuch. Zustand: Neu. Fuel Efficiency (MPG) Prediction Using Machine Learning | Abhishek Sharma (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209635007 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 134920701
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