Paper Title
Sustainable Mobility Tracker

Abstract
This paper presents a web application developed using the Flask framework for predicting and comparing the fuel consumption and CO2 emissions of various vehicle models. The application leverages machine learning models, including linear regression, ridge regression, lasso regression, and elastic net regression, to estimate fuel consumption and CO2 emissions based on user-provided input features. The models are trained and loaded into the application, allowing users to select a vehicle make and input relevant features for prediction. The system identifies the best-performing model for each prediction, highlighting the closest prediction and its associated error percentage. Additionally, the application offers a comparison feature that enables users to compare specifications of different vehicle models within the dataset. Users can select two vehicle models, and the system retrieves and displays their specifications, facilitating informed decision-making for consumers and researchers interested in understanding the environmental impact of vehicle choices. The web application provides an intuitive interface for exploring fuel consumption and emissions data, making it a valuable tool for both consumers and researchers in the automotive industry. Keywords - Flask, Machine Learning, Regression Models, Fuel Consumption, CO2 Emissions, Vehicle Models, Web Application, Comparison, Specification Retrieval

Author - Samanyu B Rao, Smit Vichare, S. Kanmani

Published : Volume-10,Issue-11  ( Nov, 2023 )

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