Driven by Data: Interactive Visual Analytics of Vehicle Performance and Pricing

Joanna Loja

Co-Presenters: Hellen Rivera, Asturias Roncal

College: Hennings College of Science Mathematics and Technology

Major: MS.COMPUTER/SCIENCE

Faculty Research Mentor: Kwak, Daehan  

Abstract:

Car buyers are often presented with extensive technical information such as horsepower, acceleration, engine size, fuel type, and price, which can make the decision-making process overwhelming. To address this challenge, a data-driven interactive website is developed to help buyers compare vehicles based on common features and performance specifications. The platform uses a public 2025 vehicle dataset from Kaggle, which includes a wide range of standardized automobile attributes.The dataset is cleaned, preprocessed, and analyzed interactively to investigate the relationship between pricing and performance variables, including horsepower, top speed, and acceleration. Brand positioning and fuel type differences, particularly those among electric and standard combustion vehicles, are compared using aggregated analysis.A data-driven dynamic website allows visitors to compare performance and pricing visually while filtering cars by brand, fuel type, and seating capacity. Users may assess value efficiency, identify outliers, and gain a deeper understanding of market patterns through interactive data summary functions that offer brief, accessible explanations of automobile characteristics.This study shows how complex technical data may be transformed into user-friendly decision-support tools by combining web development, data analytics, and visualization techniques. The platform highlights broader trends in the automobile industry and encourages consumers to make decisions based on facts. This project demonstrates how data science techniques may be applied practically to create scalable, user-focused analytical tools.