Project Abstract

Car shopping is still a difficult task for many people in the digital era since there are so many different models, amenities, and price ranges to choose from. My study explores this problem by concentrating on how artificial intelligence (AI) might improve accessibility and customization of the car-buying experience. This dissertation develops a way to systematically integrate labour market fluctuations, supply chain dynamics, and inflation rates into predictive pricing models by developing a sophisticated machine learning model that evaluates car values. With a focus on advanced predictive analytics and econometric modelling, the study makes use of reliable online scraping tools to gather current financial data and market trends. Large datasets are used in this method, and it also adjusts to the ever-changing economic environment, which has a direct impact on car costs.The initial findings are promising, indicating that the inclusion of up-to-date economic information can significantly enhance the accuracy of car pricing estimates.. These observations show how different market factors, such rising wages or shortages of certain parts, have a direct impact on car values. The study has resulted in the development of a cutting-edge artificial intelligence application that provides prospective customers with a clearer and tailored shopping experience by displaying options that correspond with their individual financial circumstances and preferences. To sum up, this study contributes to our comprehension of the car industry by showcasing the efficient use of economic data in artificial intelligence models, thereby enabling consumers to make better educated choices. This adds a valuable new tool to the consumer’s toolbox for making decisions, which could revolutionise the way we buy cars in this increasingly digital age.

Keywords: Artifical Intelligence, Machine Learning, Web Scraping

 Conference Details

Session: Poster Session B at Poster Stand 24

Location: Sir Stanley Clarke Auditorium at Wednesday 8th 09:00 – 12:30

Markers: Matt Roach, Giedre Sabaliauskaite (Siraj)

Course: BSc Software Engineering, 3rd Year

Future Plans: I’m looking for work