Second-hand car price monitor system - Lai, Scott Yong Luo (2023)
In Malaysia, the used car market frequently lacks transparency and fair pricing, making it difficult for buyers and sellers to make wise decisions. In-depth research on the creation of a machine learning-based pricing prediction model created exclusively for the Malaysian used car market is presented in this paper. The main goal of this project was to develop a reliable and open model that predicts used car pricing based on essential variables, including age, condition, location, and the availability of comparable models on the market. Numerous techniques, such as neural networks and regression models, were employed to accomplish this purpose. The scope of the project also encompassed the identification of challenges and limitations in the current used car market in Malaysia, along with proposed solutions to improve transparency and fairness for all stakeholders. Methodologically, the project involved data collection, preprocessing, feature selection, model training, and evaluation. The results demonstrated that the developed model provided precise and transparent pricing information, empowering buyers, and sellers to make informed decisions regarding their transactions. Subsequently, the project findings were translated into practical implementation with the development of a comprehensive dashboard system. This dashboard serves as a user-friendly interface, enabling real-time access to accurate pricing data. It allows buyers and sellers in the Malaysian used car market to make well-informed decisions efficiently. This project holds significant potential for enhancing transparency and fairness in the Malaysian used car market, while also serving as a valuable reference for similar initiatives in other countries and markets. The fusion of advanced machine learning techniques with an accessible dashboard interface represents a substantial step towards creating a fair and equitable used car market ecosystem.