The project aimed to develop a machine learning model to predict used car prices in the Virginia area, addressing the crucial role pricing plays in the market. Leveraging data from Cars.com, the largest US online used car website, factors influencing prices were identified and analyzed to build predictive models. By overcoming limitations of traditional pricing methods, the project aimed to create a fairer and more transparent market environment for buyers and sellers. Machine learning facilitated the discovery of hidden patterns for more accurate predictions. Key stages included data collection, preprocessing, exploratory analysis, model development, evaluation, and creating a user-friendly web application. Ultimately, by utilizing machine learning and data analysis, the project sought to enhance transparency and efficiency in car transactions, advancing data science applications in the automotive industry.