Abstract: Predicting used car prices is a complex task that involves considering several key factors, such as the year, make, model, mileage, condition, and market trends. Accurate prediction models are essential for dealerships, sellers, and consumers to make informed decisions. This study aims to explore how artificial intelligence (AI) and neural networks can be utilized to forecast the prices of secondhand cars. Our goal is to develop a practical model for used car valuation using machine learning techniques. Our methodology includes data collection, preprocessing, feature selection, and training using advanced neural network architectures. The model's accuracy will be evaluated using metrics such as mean absolute error (MAE) and root mean square error (RMSE). Ultimately, the aim is to showcase the capabilities of AI in this domain.

Keywords: Artificial Intelligence, Neural Networks, Machine Learning, Old Car Price Forecasting Predictive Modeling, Data Preprocessing Feature Engineering, Mean Absolute Error, Automotive Market.

| DOI: 10.17148/IARJSET.2024.11767