International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Correct car price prediction is significant in automotive manufacturing as it offers important benefits to the producers, dealers, customer by supporting honest pricing, inventory management, and informed management. This research develops a machine learning system that predicts the prices of cars based on an alteration of attributes, like name, price, model, year, km driven, and type of fuel. The work used a large dataset; the dataset was filtered so that missing values, data inconsistencies, and outliers in the data were reduced. like Linear Regression, Decision Trees, and Random Forests are working to make predictive models. The presentation of these models is evaluated using metrics like R-squared R² for accuracy and reliability. The outputs display the ability of Machine Learning techniques to deliver more accuracy in car price predictions,s howing their practical stability in the automotive domain. By dealing with issues like data quality, feature selection, and model interpretability, this study offers a solid basis for developing similar predictive systems. Besides, the study suggests that improvement, including incorporating real-time market data, can be considered to increase the accuracy of the prediction. This study has made it clear that Machine Learning plays a very complex role in changing the pricing strategies and supporting the stakeholders in driving an active automotive market.

Keywords: Car price prediction, machine learning, Automotive Manufacturing, predictive modeling, Linear Regression, data preprocessing, feature selection, R-squared, pricing strategies, integration, model Real-time Market Data, inventory Management

Downloads: | DOI: 10.17148/IJARCCE.2025.141036