Car Price Prediction using Machine Learning

Car price prediction is an essential task in the automotive industry, benefiting manufacturers, dealers, and customers alike. The objective of this research is to predict the price of a car based on various parameters using Machine Learning (ML) techniques. In this study, we employ regression models such as Linear Regression, Decision Tree, and Random Forest to analyze and predict car prices. The dataset used contains multiple features, including brand, year, mileage, fuel type, and transmission type. The results indicate that the Random Forest model performs better than other models in terms of accuracy. The proposed model provides a reliable approach for estimating car prices, thereby aiding buyers and sellers in making informed decisions. Key Words: Car Price Prediction, Machine Learning, Regression Models, Random Forest, Linear Regression.