Used Car Price Prediction Using Random Forest Regression

The rapid expansion of the automobile industry and the increasing demand for pre-owned vehicles have made accurate used car price estimation an important challenge for buyers, sellers, and dealerships. Determining the fair market value of a used vehicle depends on multiple factors including brand, model, year of manufacture, fuel type, mileage, engine capacity, transmission type, and vehicle condition. Traditional price estimation methods rely heavily on manual evaluation, expert opinions, or simple statistical approaches, which often lead to inconsistent results due to human bias and inability to capture complex relationships between variables. With the growth of machine learning techniques, data-driven models have emerged as reliable solutions for predictive analytics in various industries, including the automotive sector. This research proposes a used car price prediction system using the Random Forest Regressor algorithm, an ensemble learning method that improves prediction accuracy by combining multiple decision trees. The proposed system involves data collection, preprocessing, feature engineering, model training, and evaluation using regression performance metrics. Data preprocessing includes handling missing values, encoding categorical attributes, normalization, and feature selection to improve model performance. The Random Forest model learns patterns from historical car sales data and predicts vehicle prices based on user input attributes. The model performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared score to ensure reliability. Experimental results demonstrate that the Random Forest Regressor provides high prediction accuracy and handles nonlinear relationships effectively. The developed system can support buyers, sellers, and automobile dealers in making informed pricing decisions, improving transparency and efficiency in the used car marketplace