A Comprehensive Review of Vehicle Price Prediction Systems

Abstract: The "Vehicle Price Prediction" project is a machine learning-based program that calculates a car's resale value using Streamlit and Python. The method uses a dataset that includes variables such vehicle brand, year of manufacture, kilometers traveled, fuel type, gearbox type, and ownership status. These attributes are preprocessed and transformed into numerical values using Label Encoder to ensure adherence to the learning model. A Random Forest Regressor is trained on the dataset to identify complex correlations between input features and the target variable (vehicle price). The integration of Streamlit, which provides a simple and entertaining graphical interface, allows users to enter their vehicle parameters and receive a real-time pricing estimate through dropdown menus and numeric fields. Data loading, preparation, and model training are the first steps in the system's workflow. Interactive user input is then used for prediction. Before applying transformations, user-provided values are merged with a dummy row from the dataset to ensure encoding consistency. Following processing, the expected resale price—which is shown in lakhs of rupees—is predicted by the trained model. This initiative helps consumers and sellers make data-driven decisions, showcasing the real-world use of artificial intelligence in the car industry. In order to bridge the gap between raw data and actionable insights, it can also act as a basis for more sophisticated systems like integrated car resale platforms, dealership assistance tools, and consumer-focused applications.