Car Price Prediction

Car price prediction has become an essential application of machine learning in the automotive industry, particularly for used car markets. Accurately estimating the price of a used vehicle based on its features can benefit both buyers and sellers, enabling fair trade and informed decision-making. This project proposes a predictive modeling system using Python-based machine learning algorithms combined with an intuitive user interface built with Streamlit. The proposed system utilizes structured datasets consisting of various car features such as brand, model, year of manufacture, fuel type, mileage, and transmission type. These features are preprocessed and fed into machine learning algorithms including Linear Regression, Decision Tree Regression, Random Forest, and Gradient Boosting for training and evaluation. The selected model is deployed using Streamlit, allowing real-time interaction with the end-user. The system outputs predicted car prices based on user-provided inputs, making it a practical tool for real-world use. Extensive experimentation with different algorithms and evaluation metrics like Mean Absolute Error (MAE), Root Mean SquaredError (RMSE), and R² score confirms the effectiveness of the proposed solution. The outcome is a responsive, user-friendly, and efficient price prediction platform capable of handling real-time queries with high accuracy. This paper outlines the methodology, challenges, and performance of various algorithms and highlights the usability of Streamlit for deploying machine learning models for commercial use cases.