Car price prediction using machine learning

This paper presents AutoSense AI, a machine learning-based system for predicting used carprices. Accurate pricing helps buyers, sellers, and dealers make informed decisions in thedynamic automobile market. The system leverages a dataset of over one million records,including features like mileage, engine power, vehicle age, brand etc. Using GradientBoosting, the model achieves approximately 96% accuracy, outperforming traditionalmethods such as Lasso Regression and Random Forest. The methodology includes datapreprocessing, feature engineering, model training, and evaluation using metrics like MAE,RMSE, and R². AutoSense AI also emphasizes transparency through feature importanceanalysis, helping users understand the factors influencing price predictions. Futureenhancements may include integration with real-time market data, deployment via cloudplatforms, and implementation of explainable AI techniques. The proposed systemdemonstrates the potential of AI to bring transparency, efficiency, and reliability to car priceprediction.Keywords: AutoSense AI, Car Price Prediction, Machine Learning, Gradient Boosting, FeatureEngineering, Predictive Analytics