VALUDRIVE - VEHICLE PRICE PREDICTION AND RECOMMENDATION SYSTEM USING MACHINE LEARNING

Abstract: Used-car resale prices are veering according to vehicle attributes such as year, kilometres driven, fuel type, transmission, engine capacity and ownership history. This paper introduces ValuDrive, a machine-learning web application that assessments used-car selling prices and recommends comparable vehicles in one interface, to a important degree, in many respects. Linear Regression, Decision Tree Regressor and Random Forest Regressor were inspected employing an 80:20 training, testing split and the metrics Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R². This model is using the Car details v3.csv dataset and applies data cleaning, feature preparation, numerical imputation, categorical imputation and One-Hot Encoding, in real-world scenarios. Random Forest hit the strongest findings among the found models, with an MAE of ₹80,195, an RMSE of ₹180,544 and an R² of 0.851. The application blends the picked prediction pipeline with a content-based recommendation engine that's combining weighted credit matching and cosine similarity. These results reflect the available dataset and test split. The anticipated value is an estimate rather than a live-market quote or professional valuation, at least in practice.