Car Price Prediction using Machine Learning

Predicting car prices involves determining a vehicle's market worth based on various attributes like brand, model, year of manufacture, mileage, and overall state. This prediction holds significant value for the auto industry, aiding both prospective buyers and sellers in understanding pricing and making educated decisions regarding vehicle transactions. Machine learning techniques, trained on historical datasets sourced from online automotive marketplaces, dealerships, and auction platforms, are adept at forecasting the prices of both new and pre-owned cars, using their specifications. Noteworthy machine learning models applied include Linear Regression, AdaBoostRegressor, Lasso Regression, and Ridge Regression. Integrating more diverse data points, such as customer reviews, prevailing market dynamics, and regional factors, can further refine the accuracy of these prediction models. In recent trials with the provided dataset, the Lasso Regression and Ridge Regression models stood out, delivering an impressive accuracy rate nearing 90%.